CN116823059A - Index evaluation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure relates to the field of computer data processing technologies, and in particular, to an index evaluation method, an index evaluation device, an electronic apparatus, and a storage medium. The method comprises the following steps: acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on feature vectors corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target; screening target evaluation indexes according to the numerical values of weight elements in the first comprehensive weight vector; determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix; acquiring an evaluation matrix corresponding to the target evaluation index, and determining a third weight vector corresponding to the evaluation matrix; fitting is carried out based on the second weight vector set and the third weight vector, and a target weight vector is obtained. The method provides a reasonable, accurate and objective index selection scheme.
Description
Technical Field
The present disclosure relates to the field of computer data processing technologies, and in particular, to an index evaluation method, an index evaluation device, an electronic apparatus, and a storage medium.
Background
In the field of communication, along with the rapid development and iteration of technologies such as networks, clouds, artificial intelligence and the like, the development of cloud network integration is updated and iterated continuously. In particular, for telecom operators, the development level of cloud network integration reflects the digitization and intellectualization degree of the infrastructure, and the construction level of the cloud network integration is closely related to the formulation of the development of operator strategy. In order to understand the development and construction level of cloud network fusion capability, a comprehensive and effective evaluation system needs to be constructed for the development and construction of cloud network fusion. Therefore, how to select key indexes and how to effectively balance the accuracy of index evaluation is a great difficulty in the cloud network fusion evaluation system.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure aims to provide an index evaluation method, an index evaluation method device, an electronic apparatus, and a storage medium; provides a reasonable, accurate and objective index selection scheme.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided an index evaluation method, the method comprising:
acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on feature vectors corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target;
screening target evaluation indexes according to the numerical values of weight elements in the first comprehensive weight vector;
determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix;
acquiring an evaluation matrix corresponding to the target evaluation index, and determining a third weight vector corresponding to the evaluation matrix;
fitting is carried out based on the second weight vector set and the third weight vector, and a target weight vector is obtained.
In an exemplary embodiment of the present disclosure, the performing consistency verification on the judgment matrix includes:
calculating a maximum characteristic value corresponding to the evaluation matrix;
performing consistency verification calculation based on the maximum characteristic value, and when the calculation result accords with a consistency verification rule, configuring a corresponding judgment matrix to pass the consistency verification and writing the judgment matrix into an available database; or alternatively
And when the calculation result does not accord with the consistency verification rule, configuring the corresponding judgment matrix to fail consistency verification, and writing the judgment matrix into a database to be determined.
In an exemplary embodiment of the present disclosure, the method further comprises:
based on the judgment matrix in the available database, performing geometric average operation on each element in the matrix to obtain corresponding matrix mean value data;
sequentially replacing each element in the evaluation matrix in the undetermined database by using matrix mean value data to obtain an element replacement matrix, and carrying out consistency verification on the element replacement matrix;
and when passing the consistency verification, writing the element replacement matrix into the available database, and updating matrix mean value data corresponding to the available database.
In an exemplary embodiment of the present disclosure, the obtaining an evaluation matrix corresponding to the target evaluation index includes:
creating an index evaluation task based on the target evaluation index;
executing the index evaluation task, and sending the target evaluation index to a second evaluation object to obtain index evaluation parameters fed back by each second evaluation object;
the evaluation matrix is created based on the index evaluation parameters.
In an exemplary embodiment of the present disclosure, the method further comprises:
collecting object information of a second evaluation object;
and configuring index analysis coefficients according to the object information, and performing fitting operation by combining the index analysis coefficients with the second weight vector set and the third weight vector to obtain a target weight vector.
In an exemplary embodiment of the disclosure, the fitting operation performed by using the index analysis coefficient in combination with the second weight vector set and the third weight vector includes:
and calculating the target weight vector by using a weighted geometric average algorithm to combine the object information with the second weight vector set and the third weight vector.
In an exemplary embodiment of the disclosure, the fitting operation performed by using the index analysis coefficient in combination with the second weight vector set and the third weight vector includes:
creating an objective function based on a constrained nonlinear programming problem according to the objective weight vector;
and solving the objective function by combining the index analysis coefficient with the second weight vector set and the third weight vector to obtain the objective weight vector.
In an exemplary embodiment of the present disclosure, the method further comprises:
constructing a judgment matrix template based on index information of a preset index library, and creating a corresponding index judgment processing task;
and executing the index evaluation processing task, and pushing the evaluation matrix template to each first evaluation object so as to acquire the evaluation matrix fed back by each first evaluation object.
According to a second aspect of the present disclosure, there is provided an index evaluation device including:
the first comprehensive weight vector calculation module is used for acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on the feature vector corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target;
the index screening module is used for screening target evaluation indexes according to the numerical values of the weight elements in the first comprehensive weight vector;
the second weight vector calculation module is used for determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix;
the third weight vector calculation module is used for obtaining an evaluation matrix corresponding to the target evaluation index and determining a third weight vector corresponding to the evaluation matrix;
and the target weight vector calculation module is used for fitting based on the second weight vector set and the third weight vector to obtain a target weight vector.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the index evaluation method according to any one of the above embodiments via execution of the executable instructions.
According to a fourth aspect of the present disclosure, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the index evaluation method as described in any one of the above embodiments.
In the index evaluation method provided by the embodiment of the disclosure, first, a first comprehensive weight vector is calculated by using a judgment matrix passing consistency verification, so that a target evaluation index can be screened according to the numerical value of a weight element; calculating a second weight vector set corresponding to the target evaluation index; and calculating a corresponding third weight vector according to the evaluation matrix, and calculating a target weight vector by utilizing the second weight vector set and the third weight vector, so that the assignment of the index weight can be more stable and accurate.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a schematic diagram of an index evaluation method in an exemplary embodiment of the present disclosure;
fig. 2 schematically illustrates a schematic diagram of a flow of an index evaluation method applied to a cloud network fusion scenario in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a schematic diagram of an index evaluation device in an exemplary embodiment of the present disclosure;
FIG. 4 schematically illustrates a schematic diagram of an electronic device in an exemplary embodiment of the present disclosure;
fig. 5 schematically illustrates a schematic diagram of a storage medium in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In the related prior art, cloud network fusion has become a necessary trend of new technology evolution and upgrading such as 5G/6G, cloud computing, MEC, AI, blockchain and the like, and is also a key for solving the problem of cloud network reconstruction so as to meet social diversified business requirements. The cloud network fusion is a deep innovation of information infrastructure brought by deep fusion of communication technology and information technology, and three stages of collaboration, fusion and integration are needed in the development process, so that traditionally relatively independent cloud computing resources and network facilities are fused to form a system of integrated supply, integrated operation and integrated service. The research of cloud network integration has very important significance for promoting the digitization and the intelligent upgrade of industries such as power grid, industry, internet of things, medical treatment and the like. Innovations, upgrades, and related applications of basic technical elements such as networks, clouds, artificial intelligence, security, greenness and the like continuously emerge, so that the development of cloud network integration is also continuously updated and iterated. In particular, for telecom operators, the development level of cloud network integration reflects the digitization and intellectualization degree of the infrastructure, and the construction level of the cloud network integration is closely related to the formulation of the development of operator strategy. In order to understand the development and construction level of cloud network fusion capability, a comprehensive and effective evaluation system needs to be constructed for the development and construction of cloud network fusion. Therefore, how to select key indexes and how to effectively balance the accuracy of index evaluation is a great difficulty in the cloud network fusion evaluation system. The construction of an evaluation system is generally divided into three parts, namely, selecting an evaluation index, determining index weight and comprehensively grading. Methods for selecting evaluation indexes are classified into qualitative analysis and quantitative analysis, but single qualitative analysis, such as an empirical method and an expert opinion method, is too subjective. Single quantitative analysis, such as principal component analysis, requires high correlation of data, and existing data often does not meet the data quality requirements.
In this exemplary embodiment, in order to solve the technical defects existing in the prior art, an index evaluation method is provided first. Referring to fig. 1, specifically, the method may include:
step S11, acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on feature vectors corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target;
step S12, screening target evaluation indexes according to the numerical values of weight elements in the first comprehensive weight vector;
step S13, determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix;
step S14, an evaluation matrix corresponding to the target evaluation index is obtained, and a third weight vector corresponding to the evaluation matrix is determined;
and step S15, fitting is carried out based on the second weight vector set and the third weight vector, and a target weight vector is obtained.
In the index evaluation method provided in this exemplary embodiment, first, a first comprehensive weight vector is calculated using a judgment matrix that passes consistency verification, so that a target evaluation index can be screened according to the numerical value of a weight element; calculating a second weight vector set corresponding to the target evaluation index; calculating a corresponding third weight vector according to the evaluation matrix, and calculating a target weight vector by utilizing the second weight vector set and the third weight vector; on the one hand, the assignment of the index weight can be more stable and accurate. On the other hand, inaccurate weight assignment caused by simple subjective intention or objective numerical deviation is avoided.
Hereinafter, each step of the index evaluation method in the present exemplary embodiment will be described in more detail with reference to the drawings and examples.
In step S11, a judgment matrix is obtained, consistency verification is performed on the judgment matrix, and a first comprehensive weight vector is determined based on feature vectors corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target.
In this exemplary embodiment, the above method may be applied to a terminal device or a server side. Taking a terminal device as an example, a user can create an index evaluation task at the terminal device, configure specific content of the index evaluation task, and distribute the task.
In this example embodiment, the method may further include:
step S21, constructing a judgment matrix template based on index information of a preset index library, and creating a corresponding index judgment processing task;
and S22, executing the index evaluation processing task, and pushing the evaluation matrix template to each first evaluation object so as to acquire the evaluation matrix fed back by each first evaluation object.
Specifically, the above-described index evaluation task may include an index evaluation processing task. Specifically, taking cloud network fusion evaluation as an example, n index information can be selected in an index library according to the evaluation requirement to create a judgment matrix template. Meanwhile, an m-bit cloud network fusion domain expert can be selected as a first evaluation object. And pushing the index evaluation processing task to the terminal equipment corresponding to each expert when the index evaluation processing task is executed, so as to acquire an evaluation matrix fed back by each expert through the terminal equipment. Wherein the evaluation matrix templates are shown in table 1 below.
TABLE 1
| Index 1 | Index 2 | … | Index n | |
| Index 1 | a 11 | a 12 | … | a 1n |
| Index 2 | a 21 | a 22 | … | a 2n |
| … | … | … | … | … |
| Index n | a n1 | a n2 | … | a nn |
Wherein the values of the elements in the matrix represent the importance of the corresponding longitudinal index compared to the transverse index. For example, the number of the cells to be processed,a ratio of importance degrees of the ith index and the jth index for cloud network fusion is represented; n, m.epsilon.0, 9. And 0 to 9 represent the importance degree of the two indexes to the cloud network fusion, and the larger the numerical value is, the larger the importance degree is.
The m-bit expert scores n indexes to be evaluated in the required template to obtain a corresponding n multiplied by n order judgment matrix which is marked as A 1 ，A 2 ，A 3 ......A m . For each acquired judgment matrix A i The corresponding maximum eigenvalue lambda can be calculated i And corresponding feature vector omega i 。
In this example embodiment, in the step S11, the performing consistency verification on the evaluation matrix includes:
step S31, calculating a maximum eigenvalue corresponding to the evaluation matrix;
step S32, carrying out consistency verification calculation based on the maximum characteristic value, and when the calculation result accords with a consistency verification rule, configuring a corresponding judgment matrix to pass the consistency verification and writing the judgment matrix into an available database; or alternatively
And step S33, when the calculation result does not accord with the consistency verification rule, configuring the corresponding judgment matrix to not pass the consistency verification, and writing the judgment matrix into a pending database.
In this example embodiment, the method may further include:
step S34, performing geometric average operation on each element in the matrix based on the judgment matrix in the available database to obtain corresponding matrix average value data;
step S35, sequentially replacing each element in the evaluation matrix in the undetermined database by utilizing matrix mean value data to obtain an element replacement matrix, and carrying out consistency verification on the element replacement matrix;
and step S36, when passing the consistency verification, writing the element replacement matrix into the available database, and updating matrix mean value data corresponding to the available database.
Specifically, the formula can be calculated by the consistency:and carrying out consistency verification on the judgment matrix. If the one-time verification is passed, storing the data into an available database Du; if not, storing the data into a pending database Dt. Each element value a of the matrix to be validated for consistency ij And performing geometric average to obtain matrix average data.
Then, single data correction can be performed on expert index scoring corresponding to the matrix stored in the undetermined database. And starting from the first index score, replacing the first index score with a matrix mean value corresponding value, carrying out consistency verification, if the first index score passes through the first index score, storing the corrected matrix into an available database, if the corrected matrix does not pass through the first index score, restoring the first index score, changing the second index score into a matrix mean value, carrying out consistency verification, and the like, and deleting the data until the last index fails to pass the consistency verification after the last index is replaced.
And carrying out geometric average on the feature vectors corresponding to the matrix passing the consistency verification to obtain a comprehensive weight vector.
In step S12, the target evaluation index is screened according to the values of the weight elements in the first comprehensive weight vector.
In this example embodiment, for the obtained first comprehensive weight vector, the index of k before the weight vector component numerical ranking may be selected according to the requirement, that is, the screened important evaluation index is used as the target evaluation index.
In step S13, a second set of weight vectors corresponding to the target evaluation index is determined based on the evaluation matrix.
In this example embodiment, after the target evaluation indexes are screened, a set of weight vectors corresponding to the selected target evaluation indexes may be determined according to the m evaluation matrices and the N target evaluation indexes currently screened, including:
in step S14, an evaluation matrix corresponding to the target evaluation index is acquired, and a third weight vector corresponding to the evaluation matrix is determined.
In this example embodiment, the obtaining the evaluation matrix corresponding to the target evaluation index includes:
step S41, creating an index evaluation task based on the target evaluation index;
step S42, executing the index evaluation task, and sending the target evaluation index to second evaluation objects to obtain index evaluation parameters fed back by each second evaluation object;
step S43, creating the evaluation matrix based on the index evaluation parameter.
Specifically, after determining the target evaluation indexes, an index evaluation task for evaluating the target evaluation indexes may be created, and k experts may be selected to score N target evaluation indexes. Wherein k-bit expert as the second evaluation object may be different from the expert as the first evaluation object. And executing an index evaluation task, carrying N target evaluation indexes, pushing the task to terminal equipment corresponding to k experts, and acquiring k pieces of feedback scoring data. The expert can score the N evaluation indexes with reference to the reference conditions of actual deployment, operation, etc. Based on the scoring results of k parts for the N indexes, an evaluation matrix of k multiplied by N is created. The evaluation matrix can be acquired and calculated by an entropy weight method to obtain a corresponding third weight vector v= [ v ] 1 ,v 2 ,…v N ]。
In step S15, fitting is performed based on the second weight vector set and the third weight vector, so as to obtain a target weight vector.
In this example embodiment, the method further includes: collecting object information of a second evaluation object; and configuring index analysis coefficients according to the object information, and performing fitting operation by combining the index analysis coefficients with the second weight vector set and the third weight vector to obtain a target weight vector.
Specifically, when the second evaluation object is selected, the working information of k-bit experts can be collected in advance as object information, the working time limit information is extracted, and the expert index analysis coefficient beta corresponding to each expert is configured according to the working time limit information j As weights for scoring corresponding indicators by the expert in the algorithm.
In this example embodiment, the performing a fitting operation by using the index analysis coefficient in combination with the second weight vector set and the third weight vector includes:
and calculating the target weight vector by using a weighted geometric average algorithm to combine the object information with the second weight vector set and the third weight vector.
Specifically, the weighted geometric mean calculation formula may include:
wherein beta is j The expert experience years are used as expert index analysis coefficients.
Finally, a target weight vector is obtained
In this example embodiment, the performing a fitting operation by using the index analysis coefficient in combination with the second weight vector set and the third weight vector includes: creating an objective function based on a constrained nonlinear programming problem according to the objective weight vector; and solving the objective function by combining the index analysis coefficient with the second weight vector set and the third weight vector to obtain the objective weight vector.
Specifically, the target weight vector finally obtained by the weighted least square method is Unlike the weighted geometric averaging algorithm, ω opt2 Is>Are all a variable and need to be less than 1. Thus, a constrained nonlinear programming problem is developed to find the optimal weight vector ω opt2 . The optimal solution of the nonlinear programming problem is omega needed by us opt2 . The goal of the nonlinear programming problem is to minimize the following function:
and the following conditions need to be satisfied:
solving the following optimal solution of the optimization problem, namely the fitted optimal weight vector:
wherein the objective functionIs a convex function; equation constraint function->Is an affine function. The convex optimization theory shows that the optimization problem is a convex optimization problem and has an optimal solution. The optimal solution +.>I.e. the corresponding cloud network fuses each index weight omega opt2 。
For example, taking a cloud network fusion evaluation system as an example, referring to fig. 2, a cloud network fusion index library may be first constructed. The index library can comprise indexes of network capability indexes, operation capability indexes, security indexes and the like. The specific content of the index is not particularly limited in the present disclosure. Constructing a judgment matrix template based on indexes in an index library, creating corresponding index judgment processing tasks aiming at the construction judgment matrix template, pushing the tasks to preselected judgment patents respectively, acquiring judgment matrixes fed back by each expert, and expressing the importance of any two indexes compared with each other on cloud network fusion evaluation by using the judgment matrixes. Consistency verification can be performed on each judgment matrix, and geometric average operation is performed on each element of the judgment matrix passing the consistency verification to obtain matrix average data; if the judgment matrix fails consistency verification, the matrix mean value data is utilized for correction, and single data correction for scoring expert indexes is realized. And carrying out geometric average on the feature vectors corresponding to the matrix passing the consistency verification by utilizing the judgment matrix passing the consistency verification based on the AHP (Analytic Hierarchy Process) method index weighting, so as to obtain a comprehensive weight vector, thereby obtaining the weight vector of a plurality of experts based on subjective experience. And then selecting an index of n before the numerical sorting of the weight vector components according to the requirements, taking the index as a screened important evaluation index, and obtaining a group of weight vectors corresponding to the selected evaluation index. Then, for the screened indexes, a corresponding evaluation task can be created and pushed to k relevant professional specialists, the scores of the specialists on the indexes are obtained, and a k multiplied by n order matrix is obtained by using the scoring result. Then, the matrix can be calculated by utilizing an entropy weight method, and a more reasonable index weight vector is obtained by adopting a weighted geometric average method and a weighted least square method by combining the experience years of cloud network fusion experts. And configuring a weight value for a specific index in the evaluation system based on the finally obtained weight vector, so that specific evaluation score calculation can be performed. For example, the cloud fusion evaluation may be divided into nine dimensional indexes, and four domain scores. The scores of the four fields can comprise a resource facility capability domain score, a cloud network operation capability domain score, a cloud network service capability domain score and a green security capability domain score. The resource facility capability domain score can be a local cloud computing capability index score, a network capability index score and a data fusion index score; the cloud network operational capability domain score may be based on the same scheduling indicator score, an operational intelligence indicator score. The cloud network service capability domain score may be based on an integrated service indicator score, a capability open indicator score. The green security capability domain score may be based on a security trusted indicator score, a green low carbon indicator score.
According to the index evaluation method provided by the embodiment of the disclosure, in the index screening stage, abnormal dimension indexes can be processed through consistency verification; by automatically correcting the anomaly data, the usability of small sample data is increased. In the index weight design stage, based on an AHP and entropy weight method, fitting a plurality of cloud network fusion index weights by taking expert cloud network fusion experience years as the weight of a weighted least square method, providing a stable weight value for cloud network fusion capacity assessment, and avoiding weight deviation caused by abnormal data disturbance; and the inaccuracy of weight assignment caused by simple subjective willingness or objective numerical deviation is avoided. The method provides a brand-new screening and index weight design method for the cloud network fusion indexes, can be used for building a cloud network fusion capacity assessment system, and can clearly measure the development status of the cloud network fusion capacity through four-domain nine-dimensional indexes.
It is noted that the above-described figures are only schematic illustrations of processes involved in a method according to an exemplary embodiment of the application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Further, referring to fig. 3, in this exemplary embodiment, there is further provided an index evaluation device 30, where the device 30 includes: a first comprehensive weight vector calculation module 301, an index screening module 302, a second weight vector calculation module 303, a third weight vector calculation module 304, and a target weight vector calculation module 305; wherein, the liquid crystal display device comprises a liquid crystal display device,
the first comprehensive weight vector calculation module 301 may be configured to obtain a judgment matrix, perform consistency verification on the judgment matrix, and determine a first comprehensive weight vector based on feature vectors corresponding to the judgment matrix that passes the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target.
The index screening module 302 may be configured to screen the target evaluation index according to the values of the weight elements in the first comprehensive weight vector.
The second weight vector calculation module 303 may be configured to determine a second set of weight vectors corresponding to the target evaluation index based on the evaluation matrix.
The third weight vector calculation module 304 may obtain an evaluation matrix corresponding to the target evaluation index, and determine a third weight vector corresponding to the evaluation matrix.
The target weight vector calculation module 305 may be configured to perform fitting based on the second weight vector set and the third weight vector, and specific details of each module in the index evaluation device for obtaining the target weight vector are described in detail in the corresponding index evaluation method, so that details are not repeated herein.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Further, an electronic device 400 capable of implementing the above method is provided in the present exemplary embodiment. The electronic device 400 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 4, components of electronic device 400 may include, but are not limited to: the at least one processing unit 410, the at least one memory unit 420, and a bus 430 connecting the various system components, including the memory unit 420 and the processing unit 410.
Wherein the storage unit stores program code that is executable by the processing unit 410 such that the processing unit 410 performs steps according to various exemplary embodiments of the present application described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 410 may perform the steps as shown in fig. 1.
The storage unit 420 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 430 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The computer system 400 may also communicate with one or more external devices 50 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the computer system 400, and/or any devices (e.g., routers, modems, etc.) that enable the computer system 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Moreover, computer system 400 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 460. As shown, network adapter 460 communicates with other modules of computer system 400 over bus 430. Processing unit 410 is coupled to display unit 440 via bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with computer system 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the application as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
Referring to fig. 5, a program product 500 for implementing the above-described method according to an embodiment of the present application is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (11)
1. An index evaluation method, characterized in that the method comprises:
acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on feature vectors corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target;
screening target evaluation indexes according to the numerical values of weight elements in the first comprehensive weight vector;
determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix;
acquiring an evaluation matrix corresponding to the target evaluation index, and determining a third weight vector corresponding to the evaluation matrix;
fitting is carried out based on the second weight vector set and the third weight vector, and a target weight vector is obtained.
2. The method of claim 1, wherein said performing consistency verification on the judgment matrix comprises:
calculating a maximum characteristic value corresponding to the evaluation matrix;
performing consistency verification calculation based on the maximum characteristic value, and when the calculation result accords with a consistency verification rule, configuring a corresponding judgment matrix to pass the consistency verification and writing the judgment matrix into an available database; or alternatively
And when the calculation result does not accord with the consistency verification rule, configuring the corresponding judgment matrix to fail consistency verification, and writing the judgment matrix into a database to be determined.
3. The method according to claim 2, wherein the method further comprises:
based on the judgment matrix in the available database, performing geometric average operation on each element in the matrix to obtain corresponding matrix mean value data;
sequentially replacing each element in the evaluation matrix in the undetermined database by using matrix mean value data to obtain an element replacement matrix, and carrying out consistency verification on the element replacement matrix;
and when passing the consistency verification, writing the element replacement matrix into the available database, and updating matrix mean value data corresponding to the available database.
4. The method according to claim 1, wherein the obtaining the evaluation matrix corresponding to the target evaluation index includes:
creating an index evaluation task based on the target evaluation index;
executing the index evaluation task, and sending the target evaluation index to a second evaluation object to obtain index evaluation parameters fed back by each second evaluation object;
the evaluation matrix is created based on the index evaluation parameters.
5. The method according to claim 1, wherein the method further comprises:
collecting object information of a second evaluation object;
and configuring index analysis coefficients according to the object information, and performing fitting operation by combining the index analysis coefficients with the second weight vector set and the third weight vector to obtain a target weight vector.
6. The method of claim 5, wherein performing a fitting operation using the index analysis coefficients in combination with the second set of weight vectors and the third weight vector comprises:
and calculating the target weight vector by using a weighted geometric average algorithm to combine the object information with the second weight vector set and the third weight vector.
7. The method of claim 5, wherein performing a fitting operation using the index analysis coefficients in combination with the second set of weight vectors and the third weight vector comprises:
creating an objective function based on a constrained nonlinear programming problem according to the objective weight vector;
and solving the objective function by combining the index analysis coefficient with the second weight vector set and the third weight vector to obtain the objective weight vector.
8. The method according to claim 1, wherein the method further comprises:
constructing a judgment matrix template based on index information of a preset index library, and creating a corresponding index judgment processing task;
and executing the index evaluation processing task, and pushing the evaluation matrix template to each first evaluation object so as to acquire the evaluation matrix fed back by each first evaluation object.
9. An index evaluation device, characterized in that the device comprises:
the first comprehensive weight vector calculation module is used for acquiring a judgment matrix, carrying out consistency verification on the judgment matrix, and determining a first comprehensive weight vector based on the feature vector corresponding to the judgment matrix passing the consistency verification; the evaluation matrix is used for describing the importance degree of any two indexes to be evaluated on the evaluation target;
the index screening module is used for screening target evaluation indexes according to the numerical values of the weight elements in the first comprehensive weight vector;
the second weight vector calculation module is used for determining a second weight vector set corresponding to the target evaluation index based on the evaluation matrix;
the third weight vector calculation module is used for obtaining an evaluation matrix corresponding to the target evaluation index and determining a third weight vector corresponding to the evaluation matrix;
and the target weight vector calculation module is used for fitting based on the second weight vector set and the third weight vector to obtain a target weight vector.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the index evaluation method of any one of claims 1 to 8 via execution of the executable instructions.
11. A storage medium having stored thereon a computer program, which when executed by a processor implements the index evaluation method according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117634942A (en) * | 2023-11-10 | 2024-03-01 | 南京审计大学 | Fusion method, device, equipment and medium of uncertain audit evaluation data |
| CN117707065A (en) * | 2023-12-11 | 2024-03-15 | 上海曼孚机电控制工程有限公司 | Concentration-based intelligent liquid additive blending method, system and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117634942A (en) * | 2023-11-10 | 2024-03-01 | 南京审计大学 | Fusion method, device, equipment and medium of uncertain audit evaluation data |
| CN117707065A (en) * | 2023-12-11 | 2024-03-15 | 上海曼孚机电控制工程有限公司 | Concentration-based intelligent liquid additive blending method, system and medium |
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