CN-122019318-A - Cloud disaster backup service quality evaluation method for hope weighting and fuzzy mathematics

CN122019318ACN 122019318 ACN122019318 ACN 122019318ACN-122019318-A

Abstract

The invention relates to a cloud disaster recovery service quality evaluation method for expected weighting and fuzzy mathematics, which comprises the steps of responding to a cloud disaster recovery service quality evaluation request initiated by a user or detecting a preset disaster recovery service evaluation trigger signal by a service system, acquiring service quality scoring data of the service system based on a preset evaluation index, converting the service quality scoring data into a fuzzy relation matrix by adopting a parabolic membership function, and carrying out hierarchical weighted calculation by combining the preset evaluation index weight and the fuzzy relation matrix to generate a quantized cloud disaster recovery service comprehensive evaluation result. The method comprises the steps of constructing a multi-dimensional hierarchical index system, comprehensively covering the functionality, reliability and user experience of cloud disaster recovery service, objectively determining index weights by using a reverse cloud model, reducing subjective deviation, effectively modeling fuzzy scores of users by using K parabolic membership functions, improving evaluation accuracy, generating traceable comprehensive scores by hierarchical weighted calculation, identifying service bottlenecks and optimizing disaster recovery performance.

Inventors

SU XUAN
GUO SHUAI
LI RUI
CHENG RAN
MA YANPENG
ZHANG XIAOPENG
GUO BIN
ZHOU XINXING
WU ZHENGZHONG
LIU DERAN

Assignees

中国人民解放军61618部队

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (10)

1. A cloud disaster recovery service quality evaluation method for hope weighting and fuzzy mathematics is characterized by comprising the following steps of, Responding to a cloud disaster recovery service quality evaluation request initiated by a user or detecting a preset disaster recovery service evaluation trigger signal by a service system, and acquiring service quality scoring data of the service system based on a preset evaluation index; Converting the service quality scoring data into a fuzzy relation matrix by adopting a parabolic membership function; and carrying out hierarchical weighted calculation by combining a preset evaluation index weight and a fuzzy relation matrix to generate a quantized cloud disaster backup service comprehensive evaluation result.
2. The cloud computing service quality evaluation method of desired weighting and fuzzy mathematics according to claim 1, further comprising, When the system is started, a multi-dimensional hierarchical cloud disaster recovery service quality evaluation index system is constructed, which concretely comprises, Based on a preset cloud disaster recovery service quality evaluation standard, decomposing the cloud disaster recovery service quality into a plurality of primary evaluation dimensions, wherein the primary evaluation dimensions cover the functionality, non-functionality and user experience characteristics of a cloud disaster recovery system; Configuring at least one secondary evaluation index for each primary evaluation dimension, wherein the secondary evaluation index obtains quantitative data through a standardized test tool or a technical detection interface and generates an index data set which can be used for expert importance scores and user service quality scores; Mapping the second-level evaluation index to a specific component or operation and maintenance flow of the cloud disaster recovery system so as to support the positioning of the service quality problem; And calculating the weight of the primary evaluation dimension by an analytic hierarchy process, determining the priority of the primary evaluation dimension, and analyzing the relevance between the primary evaluation dimension and the secondary evaluation index to ensure that the index data set supports the accuracy of the subsequent expert scoring and the user scoring.
3. The cloud computing service quality evaluation method of desired weighting and fuzzy mathematics according to claim 2, wherein obtaining importance scoring data of the expert group for the secondary evaluation index comprises, Performing importance ranking on the secondary evaluation indexes under the same primary evaluation dimension by a plurality of experts through a grading interface provided by a standardized online grading system or a cloud computing platform based on an index data set of the secondary evaluation indexes, and performing percentage grading based on the importance ranking to generate grading data, wherein the grading of the low-importance secondary evaluation indexes is not higher than that of the high-importance secondary evaluation indexes so as to ensure grading consistency; Constructing a scoring matrix based on the scoring data: ; Wherein, the A scoring matrix representing a kth level of evaluation dimension, The scoring data of the mth secondary evaluation index under the kth primary evaluation dimension, The method comprises the steps that scoring data of an nth expert on an mth secondary evaluation index are obtained, m represents the number of the secondary evaluation indexes, and n represents the number of the experts; By calculating the scoring matrix per row vector Coefficient of variation of (2) And verifying consistency of the scoring data, wherein the formula is as follows: ; Wherein, the Is that Is used for the average value of (a), Representing the relative dispersion of the scoring data; If it is And if the preset threshold value is exceeded, re-executing the grading data collection or adjusting the grading data through the online grading system.
4. The cloud disaster recovery service quality assessment method for expectation weighting and fuzzy mathematics according to claim 3, wherein the statistical characteristic parameters of the scoring data are calculated by adopting a reverse cloud model algorithm, and the weight of each secondary evaluation index is generated, comprising, Matrix the scoring Is defined by each row vector of (a) As an input, statistical feature parameters are calculated using an inverse cloud model algorithm, including, Desirably, the formula is: ; Wherein, the For expectations, reflecting the importance core value of the ith secondary evaluation index in the kth primary evaluation dimension, Scoring the ith secondary evaluation index for the jth expert; variance, formula: ; Wherein, the The variance is used for representing the fluctuation of the scoring data of the ith secondary evaluation index under the kth primary evaluation dimension and reflecting the discrete degree of the scoring data; Entropy, the formula is: ; Wherein, the The entropy is the uncertainty of scoring data of an ith second-level evaluation index under the kth first-level evaluation dimension, and the randomness of the data is reflected; for the desire Normalizing, wherein the weight of the ith secondary evaluation index under the kth primary evaluation dimension is as follows: ; Wherein, the The weight of the ith secondary evaluation index in the kth primary evaluation dimension, The expected value of the ith secondary evaluation index in the kth primary evaluation dimension is obtained, and m is the total number of the secondary evaluation indexes in the kth primary evaluation dimension; forming weight vectors Wherein, the method comprises the steps of, The weight vector of the dimension is evaluated for the kth level.
5. The cloud computing power quality of service evaluation method of desired weighting and fuzzy mathematics according to claim 4, wherein said calculating statistical characteristic parameters further comprises, Based on the variance Sum entropy And calculating the super entropy, wherein the formula is as follows: ; Wherein, the The super entropy is used for representing the consensus degree and the stability of the scoring data; When (when) Generating hyper-entropy diagnosis information, indicating that the domain range of the secondary evaluation index exceeds a preset threshold value, and triggering refining or redefining operation of the secondary evaluation index; the refinement includes decomposing the secondary evaluation index with the domain range exceeding a preset threshold into more specific sub-indexes.
6. The cloud computing power quality of service evaluation method of claim 2, wherein said converting the quality of service scoring data into a fuzzy relationship matrix using parabolic membership functions comprises, Based on an index data set of the secondary evaluation index, acquiring quantitative data of cloud disaster backup service through a standardized test tool or a technical detection interface, acquiring service quality scores aiming at the secondary evaluation index through a user terminal, generating user score data by adopting a preset evaluation grade, removing the highest score and the lowest score, and calculating a mean value as the service quality score of the secondary evaluation index; the evaluation data are mapped into a fuzzy relation matrix by adopting K parabolic membership functions: ; Wherein, the A membership value representing the score value and a preset evaluation grade; The parabolic membership function is adapted to different user scoring styles by adjusting parameters, wherein the parameters comprise a function index K, a central value and a range width.
7. The cloud disaster recovery service quality evaluation method of expected weighting and fuzzy mathematics according to claim 5 or 6, wherein the step of performing hierarchical weighted calculation by combining a preset evaluation index weight and a fuzzy relation matrix to generate a quantized cloud disaster recovery service comprehensive evaluation result comprises, The weight vector Matrix of fuzzy relation And (3) performing matrix multiplication, and calculating a fuzzy judgment vector, wherein the formula is as follows: ; Wherein, the A judgment result representing an evaluation dimension; Calculating and evaluating the quantization scores of the dimensions by using a preset evaluation grade score set E, wherein the formula is as follows: ; Wherein, the A composite score representing the evaluation dimension, Transpose of the set of evaluation rank scores E, for converting the row vector E into a column vector; based on each level of evaluation dimension Comprehensive scoring of cloud disaster backup service by layer calculation ; According to And Identifying a point of weakness in quality of service, including, Comparing the quantization score of each primary evaluation dimension with a preset threshold, when When the threshold value is lower than the threshold value, marking the kth primary evaluation dimension as a weak link, and referring to the comprehensive score Evaluating the impact of the weak point on the overall quality of service; and combining the hyper-entropy diagnosis information, positioning a second-level evaluation index with the range of the domain exceeding a preset threshold value, triggering refining or redefining operation, and optimizing an index system structure.
8. The cloud disaster recovery service quality evaluation system for the expected weighting and fuzzy mathematics is characterized by comprising an index system construction module, a weight generation module and a fuzzy evaluation and comprehensive calculation module; The index system construction module is used for responding to a cloud disaster recovery service quality evaluation request initiated by a user or detecting a preset disaster recovery service evaluation trigger signal by a service system and acquiring service quality scoring data of the service system based on a preset evaluation index; the weight generation module is used for converting the service quality scoring data into a fuzzy relation matrix by adopting a parabolic membership function; And the comprehensive calculation module is used for carrying out hierarchical weighted calculation by combining preset evaluation index weights and a fuzzy relation matrix to generate a quantized cloud disaster backup service comprehensive evaluation result.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor when executing the program implements the steps in the cloud disaster recovery service quality assessment method for desired weighting and fuzzy mathematics as set forth in any one of claims 1-7.
10. A storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the cloud disaster recovery service quality assessment method for desired weighting and fuzzy mathematics according to any one of claims 1 to 7.

Description

Cloud disaster backup service quality evaluation method for hope weighting and fuzzy mathematics Technical Field The invention belongs to the technical field of cloud computing service quality evaluation, and particularly relates to a cloud disaster recovery service quality evaluation method with expected weighting and fuzzy mathematics. Background With the rapid development of cloud computing, virtualization and data center technology, a cloud platform becomes a core infrastructure in the fields of government affairs, medical treatment, finance and the like through high elasticity, high availability and resource expandability. The cloud disaster recovery service provides the capabilities of data protection, fault taking over, RPO/RTO management and control and the like through cloud backup and cloud disaster recovery functions, and ensures service continuity and data security. However, the existing cloud disaster recovery service quality evaluation method has the remarkable defects that firstly, an evaluation system is incomplete and focuses on performance indexes such as RPO/RTO and the like, the evaluation of the performance indexes is caused by neglecting dimensionalities such as functional compatibility, information safety and usability, and the like, secondly, a traditional weighting method such as an Analytic Hierarchy Process (AHP) depends on subjective comparison, consistency deviation is easy to generate, expert knowledge is ignored by an entropy weighting method, weight stability is poor, in addition, user scoring is often expressed in a fuzzy language, uncertainty of the existing model is difficult to effectively model, and finally, a traceable mathematical modeling mechanism is lacked, evaluation results are poor in repeatability, and service optimization is difficult to guide. The invention provides a cloud disaster recovery service quality evaluation method combining a reverse cloud model and fuzzy mathematics, which solves the problems of incomplete evaluation system, strong weight subjectivity and insufficient fuzzy information modeling in the prior art, and generates a traceable comprehensive scoring result through a multidimensional index system, objective weighting and layering evaluation. Disclosure of Invention The invention aims to provide a cloud disaster recovery service quality evaluation method with expected weighting and fuzzy mathematics, which aims to solve the technical problems of incomplete existing cloud disaster recovery service quality evaluation system, strong subjectivity of weight distribution, insufficient modeling of user scoring ambiguity and non-traceability of results. In order to achieve one of the above objects, an embodiment of the present invention provides a cloud disaster recovery service quality evaluation method for which weighting and fuzzy mathematics are desired, the method comprising, Responding to a cloud disaster recovery service quality evaluation request initiated by a user or detecting a preset disaster recovery service evaluation trigger signal by a service system, and acquiring service quality scoring data of the service system based on a preset evaluation index; Converting the service quality scoring data into a fuzzy relation matrix by adopting a parabolic membership function; and carrying out hierarchical weighted calculation by combining a preset evaluation index weight and a fuzzy relation matrix to generate a quantized cloud disaster backup service comprehensive evaluation result. As a further improvement of an embodiment of the invention, the method also comprises the steps of constructing a multi-dimensional hierarchical cloud disaster recovery service quality evaluation index system when the system is started, specifically comprising, Based on a preset cloud disaster recovery service quality evaluation standard, decomposing the cloud disaster recovery service quality into a plurality of primary evaluation dimensions, wherein the primary evaluation dimensions cover the functionality, non-functionality and user experience characteristics of a cloud disaster recovery system; Configuring at least one secondary evaluation index for each primary evaluation dimension, wherein the secondary evaluation index obtains quantitative data through a standardized test tool or a technical detection interface and generates an index data set which can be used for expert importance scores and user service quality scores; Mapping the second-level evaluation index to a specific component or operation and maintenance flow of the cloud disaster recovery system so as to support the positioning of the service quality problem; And calculating the weight of the primary evaluation dimension by an analytic hierarchy process, determining the priority of the primary evaluation dimension, and analyzing the relevance between the primary evaluation dimension and the secondary evaluation index to ensure that the index data set supports the accuracy of the subsequent exper