Credibility evaluation method and device, equipment, medium and program product

Abstract

The invention relates to a credibility evaluation method and device, equipment, a medium and a program product. The credibility assessment method comprises the following steps: acquiring credibility data of at least one to-be-assessed application under different to-be-assessed indexes; for each to-be-evaluated index, selecting feature reference data of the to-be-evaluated index from the credibility data of each to-be-evaluated application under the corresponding to-be-evaluated index; for any to-be-evaluated application, determining relevancy data between the credibility data of the to-be-evaluated application under the to-be-evaluated index and the corresponding feature reference data; and determining a credibility assessment value of the to-be-assessed application according to the correlation data of the to-be-assessed application under different to-be-assessed indexes. Through the steps, the accuracy of credibility evaluation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of credibility assessment, and in particular to a credibility assessment method, device, equipment, medium and program product. Background Art

[0002] With the rapid development of information technology, software applications are becoming more and more widespread, and the credibility issues they bring are becoming increasingly prominent. Especially for the supply chain, since each link of the supply chain is not isolated, but interconnected and mutually influential, when there is a problem with the credibility of the supply chain software, it will affect all links of the entire supply chain and bring losses to the enterprise.

[0003] In traditional technology, an evaluation threshold is usually configured for a software application, the software application is evaluated through an evaluation object, and the evaluation value of the software application is calculated, and then the evaluation value is compared with the evaluation threshold to evaluate the quality of the software application. Although this method can evaluate software applications to a certain extent, it still has the problem of low accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide credibility assessment methods, devices, equipment, media and program products that can improve the accuracy of assessment in response to the above technical problems.

[0005] In a first aspect, the present application provides a credibility assessment method, comprising:

[0006] Obtaining credibility data of at least one application to be evaluated under different indicators to be evaluated;

[0007] For each indicator to be evaluated, select characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated;

[0008] For any application to be evaluated, determine the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data;

[0009] The credibility evaluation value of the application to be evaluated is determined according to the correlation data of the application to be evaluated under different evaluation indicators.

[0010] In one of the embodiments, the number of feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated corresponds to different feature types; determining the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data, including: for each indicator to be evaluated, determining the target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated; for each feature type, determining the maximum target distance and the minimum target distance from the target distances of different indicators to be evaluated of the application to be evaluated under the feature type; for each indicator to be evaluated, determining the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the corresponding minimum target distance under the feature type.

[0011] In one of the embodiments, the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type is determined according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the minimum target distance under the feature type, including: adjusting the minimum target distance under the corresponding feature type according to the maximum target distance under the feature type to obtain a first adjusted distance; adjusting the target distance corresponding to the indicator to be evaluated according to the maximum target distance under the feature type to obtain a second adjusted distance; determining the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the ratio of the first adjusted distance to the second adjusted distance.

[0012] In one of the embodiments, the number of feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated corresponds to different feature types; according to the correlation data of the application to be evaluated under different indicators to be evaluated, the credibility evaluation value of the application to be evaluated is determined, including: for each feature type, according to the target weight corresponding to each indicator to be evaluated, the correlation data of different indicators to be evaluated of the application to be evaluated under the feature type are weighted and obtained to obtain the credibility evaluation value of the application to be evaluated under the corresponding feature type; the credibility evaluation values ​​of the application to be evaluated under different feature types are integrated to obtain the credibility evaluation value of the application to be evaluated.

[0013] In one of the embodiments, the target weight corresponding to each indicator to be evaluated is determined in the following manner: the target evaluation category to which the target indicator to be evaluated belongs and the preset total weight corresponding to the target evaluation category are obtained; the target indicator to be evaluated is selected from each indicator to be evaluated; evaluation data of different indicators to be evaluated under the target evaluation category are obtained; a first weight of the target indicator to be evaluated is determined based on the evaluation data of different indicators to be evaluated under the target evaluation category; and the target weight of the target indicator to be evaluated is determined based on the first weight of the target indicator to be evaluated and the preset total weight corresponding to the target evaluation category.

[0014] In one of the embodiments, the evaluation data includes a fuzzy number for evaluating the importance of a corresponding indicator to be evaluated by at least one evaluation object; determining a first weight of a target indicator to be evaluated based on the evaluation data of different indicators to be evaluated under an evaluation category, including: determining the fuzzy weights of different indicators to be evaluated based on the evaluation data of different indicators to be evaluated under the target evaluation category; determining the total fuzzy weight of all indicators to be evaluated under the target evaluation category based on the fuzzy weights of different indicators to be evaluated; determining the first weight of the target indicator to be evaluated based on the fuzzy weight of the target indicator to be evaluated and the total fuzzy weight of all indicators to be evaluated under the target evaluation category.

[0015] In a second aspect, the present application also provides a credibility assessment device, comprising:

[0016] An acquisition module, used to acquire credibility data of at least one application to be evaluated under different indicators to be evaluated;

[0017] A selection module is used to select characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated for each indicator to be evaluated;

[0018] A first determination module is used to determine, for any application to be evaluated, correlation data between the credibility data of the application to be evaluated under the evaluation index and the corresponding feature reference data;

[0019] The second determination module is used to determine the credibility evaluation value of the application to be evaluated according to the correlation data of the application to be evaluated under different indicators to be evaluated.

[0020] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0021] Obtaining credibility data of at least one application to be evaluated under different indicators to be evaluated;

[0022] For each indicator to be evaluated, select characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated;

[0023] For any application to be evaluated, determine the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data;

[0024] The credibility evaluation value of the application to be evaluated is determined according to the correlation data of the application to be evaluated under different evaluation indicators.

[0025] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0026] Obtaining credibility data of at least one application to be evaluated under different indicators to be evaluated;

[0027] For each indicator to be evaluated, select characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated;

[0028] For any application to be evaluated, determine the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data;

[0029] The credibility evaluation value of the application to be evaluated is determined according to the correlation data of the application to be evaluated under different evaluation indicators.

[0030] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0031] Obtaining credibility data of at least one application to be evaluated under different indicators to be evaluated;

[0032] For each indicator to be evaluated, select characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated;

[0033] For any application to be evaluated, determine the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data;

[0034] The credibility evaluation value of the application to be evaluated is determined according to the correlation data of the application to be evaluated under different evaluation indicators.

[0035] The above-mentioned credibility assessment method, device, equipment, medium and program product provide an assessment basis for different applications to be evaluated by introducing characteristic reference data. Since the characteristic reference data is selected from the credibility data of the corresponding indicators to be evaluated of each application to be evaluated, an evaluation benchmark is constructed between each application to be evaluated, which is convenient for accurately and quantifiably evaluating the performance of the target application to be evaluated relative to other applications to be evaluated. By determining the correlation data between the credibility data of the target application to be evaluated under the indicators to be evaluated and the corresponding characteristic reference data, and by determining the credibility assessment value of the target application to be evaluated based on the correlation data of the target application to be evaluated under different indicators to be evaluated, a quantitative analysis of the credibility of different indicators to be evaluated and a comprehensive evaluation of the target application to be evaluated are achieved, thereby improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 is a flow chart of a credibility evaluation method in one embodiment;

[0038] Figure 2 is a flow chart of a step of determining relevance data in one embodiment;

[0039] Figure 3 Schematic diagram of a process for determining a target weight in one embodiment;

[0040] Figure 4 is a flow chart of a credibility assessment method in another embodiment;

[0041] Figure 5 is a structural block diagram of a credibility evaluation device in one embodiment;

[0042] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In one embodiment, Figure 1 As shown, a credibility evaluation method is provided. This embodiment takes the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0045] S110: Obtain credibility data of at least one application to be evaluated under different indicators to be evaluated.

[0046] The application to be evaluated refers to the application or software that needs to be evaluated for credibility, such as supply chain software, etc. The indicator to be evaluated refers to the dimension that needs to be evaluated for the application to be evaluated.

[0047] Exemplarily, the evaluation category of the indicator to be evaluated may include at least one of the availability category, reliability category, security category, maintainability category, etc. Among them, the availability category includes at least one of functional accuracy, ease of operation, and ease of installation; the reliability category includes at least one of error frequency and fault tolerance; the security category includes at least one of confidentiality, integrity, and real-time performance; the maintainability category includes at least one of diagnostic difficulty, modifiability, stability, recoverability, and self-improvement. It should be noted that this application does not impose any restrictions on the specific types of indicators to be evaluated.

[0048] Among them, the credibility data refers to the credibility of the application to be evaluated under the corresponding indicator to be evaluated. The credibility data can be obtained by normalizing the initial credibility data. In particular, the initial credibility data can be normalized by a utility function. Among them, the corresponding utility function can be selected according to the type of indicator to be evaluated. The type of indicator to be evaluated can be based on cost type or benefit type. The initial credibility data can be obtained by scoring the evaluation object, analyzing historical data, or a combination of these. Exemplarily, the credibility of the application to be evaluated under the corresponding indicator to be evaluated can be evaluated by an expert or an expert group, and the initial credibility data can be determined by data fitting. Exemplarily, it can also be based on historical data analysis, by obtaining historical data such as historical diagnosis time, historical installation time and historical failure rate of the software to be evaluated, so as to determine the initial credibility data of the corresponding indicator to be evaluated.

[0049] For ease of understanding, ij It represents the initial credibility data of the i-th application to be evaluated in each j-th indicator to be evaluated, 1≤i≤m and 1≤j≤n.

[0050] Optionally, the following formula can be used to calculate the initial credibility data s ij Normalize it and get the credibility data r ij :

[0051] .

[0052] Optionally, the following formula can be used to calculate the initial credibility data s ij Normalize it and get the credibility data r ij :

[0053] .

[0054] S120 , for each indicator to be evaluated, selecting characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated.

[0055] The feature reference data may be understood as an evaluation benchmark of the indicator to be evaluated constructed between the applications to be evaluated. The number of feature reference data is at least one, that is, the feature reference data for the indicator to be evaluated may be one, two or more. Optionally, the feature types of different feature reference data of the indicator to be evaluated may be the same or different.

[0056] In one embodiment, the characteristic reference data of the indicator to be evaluated can be selected by the positive and negative ideal point method. Among them, the positive and negative ideal point method is a multi-objective decision-making method, also known as the ideal point method or the extreme point method. Exemplarily, the maximum credibility data and / or the minimum credibility data can be selected from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated, and the maximum credibility data and / or the minimum credibility data can be used as the characteristic reference data of the indicator to be evaluated.

[0057] For ease of understanding, ij It represents the credibility data of the i-th application to be evaluated at the j-th indicator to be evaluated, 1≤i≤m and 1≤j≤n.

[0058] Optionally, the maximum credibility data r of the kth indicator to be evaluated is determined by the following formula: k + :

[0059] ;

[0060] The above formula can be understood as selecting the maximum value from the credibility data of the kth indicator to be evaluated of all the applications to be evaluated and taking it as the maximum credibility data r k + .

[0061] Similarly, the minimum credibility data r of the kth indicator to be evaluated is determined by the following formula: k - :

[0062] ;

[0063] The above formula can be understood as selecting the minimum value from the credibility data of the kth indicator to be evaluated of all the applications to be evaluated and taking it as the minimum credibility data r k - .

[0064] S130 . For any application to be evaluated, determine the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data.

[0065] The correlation data is used to reflect the correlation between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data.

[0066] In an optional implementation, the correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data can be determined based on the grey correlation analysis method. Optionally, the correlation data between the credibility data and the corresponding feature reference data can be determined by determining the target distance between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data.

[0067] S140: Determine a credibility evaluation value of the application to be evaluated according to correlation data of the application to be evaluated under different evaluation indicators.

[0068] The credibility evaluation value is used to represent the comprehensive credibility of the application to be evaluated. The larger the credibility evaluation value of the application to be evaluated is, the higher the comprehensive credibility of the application to be evaluated is.

[0069] In an optional embodiment, different feature reference data of the indicator to be evaluated correspond to different feature types. According to the correlation data of the application to be evaluated under different indicators to be evaluated, the credibility evaluation value of the application to be evaluated is determined, including: for each feature type, according to the target weight corresponding to each indicator to be evaluated, the correlation data of different indicators to be evaluated of the application to be evaluated under the feature type are weighted and obtained to obtain the credibility evaluation value of the application to be evaluated under the corresponding feature type; the credibility evaluation values ​​of the application to be evaluated under different feature types are merged to obtain the credibility evaluation value of the application to be evaluated.

[0070] The feature reference data may include at least one of maximum credibility data corresponding to the maximum value type and minimum credibility data corresponding to the minimum value type. The target weight may be set by a technician according to needs or experience, or determined through a large number of experiments, or determined in other ways, and this application does not make any limitation on this.

[0071] For the sake of convenience, the characteristic reference data of a certain application to be evaluated under the jth indicator to be evaluated is taken as the maximum credibility data r j + and the minimum credibility data r j - For example, in order to facilitate the distinction, the credibility data of the application to be evaluated under the jth indicator to be evaluated and the corresponding maximum credibility data r j + The correlation data between ; The credibility data of the application to be evaluated under the jth indicator to be evaluated and the corresponding minimum credibility data r j - The correlation data between At the same time, the credibility evaluation value of the application to be evaluated in the corresponding maximum value type is the first credibility evaluation value ; The credibility evaluation value of the application to be evaluated in the corresponding minimum value type is the second credibility evaluation value .

[0072] Exemplarily, the first credibility evaluation value of the application to be evaluated is , determined by the following formula:

[0073] ;

[0074] in, represents the target weight of the jth indicator to be evaluated; Indicates the first correlation.

[0075] Exemplarily, the second credibility evaluation value of the application to be evaluated is , determined by the following formula:

[0076] ;

[0077] in, represents the target weight of the jth indicator to be evaluated; Indicates the second correlation.

[0078] Exemplarily, the credibility evaluation values ​​of the application to be evaluated under different feature types are integrated by the following formula to obtain the credibility evaluation value of the application to be evaluated: :

[0079] ;

[0080] in, represents a first credibility evaluation value; represents a second credibility evaluation value; Indicates the credibility evaluation value of the application to be evaluated.

[0081] In the embodiment of the present application, by introducing feature reference data, an evaluation basis is provided for different applications to be evaluated. Since the feature reference data is selected from the credibility data of the corresponding indicators to be evaluated of each application to be evaluated, an evaluation benchmark is constructed between each application to be evaluated, which is convenient for accurately and quantifiably evaluating the performance of the target application to be evaluated relative to other applications to be evaluated. By determining the correlation data between the credibility data of the target application to be evaluated under the indicators to be evaluated and the corresponding feature reference data, and by determining the credibility evaluation value of the target application to be evaluated according to the correlation data of the target application to be evaluated under different indicators to be evaluated, a quantitative analysis of the credibility of different indicators to be evaluated and a comprehensive evaluation of the target application to be evaluated are achieved, thereby improving the accuracy of the evaluation.

[0082] Based on the technical solutions of the above embodiments, the present application also provides an optional embodiment, in which the steps for determining the correlation data are refined.

[0083] See also Figure 2 The steps for determining the correlation data shown include:

[0084] S210: For each indicator to be evaluated, determine a target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated.

[0085] The number of feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated correspond to different feature types.

[0086] For ease of understanding, the characteristic reference data of a certain application to be evaluated under the jth indicator to be evaluated is taken as the maximum credibility data r j + and the minimum credibility data r j - For example, at the same time, the credibility data of the application to be evaluated under the jth indicator to be evaluated is r j In order to distinguish, the credibility data under the evaluation index and the maximum credibility data r j + The target distance between is the first target distance ; The credibility data and the minimum credibility data r under the evaluation index j - The target distance between is the second target distance .

[0087] For example, the credibility data r j With the maximum credibility data r j + The first target distance between , determined by the following formula:

[0088] .

[0089] For example, the credibility data r j With the minimum credibility data r j - The second target distance between , determined by the following formula:

[0090] .

[0091] S220 . For each feature type, determine a maximum target distance and a minimum target distance from target distances of different to-be-evaluated indicators of the to-be-evaluated application under the feature type.

[0092] For ease of understanding, continue to refer to the above example, the reliability data r has been determined j With the maximum credibility data r j + The first target distance between Therefore, the first target distance The maximum target distance in can be expressed as: ; First target distance The minimum target distance in can be expressed as: .

[0093] Similarly, the reliability data r has been determined above j With the minimum credibility data r j - The second target distance between Therefore, the second target distance The maximum target distance in can be expressed as: ; Second target distance The minimum target distance in can be expressed as: .

[0094] S230, for each indicator to be evaluated, determine the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the corresponding minimum target distance under the feature type.

[0095] In an optional embodiment, the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type is determined by the following steps: according to the maximum target distance under the feature type, the minimum target distance under the corresponding feature type is adjusted to obtain a first adjusted distance; according to the maximum target distance under the feature type, the target distance corresponding to the indicator to be evaluated is adjusted to obtain a second adjusted distance; according to the ratio of the first adjusted distance to the second adjusted distance, the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type is determined.

[0096] For ease of understanding, continue to refer to the above example, the target distance corresponding to the indicator to be evaluated, as well as the maximum target distance and the corresponding minimum target distance under the feature type have been determined. j + The correlation data between ; The credibility data of the indicator to be evaluated and the minimum credibility data r j - The correlation data between .

[0097] Exemplarily, the first correlation is calculated by the following formula: :

[0098] ;

[0099] in, Indicates the first target distance; Indicates the distance to the first target The maximum target distance in ; Indicates the distance to the first target The minimum target distance in ; It represents a preset coefficient, which can be set by technicians according to needs or experience, or determined through a large number of experiments, and this application does not impose any limitation on this.

[0100] Exemplarily, the second correlation is calculated by the following formula: :

[0101] ;

[0102] in, Indicates the distance to the second target; Indicates the distance to the second target The maximum target distance in ; Indicates the distance to the second target The minimum target distance in ; It represents a preset coefficient, which can be set by technicians according to needs or experience, or determined through a large number of experiments, and this application does not impose any limitation on this.

[0103] In an embodiment of the present application, by determining the target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated, a quantitative basis is provided for determining the correlation data. At the same time, by determining the maximum target distance and the minimum target distance under the corresponding feature type, the correlation data can be further corrected, thereby improving the accuracy of calculating the correlation data.

[0104] Based on the technical solutions of the above embodiments, the present application also provides an optional embodiment, in which the step of determining the target weight is refined.

[0105] See also Figure 3 The steps for determining the target weight shown include:

[0106] S310, obtaining the target evaluation category to which the target indicator to be evaluated belongs and the preset total weight corresponding to the target evaluation category; the target indicator to be evaluated is selected from the indicators to be evaluated.

[0107] Among them, the indicators to be evaluated may involve multiple evaluation categories, such as availability category, reliability category, security category and maintainability category, etc. The preset total weight corresponding to the target evaluation category can be set by the technicians according to needs or experience, or determined through a large number of experiments, and this application does not make any limitation on this.

[0108] S320: Obtain evaluation data of different indicators to be evaluated under the target evaluation category.

[0109] Among them, the evaluation data can be understood as the evaluation value given by the evaluation object according to the importance of different indicators to be evaluated;

[0110] In an optional embodiment, the evaluation data includes at least one fuzzy number for evaluating the importance of the corresponding indicator to be evaluated by the evaluation object; in particular, the evaluation data is a trapezoidal fuzzy number (a, b, c, d). As shown in Table 1, the fuzzy numbers corresponding to some fuzzy words, multiple fuzzy words can be pre-set, and fuzzy numbers are assigned to different fuzzy words. The evaluation object can evaluate the evaluation indicator based on the fuzzy words with the corresponding fuzzy numbers (a, b, c, d). This application does not impose any restrictions on the specific form of the fuzzy words, nor does it impose any restrictions on the values ​​of the corresponding fuzzy numbers.

[0111] Table 1 - Fuzzy numbers corresponding to some fuzzy words

[0112]

[0113] S330: Determine a first weight of a target indicator to be evaluated according to evaluation data of different indicators to be evaluated under the target evaluation category.

[0114] The first weight may be understood as the weight of the evaluation data of the target indicator to be evaluated in the evaluation data of all indicators to be evaluated in the target evaluation category.

[0115] In an optional embodiment, the first weight is calculated by the following steps: determining the fuzzy weights of different indicators to be evaluated under the target evaluation category based on the evaluation data of different indicators to be evaluated; determining the total fuzzy weight of all indicators to be evaluated under the target evaluation category based on the fuzzy weights of different indicators to be evaluated; determining the first weight of the target indicator to be evaluated based on the fuzzy weight of the target indicator to be evaluated and the total fuzzy weight of all indicators to be evaluated under the target evaluation category.

[0116] Among them, fuzzy weight can be understood as the use of fuzzy set theory to assign weights to multiple indicators or factors in a fuzzy environment to deal with the fuzziness and uncertainty of weight assignment.

[0117] In an optional embodiment, the fuzzy weight is determined by the following steps: determining at least one trapezoidal fuzzy number corresponding to the target indicator to be evaluated; determining a comprehensive fuzzy number based on the at least one trapezoidal fuzzy number; and averaging the values ​​on each data bit of the comprehensive fuzzy number to obtain the fuzzy weight.

[0118] For ease of understanding, take the indicator "functional accuracy" as an example. The trapezoidal fuzzy numbers of the three evaluators for the indicator are: "(5,7,7,9), (7,9,9,10) and (5,7,7,9)", and the comprehensive fuzzy number (17 / 3, 23 / 3, 23 / 3, 28 / 3) can be determined by taking the average. The fuzzy weight of each data bit of the comprehensive fuzzy number is 7.583. For ease of explanation, taking the fuzzy weights of 5.667 and 4.917 corresponding to other indicators to be evaluated under the target evaluation category as an example, it can be determined that the total fuzzy weight of all indicators to be evaluated under the target evaluation category is 18.167. By determining the ratio of the fuzzy weight of the target indicator to be evaluated to the total fuzzy weight, the first weight of the target indicator to be evaluated can be obtained. That is, 7.583 / 18.167=0.417.

[0119] S340: Determine a target weight of the target indicator to be evaluated according to the first weight of the target indicator to be evaluated and a preset total weight corresponding to the target evaluation category.

[0120] In an optional embodiment, the target weight of the target indicator to be evaluated is determined by multiplying the first weight of the target indicator to be evaluated by a preset total weight corresponding to the target evaluation category.

[0121] Continuing to refer to the above example, the first weight of the target indicator to be evaluated has been determined to be 0.417. For example, if the preset total weight corresponding to the target evaluation category is 0.17, the target weight of the target indicator to be evaluated is 0.07.

[0122] In the embodiment provided by the present application, the first weight of the target indicator to be evaluated is determined by presetting the preset total weight corresponding to the target evaluation category and based on the evaluation data of different indicators to be evaluated under the target evaluation category, thereby combining the preset total weight with the first weight to determine the target weight of the target indicator to be evaluated. By determining the target weight from two dimensions, while ensuring the objectivity and rationality of the target weight, the calculation difficulty is also reduced, which is conducive to improving the calculation efficiency.

[0123] Based on the technical solutions of the above embodiments, the present application also provides an optional embodiment, in which another specific implementation of the credibility evaluation method is provided.

[0124] See also Figure 4 Another credibility assessment method shown includes:

[0125] S410: Obtain credibility data of at least one application to be evaluated under different indicators to be evaluated.

[0126] S420 , for each indicator to be evaluated, selecting characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated.

[0127] S430. For any application to be evaluated, perform the following steps: for each indicator to be evaluated, determine the target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated; different feature reference data of the indicator to be evaluated correspond to different feature types.

[0128] S440 . For any application to be evaluated, perform the following steps: for each feature type, determine a maximum target distance and a minimum target distance from target distances of different indicators to be evaluated of the application to be evaluated under the feature type.

[0129] S450. For any application to be evaluated, perform the following steps: for each indicator to be evaluated, determine the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the corresponding minimum target distance under the feature type.

[0130] S460: Determine correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data.

[0131] S470 , for each feature type, according to the target weight corresponding to each indicator to be evaluated, weighted sum of the correlation data of different indicators to be evaluated of the application to be evaluated under the feature type is performed to obtain the credibility evaluation value of the application to be evaluated under the corresponding feature type.

[0132] S480: The credibility evaluation values ​​of the application to be evaluated under different feature types are integrated to obtain a credibility evaluation value of the application to be evaluated.

[0133] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0134] Based on the technical solutions of the above embodiments, the present application also provides a verification embodiment, in which a credibility index system is first provided for supply chain software, namely, the indicators to be evaluated for supply chain software as shown in Table 2. The indicators to be evaluated include six first-level categories, each of which has corresponding indicators to be evaluated.

[0135] Table 2-Indicators to be evaluated for supply chain software

[0136]

[0137] Secondly, as shown in Table 3, which is a comparison table of evaluation data and target weights, the evaluation data of each indicator to be evaluated is obtained, and the comprehensive fuzzy number is determined through the evaluation data. The fuzzy weight is determined according to the comprehensive fuzzy number, and the first weight of each indicator to be evaluated is determined.

[0138] Table 3-Comparison table of evaluation data and target weights

[0139]

[0140] By determining the preset total weight of availability I1, and multiplying the preset total weight by the first weight of each of the above-mentioned indicators to be evaluated, the target weight of each of the above-mentioned indicators to be evaluated can be obtained. Among them, the determination method of the comprehensive fuzzy number, fuzzy weight and first weight in Table 3 has been recorded in the above-mentioned embodiment and will not be repeated here.

[0141] Exemplarily, after determining the target weight, the embodiment of the present application obtains the credibility data of each application to be evaluated under different indicators to be evaluated. In this embodiment, the initial credibility data of 3 applications to be evaluated under 13 indicators to be evaluated are provided, and an initial credibility evaluation matrix is ​​formed, as follows:

[0142] ;

[0143] Among them, s ijIt represents the initial credibility data of the i-th application to be evaluated in each j-th indicator to be evaluated.

[0144] Exemplarily, after obtaining the initial credibility evaluation matrix, the initial credibility evaluation matrix is ​​normalized. During the normalization process, a corresponding utility function can be selected based on actual conditions. After normalization, credibility data of three applications to be evaluated under 13 indicators to be evaluated are obtained, and a credibility evaluation matrix is ​​formed, as follows:

[0145] ;

[0146] Among them, r ij Represents the credibility data of the i-th application to be evaluated in each j-th indicator to be evaluated.

[0147] For example, after obtaining the credibility data of three applications to be evaluated under 13 indicators to be evaluated, the embodiment of the present application determines the feature reference data under the 13 indicators to be evaluated, that is, the maximum credibility data set r + and the minimum credibility data set r - :

[0148] ;

[0149] ;

[0150] It is not difficult to understand that the maximum credibility data set r + The jth data in the above table represents the maximum credibility data under the jth indicator to be evaluated; correspondingly, the minimum credibility data set r - The j-th data in represents the minimum credibility data under the j-th indicator to be evaluated.

[0151] Exemplarily, after determining the maximum credibility data and the minimum credibility data, the embodiment of the present application calculates the credibility evaluation values ​​of the three applications to be evaluated respectively, and determines that the credibility evaluation value of the third application to be evaluated is the largest, so the third application to be evaluated has the highest credibility.

[0152] Based on the same inventive concept, the embodiment of the present application also provides a credibility assessment device for implementing the credibility assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of one or more credibility assessment device embodiments provided below can refer to the limitations of the credibility assessment method above, and will not be repeated here.

[0153] In an exemplary embodiment, Figure 5As shown, a credibility evaluation device is provided, including: an acquisition module 510, a selection module 520, a first determination module 530 and a second determination module 540, wherein:

[0154] The acquisition module 510 is used to acquire the credibility data of at least one application to be evaluated under different indicators to be evaluated.

[0155] The selection module 520 is used to select characteristic reference data of each indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated.

[0156] The first determination module 530 is used to determine, for any application to be evaluated, correlation data between the credibility data of the application to be evaluated under the evaluation indicator and corresponding feature reference data.

[0157] The second determination module 540 is used to determine the credibility evaluation value of the application to be evaluated according to the correlation data of the application to be evaluated under different indicators to be evaluated.

[0158] In one embodiment, the first determination module 530 includes: a first determination unit, used to determine the target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated; the number of feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated correspond to different feature types; a second determination unit, used to determine the maximum target distance and the minimum target distance from the target distances of different indicators to be evaluated of the application to be evaluated under the feature type; a third determination unit, used to determine the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type based on the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the corresponding minimum target distance under the feature type.

[0159] In one embodiment, the third determination unit includes: a first adjustment subunit, used to adjust the minimum target distance under the corresponding feature type according to the maximum target distance under the feature type, to obtain a first adjusted distance; a second adjustment subunit, used to adjust the target distance corresponding to the indicator to be evaluated according to the maximum target distance under the feature type, to obtain a second adjusted distance; the first determination subunit is used to determine the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the ratio of the first adjustment distance to the second adjustment distance.

[0160] In one embodiment, the second determination module 540 includes: a first processing unit, which is used to perform weighted sum of the correlation data of different indicators to be evaluated of the application to be evaluated under the feature type according to the target weight corresponding to each indicator to be evaluated, so as to obtain the credibility evaluation value of the application to be evaluated under the corresponding feature type; and a second processing unit, which is used to fuse the credibility evaluation values ​​of the application to be evaluated under different feature types, so as to obtain the credibility evaluation value of the application to be evaluated.

[0161] In one embodiment, the second determination module 540 also includes: a first acquisition unit, used to obtain the target evaluation category to which the target indicator to be evaluated belongs and the preset total weight corresponding to the target evaluation category; the target indicator to be evaluated is selected from each indicator to be evaluated; a second acquisition unit, used to obtain evaluation data of different indicators to be evaluated under the target evaluation category; the evaluation data includes at least one fuzzy number for evaluating the importance of the corresponding indicator to be evaluated by the evaluation object; a fourth determination unit, used to determine the first weight of the target indicator to be evaluated according to the evaluation data of different indicators to be evaluated under the target evaluation category; a fifth determination unit, used to determine the target weight of the target indicator to be evaluated according to the first weight of the target indicator to be evaluated and the preset total weight corresponding to the target evaluation category.

[0162] In one embodiment, the fourth determination unit includes: a second determination subunit, used to determine the fuzzy weights of different indicators to be evaluated under the target evaluation category based on the evaluation data of different indicators to be evaluated; a third determination subunit, used to determine the total fuzzy weight of all indicators to be evaluated under the target evaluation category based on the fuzzy weights of different indicators to be evaluated; and a fourth determination subunit, used to determine the first weight of the target indicator to be evaluated based on the fuzzy weight of the target indicator to be evaluated and the total fuzzy weight of all indicators to be evaluated under the target evaluation category.

[0163] Each module in the above credibility assessment device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0164] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a credibility assessment method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0165] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0166] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0168] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0169] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0171] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A credibility assessment method, characterized in that: The method comprises: Obtaining credibility data of at least one application to be evaluated under different indicators to be evaluated; For each indicator to be evaluated, selecting characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated; For any application to be evaluated, determining correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and corresponding feature reference data; The credibility evaluation value of the application to be evaluated is determined according to the correlation data of the application to be evaluated under different indicators to be evaluated.

2. The method according to claim 1, characterized in that The number of the feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated correspond to different feature types; The determining of correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data includes: For each indicator to be evaluated, determining a target distance between the credibility data under the indicator to be evaluated and the feature reference data of each feature type under the indicator to be evaluated; For each feature type, determining a maximum target distance and a minimum target distance from target distances of different to-be-assessed indicators of the to-be-assessed application under the feature type; For each indicator to be evaluated, the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type is determined according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the corresponding minimum target distance under the feature type.

3. The method according to claim 2, characterized in that The step of determining the correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type according to the target distance corresponding to the indicator to be evaluated, and the maximum target distance and the minimum target distance under the feature type, comprises: According to the maximum target distance under the feature type, the minimum target distance under the corresponding feature type is adjusted to obtain a first adjusted distance; According to the maximum target distance under the feature type, the target distance corresponding to the indicator to be evaluated is adjusted to obtain a second adjusted distance; According to the ratio of the first adjustment distance to the second adjustment distance, correlation data between the credibility data of the indicator to be evaluated and feature reference data of the corresponding feature type is determined.

4. The method according to any one of claims 1 to 3, characterized in that: The number of the feature reference data of the indicator to be evaluated is at least one, and different feature reference data of the indicator to be evaluated correspond to different feature types; The step of determining the credibility evaluation value of the application to be evaluated according to the correlation data of the application to be evaluated under different indicators to be evaluated includes: For each feature type, according to the target weight corresponding to each indicator to be evaluated, the relevance data of different indicators to be evaluated of the application to be evaluated under the feature type are weighted and summed to obtain the credibility evaluation value of the application to be evaluated under the corresponding feature type; The credibility evaluation values ​​of the application to be evaluated under different feature types are integrated to obtain the credibility evaluation value of the application to be evaluated.

5. The method according to claim 4, characterized in that The target weight corresponding to each indicator to be evaluated is determined in the following way: Obtaining a target evaluation category to which the target indicator to be evaluated belongs and a preset total weight corresponding to the target evaluation category; the target indicator to be evaluated is selected from the indicators to be evaluated; Obtaining evaluation data of different indicators to be evaluated under the target evaluation category; Determining a first weight of the target indicator to be evaluated according to evaluation data of different indicators to be evaluated under the target evaluation category; The target weight of the target indicator to be evaluated is determined according to the first weight of the target indicator to be evaluated and the preset total weight corresponding to the target evaluation category.

6. The method according to claim 5, characterized in that The evaluation data includes a fuzzy number of at least one evaluation object evaluating the importance of a corresponding indicator to be evaluated; the first weight of the target indicator to be evaluated is determined according to the evaluation data of different indicators to be evaluated under the evaluation category, including: Determine the fuzzy weights of different indicators to be evaluated according to the evaluation data of different indicators to be evaluated under the target evaluation category; According to the fuzzy weights of different indicators to be evaluated, the total fuzzy weight of all indicators to be evaluated under the target evaluation category is determined; A first weight of the target indicator to be evaluated is determined according to the fuzzy weight of the target indicator to be evaluated and the total fuzzy weight of all indicators to be evaluated under the target evaluation category.

7. A credibility assessment device, characterized in that: The device comprises: An acquisition module, used to acquire credibility data of at least one application to be evaluated under different indicators to be evaluated; A selection module, for selecting, for each indicator to be evaluated, characteristic reference data of the indicator to be evaluated from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated; A first determination module is used to determine, for any application to be evaluated, correlation data between the credibility data of the application to be evaluated under the indicator to be evaluated and corresponding feature reference data; The second determination module is used to determine the credibility evaluation value of the application to be evaluated according to the correlation data of the application to be evaluated under different indicators to be evaluated.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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