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

Abstract

The present application relates to a credibility assessment method, apparatus, device, medium, and program product. The credibility assessment method includes: obtaining credibility data of at least one application to be assessed under different indicators to be assessed; for each indicator to be assessed, selecting characteristic reference data of the indicator to be assessed from the credibility data of each application to be assessed under the corresponding indicator to be assessed; for any application to be assessed, determining the correlation data between the credibility data of the application to be assessed under the indicator to be assessed and the corresponding characteristic reference data; and determining the credibility assessment value of the application to be assessed based on the correlation data of the application to be assessed under different indicators to be assessed. Through the above steps, the accuracy of the credibility assessment 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, apparatus, device, medium, and program product. Background Art

[0002] With the rapid development of information technology, software applications are becoming increasingly widespread, and the resulting credibility issues are becoming increasingly prominent. This is particularly true for supply chains, where each link is interconnected and impacts each other. Therefore, when supply chain software reliability issues arise, they impact every link in the supply chain, causing losses for the enterprise.

[0003] Traditionally, software applications are typically evaluated using a threshold. These applications are then evaluated using an evaluation object and their evaluation value is calculated. This value is then compared with the threshold to assess the quality of the software application. While this approach can assess software applications to a certain extent, it still suffers from 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 evaluation indicator and the corresponding feature reference data;

[0009] Determine the credibility of the application under evaluation based on its correlation data under different evaluation indicators.

[0010] In one embodiment, 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 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.

[0011] In one 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 based on 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; and 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 embodiment, 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; based on 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 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.

[0013] In one embodiment, 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 a 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 embodiment, 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 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; and 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 further provides a credibility assessment device, comprising:

[0016] An acquisition module, configured 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 configured 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;

[0019] The second determining module is configured to determine a credibility evaluation value of the application to be evaluated based on correlation data of the application to be evaluated under different evaluation indicators.

[0020] In a third aspect, the present application further provides a computer device comprising 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 evaluation indicator and the corresponding feature reference data;

[0024] Determine the credibility of the application under evaluation based on its correlation data 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, which, when executed by a processor, implements the following steps:

[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 evaluation indicator and the corresponding feature reference data;

[0029] Determine the credibility of the application under evaluation based on its correlation data under different evaluation indicators.

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

[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 evaluation indicator and the corresponding feature reference data;

[0034] Determine the credibility of the application under evaluation based on its correlation data 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 assessment benchmark is constructed between each application to be evaluated, which facilitates accurate and quantifiable evaluation of 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 assessment of the target application to be evaluated are achieved, thereby improving the accuracy of the assessment. 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 following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. 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 any creative work.

[0037] Figure 1 1 is a flow chart of a credibility assessment method according to an embodiment;

[0038] Figure 2 1 is a flow chart of the steps for determining relevance data in one embodiment;

[0039] Figure 3 Schematic diagram of a flow chart of the steps for determining target weights in one embodiment;

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

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

[0042] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] In one embodiment, Figure 1 As shown, a credibility assessment method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through 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 evaluation indicators.

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

[0047] Exemplarily, the evaluation categories of the indicators to be evaluated may include at least one of availability, reliability, security, and maintainability. 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; and 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, historical data analysis 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 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 Perform normalization to obtain the credibility data r ij :

[0051] .

[0052] Optionally, the following formula can be used to calculate the initial credibility data s ij Perform normalization to obtain 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 can be understood as an evaluation benchmark for the indicator to be evaluated, established between the applications to be evaluated. The number of feature reference data is at least one, i.e., there can be one, two, or more feature reference data for the indicator to be evaluated. Optionally, the feature types of different feature reference data for the indicator to be evaluated can be the same or different.

[0056] In one embodiment, characteristic reference data for the indicator to be evaluated can be selected using the positive and negative ideal point method. 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. For example, the maximum credibility data and / or 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 minimum credibility data can be used as the characteristic reference data for the indicator to be evaluated.

[0057] For ease of understanding, ij It represents the credibility data of the i-th application to be evaluated in 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 correlation data between the credibility data of the application to be evaluated under the evaluation indicator and corresponding feature reference data.

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

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

[0067] S140 : Determine a credibility evaluation value of the application to be evaluated based on 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, the higher the comprehensive credibility of the application to be evaluated.

[0069] In an optional embodiment, different feature reference data of the indicator to be evaluated corresponds to different feature types. Based on 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 under the feature type of the application to be evaluated 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.

[0070] The feature reference data may include at least one of maximum credibility data corresponding to a maximum value type and minimum credibility data corresponding to a minimum value type. The target weight may be set by a technician based on needs or experience, determined through extensive experimentation, or otherwise determined, and this application does not impose any limitations thereon.

[0071] For the sake of convenience, the characteristic reference data of a certain application to be evaluated under the jth evaluation indicator 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 first correlation ; 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] For example, the application to be evaluated has a first credibility evaluation value of the corresponding maximum value type. , determined by the following formula:

[0073] ;

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

[0075] For example, 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] For example, the credibility evaluation value of the application to be evaluated under different feature types is 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 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.

[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 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 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 evaluation indicator is r j In order to distinguish them, the credibility data under the evaluation index and the maximum credibility data r j + The target distance between the first target distance ; The credibility data under the evaluation index and the minimum credibility data r j - The target distance between the two 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 distance between the second target , 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 distance between the second target 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 first correlation ; 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 distance to the first target; Indicates the distance to the first target Maximum target distance in ; Indicates the distance to the first target 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. 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 Maximum target distance in ; Indicates the distance to the second target 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. 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 the calculated 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] The indicators to be evaluated may involve multiple evaluation categories, such as availability, reliability, security, and maintainability. The preset total weights corresponding to the target evaluation categories can be set by technicians based on needs or experience, or determined through extensive experiments, and this application does not impose any restrictions on this.

[0108] S320: Obtain evaluation data for 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 used by the evaluation object to evaluate the importance of the corresponding indicator to be evaluated; 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 are shown. Multiple fuzzy words can be pre-set, and fuzzy numbers can be assigned to different fuzzy words. The evaluation object can evaluate the indicator to be evaluated 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 the target indicator to be evaluated based on 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, let's take the indicator "functional accuracy" as an example. The trapezoidal fuzzy numbers assigned by three evaluators for this indicator are: (5, 7, 7, 9), (7, 9, 9, 10), and (5, 7, 7, 9). The composite fuzzy numbers (17 / 3, 23 / 3, 23 / 3, and 28 / 3) can be determined by taking the average. By averaging the values ​​at each digit of the composite fuzzy number, the fuzzy weight is 7.583. For illustration, using the fuzzy weights of 5.667 and 4.917 for other indicators under the target evaluation category, the total fuzzy weight for all indicators under the target evaluation category is 18.167. The first weight of the target indicator can be obtained by calculating the ratio of the fuzzy weight of the target indicator to the total fuzzy weight: 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 embodiments provided herein, a first weight of the target indicator to be evaluated is determined by combining the preset total weight corresponding to the pre-set target evaluation category and the evaluation data of the 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 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 evaluation indicators.

[0126] S420 : 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.

[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 corresponds to different feature types.

[0128] S440 , for any application to be evaluated, perform the following steps: for each feature type, 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.

[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 based on 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.

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

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

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

[0133] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0134] Based on the technical solutions of the above embodiments, this application also provides a verification embodiment. In this verification embodiment, a credibility indicator system is first provided for supply chain software, namely, the supply chain software indicators to be evaluated 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 - Evaluation indicators 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 of evaluation data and target weights

[0139]

[0140] By determining a preset total weight for availability I1 and multiplying the preset total weight by the first weight of each of the aforementioned indicators to be evaluated, the target weight for each of the aforementioned indicators to be evaluated can be obtained. The method for determining the comprehensive fuzzy number, fuzzy weight, and first weight in Table 3 has been described in the above embodiment and will not be repeated here.

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

[0142] ;

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

[0144] For example, 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 the actual situation. After normalization, the credibility data of the three applications to be evaluated under the 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 3 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 equation 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] Illustratively, 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, embodiments of the present application also provide a credibility assessment device for implementing the credibility assessment method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more credibility assessment device embodiments provided below can be found in the limitations of the credibility assessment method described 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 configured to acquire credibility data of at least one application to be evaluated under different evaluation indicators.

[0155] The selection module 520 is configured to select, 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.

[0156] The first determining module 530 is configured 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 determining module 540 is configured to determine a credibility evaluation value of the application to be evaluated based on correlation data of the application to be evaluated under different evaluation indicators.

[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, 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, for performing weighted summation 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, to obtain a credibility evaluation value of the application to be evaluated under the corresponding feature type; a second processing unit, for fusing the credibility evaluation values ​​of the application to be evaluated under different feature types, to obtain a 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 based on 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 based on 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-mentioned credibility assessment device can be implemented in whole or in part through 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 the computer device in the form of software, so that the processor can call and execute the corresponding operations of 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, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via 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 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 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 external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a credibility assessment method. The display unit of the computer device is used to form a visually visible image, and 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 casing, 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0166] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, 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] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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. In particular, any reference to memory, database, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0170] The technical features of the above embodiments can be combined arbitrarily. In order 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 merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended 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; the credibility data is used to characterize the credibility of the application to be evaluated under the corresponding indicators to be evaluated; For each indicator to be evaluated, feature reference data of the indicator to be evaluated is selected from the credibility data of each application to be evaluated under the corresponding indicator to be evaluated; the feature reference data is an evaluation benchmark for the indicator to be evaluated constructed between each application 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; 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 the corresponding feature reference data; the correlation data is used to quantify the degree of correlation between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data; Determining a credibility evaluation value of the application to be evaluated based on correlation data of the application to be evaluated under different evaluation indicators; The step of 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 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, determining correlation data between the credibility data of the indicator to be evaluated and 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; The step of determining the credibility evaluation value of the application to be evaluated based on the correlation data of the application to be evaluated under different evaluation indicators includes: For each feature type, based on the target weight corresponding to each indicator to be evaluated, the 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; 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.

2. The method according to claim 1, characterized in that The step of determining, based on the target distance corresponding to the indicator to be evaluated and the maximum target distance and the minimum target distance under the feature type, correlation data between the credibility data of the indicator to be evaluated and the feature reference data of the corresponding feature type includes: 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; 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; Correlation data between the credibility data of the indicator to be evaluated and feature reference data of the corresponding feature type is determined according to a ratio of the first adjustment distance to the second adjustment distance.

3. The method according to claim 1 or 2, 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 a 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 for different indicators to be evaluated under the target evaluation category; Determining a first weight of the target indicator to be evaluated based on 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.

4. The method according to claim 3, characterized in that The evaluation data includes a fuzzy number used by at least one evaluation object to evaluate the importance of a corresponding indicator to be evaluated; and determining a first weight of the target indicator to be evaluated based on the evaluation data of different indicators to be evaluated under the evaluation category includes: Determining fuzzy weights of different indicators to be evaluated based on the evaluation data of different indicators to be evaluated under the target evaluation category; Determine the total fuzzy weight of all indicators to be evaluated under the target evaluation category according to the fuzzy weights of different indicators to be evaluated; 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.

5. A credibility evaluation device, characterized in that: The device comprises: An acquisition module, configured to acquire credibility data of at least one application to be evaluated under different indicators to be evaluated; the credibility data is used to characterize the credibility of the application to be evaluated under the corresponding indicators to be evaluated; a selection module configured to select, for each indicator to be evaluated, feature 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; the feature reference data being an evaluation benchmark for the indicator to be evaluated constructed between the applications to be evaluated; the number of feature reference data of the indicator to be evaluated being at least one, and different feature reference data of the indicator to be evaluated corresponding to different feature types; A first determination module is configured 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 the corresponding feature reference data; the correlation data is used to quantify the degree of correlation between the credibility data of the application to be evaluated under the indicator to be evaluated and the corresponding feature reference data; A second determining module is configured to determine a credibility evaluation value of the application to be evaluated based on correlation data of the application to be evaluated under different evaluation indicators; The step of 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 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, determining correlation data between the credibility data of the indicator to be evaluated and 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; The step of determining the credibility evaluation value of the application to be evaluated based on the correlation data of the application to be evaluated under different evaluation indicators includes: For each feature type, based on the target weight corresponding to each indicator to be evaluated, the 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; 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.

6. The device according to claim 5, characterized in that The second determining module further includes: A first obtaining unit is configured to obtain a target evaluation category to which a 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; A second acquisition unit is used to obtain evaluation data of different indicators to be evaluated under the target evaluation category; a fourth determining unit, configured to determine a first weight of the target indicator to be evaluated based on evaluation data of different indicators to be evaluated under the target evaluation category; A fifth determining unit is configured to 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.

7. The device according to claim 6, characterized in that The fourth determining unit includes: A second determining subunit is configured to determine fuzzy weights of different indicators to be evaluated according to the evaluation data of the different indicators to be evaluated under the target evaluation category; A third determining subunit is configured to determine the total fuzzy weight of all indicators to be evaluated under the target evaluation category according to the fuzzy weights of different indicators to be evaluated; The fourth determining subunit is configured to determine a first weight of the target indicator to be evaluated 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.

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 method according to any one of claims 1 to 4 is 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 method according to any one of claims 1 to 4 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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