Software system benefit evaluation index data processing method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of software system evaluation, and discloses a software system benefit evaluation index data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: determining each benefit index in each index grade, and calculating the weight of each index grade; third-level index data of the software system to be evaluated are obtained, and a fuzzy relation matrix of third-level indexes is obtained through fuzzy evaluation; and fusing the fuzzy relation matrix with the third-level index weight, the second-level index weight and the first-level index weight in a stepped manner. Fuzzy evaluation is introduced to evaluate the three-level index data of the to-be-evaluated software system, the system evaluation result is obtained based on the weight of each index level, effective evaluation of the software system is achieved, data support is provided for forming software system optimization measures, and therefore the software system operation quality and the software system utilization rate are improved, and the software system evaluation efficiency is improved. The informatization project return on investment is further increased, and the digital transformation and intelligent development of the oil field are assisted.

Description

Technical Field

[0001] This invention relates to the field of software system evaluation technology, specifically a method, apparatus, electronic device, and storage medium for processing data of software system benefit evaluation indicators. Background Technology

[0002] The application software systems in the oilfield sector are diverse, covering the entire oilfield business process, including exploration and development, production operations, and management. The evaluation of these software systems is crucial; their success or failure is a complex interplay of qualitative and quantitative factors, and is a significant issue affecting the company's investment and return on investment.

[0003] Determining the success of a software system requires multi-stage, multi-stakeholder, and multi-level analysis. Due to the continuous and complex nature of software systems, their effectiveness evaluation necessitates the use of different evaluation systems and methods, considering and assessing them from various perspectives. Currently, the oil and gas sector lacks systematic post-deployment assessments of software systems and has not established unified standards and methods for evaluating their effectiveness. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for processing data of software system benefit evaluation indicators, which overcomes the shortcomings of the prior art and can effectively solve the problem in the oilfield field of not being able to effectively process the benefit evaluation indicators of application software systems.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for processing data of software system benefit evaluation indicators, comprising:

[0006] Determine the benefit indicators in each indicator level and calculate the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators.

[0007] Obtain the tertiary indicator data of the software system to be evaluated, and use fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators;

[0008] The fuzzy relation matrix is ​​integrated with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

[0009] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0010] The above-mentioned analytic hierarchy process (AHP) is used to calculate the weights of each indicator level, including:

[0011] Based on the comparative quantitative standards, a comparative judgment matrix is ​​constructed for each level of indicators, and then normalized.

[0012] Perform consistency checks on the matrix data of each comparison judgment matrix;

[0013] After the consistency check passes, the weight of each indicator level is calculated.

[0014] The above-mentioned acquisition of the third-level indicator data of the software system to be evaluated, and the use of fuzzy evaluation to obtain the fuzzy relation matrix of the third-level indicators, includes:

[0015] Obtain the three-level indicator data of the software system to be evaluated, and input them into the trapezoidal membership function model to obtain the corresponding level membership matrix;

[0016] By combining the membership degree level division intervals, the evaluation level membership degree matrix is ​​converted into a fuzzy evaluation matrix;

[0017] Frequency statistics are performed on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

[0018] The above-mentioned fuzzy relation matrix is ​​fused with the weights of the third-level, second-level, and first-level indicators in a stepwise manner to obtain the system evaluation results, including:

[0019] The data in each column of the fuzzy relation matrix is ​​multiplied one-to-one with the weights of the third-level indicators, and all products are summed to obtain the evaluation result matrix of the second-level indicators.

[0020] Multiply the data in each column of the evaluation result matrix of each secondary indicator with the weight of the secondary indicator one-to-one, and sum all the products to obtain the evaluation result matrix of the primary indicator.

[0021] The system evaluation results are obtained by multiplying the data of the primary indicator evaluation result matrix with the weights of the primary indicators one-to-one, and summing all the products.

[0022] The second technical solution of the present invention is achieved through the following measures: a software system benefit evaluation index data processing device, comprising:

[0023] The indicator determination unit determines the various benefit indicators in each indicator level and calculates the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators.

[0024] The fuzzy evaluation unit acquires the tertiary indicator data of the software system to be evaluated and uses fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators.

[0025] The fusion unit integrates the fuzzy relation matrix with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

[0026] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0027] The aforementioned fuzzy evaluation unit includes:

[0028] The first analysis module acquires the three-level indicator data of the software system to be evaluated and inputs it into the trapezoidal distribution membership function model to obtain the corresponding level membership matrix.

[0029] The second analysis module, combined with the membership degree level division interval, converts the evaluation level membership degree matrix into a fuzzy evaluation matrix;

[0030] The third analysis module performs frequency statistics on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

[0031] The third technical solution of the present invention is achieved through the following measures: an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the steps in the software system benefit evaluation index data processing method.

[0032] The fourth technical solution of the present invention is achieved through the following measures: a storage medium, characterized in that the storage medium stores a computer program that can be read by a computer, the computer program being configured to execute the steps in the software system benefit evaluation index data processing method when running.

[0033] This invention determines the levels of each indicator and introduces fuzzy evaluation to evaluate the three-level indicator data of the software system to be evaluated. Then, based on the weight of each indicator level, the system evaluation result is obtained, realizing the effective evaluation of the software system. This provides data support for the formation of software system optimization measures, thereby improving the operating quality and utilization rate of the software system, further increasing the return on investment of information technology projects, and helping the digital transformation and intelligent development of oilfields. Attached Figure Description

[0034] Appendix Figure 1 This is a schematic flowchart of a processing method provided in one embodiment of the present invention.

[0035] Appendix Figure 2 This is a schematic diagram of a weight calculation method provided in one embodiment of the present invention.

[0036] Appendix Figure 3 This is a schematic diagram of a fuzzy evaluation method provided in one embodiment of the present invention.

[0037] Appendix Figure 4 This is a schematic diagram of a processing device provided in one embodiment of the present invention. Detailed Implementation

[0038] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0039] Those skilled in the art will understand that, unless otherwise stated, in the embodiments of this application, "module" or "unit" refers to a computer program or part of a computer program with a predetermined function, which works together with other related parts to achieve a predetermined goal, and can be implemented, wholly or partially, using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0040] In addition, in the embodiments of this application, "multiple" refers to two or more, and "first" and "second" are used to distinguish descriptions and should not be construed as implying relative importance.

[0041] This application provides a method, apparatus, electronic device, and storage medium for processing data on software system benefit evaluation indicators. This method and apparatus for processing software system benefit evaluation indicator data can be integrated into a computer device, which can be a server, a terminal, or other similar device; it can also be executed jointly by a terminal and a server. The above examples should not be construed as limiting this application.

[0042] The aforementioned terminals may include mobile phones, wearable smart devices, tablets, laptops, personal computers (PCs), and in-vehicle computers, etc., and this application does not limit them. This application also does not limit the number of terminal devices.

[0043] The aforementioned server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. This application does not impose any restrictions on this.

[0044] For example, computer equipment determines the benefit indicators in each indicator level and calculates the weight of each indicator level, where the indicator levels include primary, secondary and tertiary indicators; it obtains the tertiary indicator data of the software system to be evaluated and uses fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators; it then integrates the fuzzy relation matrix with the weights of the tertiary, secondary and primary indicators in a stepwise manner to obtain the system evaluation result.

[0045] Based on this, the present application will be further described below with reference to the embodiments and accompanying drawings:

[0046] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0047] Example 1: As shown in the attached document Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for processing data of software system benefit evaluation indicators, including:

[0048] Step S110: Determine the benefit indicators in each indicator level and calculate the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators.

[0049] In this embodiment, the indicator levels can be divided according to the key points of benefit evaluation, and the various benefit indicators covered in each indicator level can also be determined according to the key aspects of benefit evaluation.

[0050] For example, this embodiment can, but is not limited to, conduct a comprehensive evaluation of the software system's benefits from four levels: business, efficiency, technology, and management. Then, corresponding benefit evaluation indicators are determined for each level, and secondary and tertiary indicators are constructed in each primary indicator based on the subordinate relationship of each benefit evaluation indicator. That is, a primary indicator may include multiple secondary indicators, and each secondary indicator may include multiple tertiary indicators.

[0051] In this embodiment, the weights of each indicator level are calculated, that is, the weights are divided according to the indicator level. The sum of the weights of all first-level indicators is 100%, the sum of the weights of all second-level indicators in each first-level indicator is 100%, and the sum of the weights of all third-level indicators in each second-level indicator is 100%. The weight division is determined according to the value and relative importance of each benefit evaluation indicator in the whole.

[0052] Step S120: Obtain the three-level indicator data of the software system to be evaluated, and use fuzzy evaluation to obtain the fuzzy relation matrix of the three-level indicators.

[0053] This embodiment introduces fuzzy evaluation, which comprehensively considers various evaluation factors affecting the software system. Based on the importance of each factor and the evaluation results, the original qualitative evaluation is quantified. In this embodiment, the evaluation factors used in fuzzy evaluation are the various tertiary indicator data of the software system to be evaluated.

[0054] Step S130: The fuzzy relation matrix is ​​fused with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

[0055] In this step, the fuzzy relation matrix is ​​integrated with the weights of the third-level, second-level, and first-level indicators in a stepwise manner. Specifically, the fuzzy relation matrix is ​​merged with the weights of the third-level indicators by multiplying the data in each column of the fuzzy relation matrix one-to-one with the weights of the third-level indicators and summing all the products to obtain the second-level indicator evaluation result matrix. Then, the data in each column of each second-level indicator evaluation result matrix is ​​multiplied one-to-one with the weights of the second-level indicators and summed to obtain the first-level indicator evaluation result matrix. Finally, the data in the first-level indicator evaluation result matrix is ​​multiplied one-to-one with the weights of the first-level indicators and summed to obtain the system evaluation result.

[0056] This invention discloses a data processing method for software system benefit evaluation indicators. It determines the levels of each indicator and introduces fuzzy evaluation to assess the three-level indicator data of the software system to be evaluated. Then, based on the weights of each indicator level, it obtains the system evaluation result, achieving effective evaluation of the software system. This provides data support for formulating software system optimization measures, thereby improving the operational quality and utilization rate of the software system, further increasing the return on investment of information technology projects, and contributing to the digital transformation and intelligent development of oilfields.

[0057] Example 2: As shown in the attached document Figure 2 As shown, the embodiments of the present invention are further optimizations of the above embodiments, wherein the weights of each indicator level are calculated using the analytic hierarchy process (AHP), including:

[0058] Step S210: Based on the comparative quantification standard, construct a comparison judgment matrix for each level of indicator and normalize it.

[0059] The comparison quantification criteria in this embodiment are shown in Table 1:

[0060] Table 1 Comparative Quantitative Standards

[0061] Factor i compared to factor j Quantized value Equally important 1 Slightly important 3 Stronger and more important 5 Strongly important 7 Extremely important 9 The median value between two adjacent judgments 2，4，6，8 reciprocal <![CDATA[a ij =l / a ji ]]>

[0062] Among them, a ij This indicates the degree of importance of i to j, compared with a. ji They are in a reciprocal relationship.

[0063] Step S220: Perform consistency verification on the matrix data of each comparison judgment matrix.

[0064] In this embodiment, consistency verification is used to verify the reliability of the data. It is necessary to determine that both the matrix and the hierarchical overall sorting result have sufficient consistency, that is, the CR must be less than 0.1.

[0065] The consistency index CI, random consistency index RI, and consistency ratio CR are shown below:

[0066]

[0067] Step S230: After the consistency verification is passed, calculate the weight of each indicator level.

[0068] Methods for calculating weights include: the arithmetic mean method, which normalizes the matrix column-wise, adds the matrices row-wise, and then divides by the matrix order; the geometric mean method, which multiplies the matrices row-wise, takes the square root, and then normalizes the result; and the eigenvalue method, which finds the eigenvector corresponding to the largest eigenvalue, normalizes it, and then gives the weight. The specific method chosen depends on the requirements.

[0069] The above steps are the general steps of the Analytic Hierarchy Process (AHP). AHP decomposes a decision problem into different hierarchical structures according to the overall goal, evaluation criteria, and alternative solutions. It then calculates the priority weight of each element at each level relative to a certain element at the level above it, and finally derives the final weight of each alternative solution relative to the overall goal. This embodiment starts with three levels of indicators and analyzes each level progressively to obtain the weight of each indicator level.

[0070] For example, if the levels of each indicator are as shown in Table 2, then the weights of each indicator level calculated using the analytic hierarchy process are shown in Tables 3, 4, and 5, respectively.

[0071] Table 2. Combination of Indicator Levels

[0072]

[0073] Table 3.1 Results of the weights of the three-level indicators

[0074]

[0075] Table 3.2 Results of the weights of the three-level indicators

[0076]

[0077] Table 3.3 Results of the weights of the three-level indicators

[0078]

[0079] Table 4. Results of Secondary Indicator Weights

[0080]

[0081] Table 5: Results of Primary Indicator Weights

[0082]

[0083] Example 3: As shown in the attached document Figure 3As shown, this embodiment of the invention is a further optimization of the above embodiment, wherein obtaining the three-level indicator data of the software system to be evaluated and using fuzzy evaluation to obtain the fuzzy relation matrix of the three-level indicators includes:

[0084] Step S310: Obtain the three-level indicator data of the software system to be evaluated, and input them into the trapezoidal distribution membership function model to obtain the corresponding level membership matrix;

[0085] The trapezoidal membership function model is shown below:

[0086] (1) Difference (V1):

[0087]

[0088] (2) Poor (V2):

[0089]

[0090] (3) General (V3):

[0091]

[0092] (4) Good (V4):

[0093]

[0094] (5) Excellent (V5):

[0095]

[0096] It should be noted that the tertiary indicator data corresponding to all tertiary indicators belonging to the same secondary indicator constitute an evaluation level membership matrix R, which is shown below:

[0097]

[0098] Where, μ ij Let be the membership degree of the i-th evaluation factor at the j-th evaluation level.

[0099] Step S320: Combine the membership degree level division intervals with the evaluation level membership degree matrix to convert it into a fuzzy evaluation matrix.

[0100] In this step, the membership level division interval is divided according to the actual situation.

[0101] Step S330: Perform frequency statistics on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

[0102] In this step, each row of the fuzzy relation matrix represents a three-level indicator, each column represents an evaluation level, and each element in the matrix represents the frequency of the indicator at that evaluation level. The frequency can be the proportion of the number of times the indicator is evaluated at that evaluation level to the total number of times the indicator is evaluated at all evaluation levels. Then, the sum of each row of the matrix is ​​1.

[0103] Example 4: This example starts with four primary indicators: business, efficiency, technology, and management. These are further broken down into 15 secondary indicators and 65 tertiary indicators. Tertiary indicator data for the software system to be evaluated are obtained and processed, as detailed below:

[0104] (1) Determine the weight of each indicator level and obtain the three-level indicator data of the downhole operation production management information system, as shown in Table 6:

[0105] Table 6.1 Data Acquired by the Downhole Operation Production Management Information System

[0106]

[0107] Table 6.2 Data Acquired by the Downhole Operation Production Management Information System (Table 2)

[0108]

[0109] (2) Substituting it into the trapezoidal membership function model, we obtain the corresponding membership degree matrix, as shown in Table 7:

[0110] Table 7.1 Membership Matrix

[0111]

[0112] Table 7.2 Membership Matrix

[0113]

[0114] (3) Combining the membership degree level division interval, the evaluation level membership degree matrix is ​​converted into a fuzzy evaluation matrix. Frequency statistics are performed on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix. This fuzzy relation matrix is ​​relatively large, so the fuzzy relation matrix corresponding to the first-level indicator "benefit level" is taken as an example, as shown in Table 8:

[0115] Table 8. Fuzzy Relationship Matrix Corresponding to Benefit Level

[0116]

[0117] In Table 8, the middle matrix is ​​the fuzzy evaluation matrix, and the left matrix is ​​the fuzzy relation matrix.

[0118] (4) The fuzzy relation matrix is ​​combined with the weights of the third-level indicators to obtain the evaluation result matrix of the second-level indicators, as shown in Table 9:

[0119] Table 9. Evaluation Results Matrix of Secondary Indicators

[0120]

[0121] Specifically, for example:

[0122] The difference V1 is 0.02348 = 0.2 * 0.1174;

[0123] The difference V2 is 0.19532 = 0.2 * 0.3099 + 0.6 * 0.0657 + 0.8 * 0.1174.

[0124] (5) Using the same method as in step (4), merge the secondary indicator evaluation result matrix with the secondary indicator weights to obtain the primary indicator evaluation result matrix. Then merge the primary indicator evaluation result matrix with the primary indicator weights to obtain the system evaluation result, thus completing the indicator data processing. Furthermore, system evaluation conditions can be introduced, and the system evaluation results can be brought into the system evaluation conditions to obtain the final system evaluation content. The system evaluation conditions can be set based on a threshold.

[0125] Example 4: As shown in the appendix Figure 4 As shown in the figure, an embodiment of the present invention discloses a software system benefit evaluation index data processing device, comprising:

[0126] The indicator determination unit determines the various benefit indicators in each indicator level and calculates the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators.

[0127] The fuzzy evaluation unit acquires the tertiary indicator data of the software system to be evaluated and uses fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators.

[0128] The fusion unit integrates the fuzzy relation matrix with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

[0129] The fuzzy evaluation unit includes:

[0130] The first analysis module acquires the three-level indicator data of the software system to be evaluated and inputs it into the trapezoidal distribution membership function model to obtain the corresponding level membership matrix.

[0131] The second analysis module, combined with the membership degree level division interval, converts the evaluation level membership degree matrix into a fuzzy evaluation matrix;

[0132] The third analysis module performs frequency statistics on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

[0133] Example 5: This embodiment of the invention discloses a storage medium storing a computer program that can be read by a computer. The computer program is configured to execute a software system benefit evaluation index data processing method at runtime.

[0134] The aforementioned storage media may include, but are not limited to, USB flash drives, read-only memory, portable hard drives, magnetic disks, optical disks, and other media capable of storing computer programs.

[0135] Example 6: This embodiment of the invention discloses an electronic device, including a processor and a memory. The memory stores a computer program, which is loaded and executed by the processor to implement a software system benefit evaluation index data processing method.

[0136] The processor described above can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. It can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The memory can include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, portable hard drives, magnetic disks, or optical disks.

[0137] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] The above content is only a specific embodiment of this application, which has strong adaptability and implementation effect. However, the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. Therefore, equivalent changes made in accordance with the claims of this application are still within the scope of this application.

Claims

1. A method for processing data of software system benefit evaluation indicators, characterized in that, include: Determine the benefit indicators in each indicator level and calculate the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators. Obtain the tertiary indicator data of the software system to be evaluated, and use fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators; The fuzzy relation matrix is ​​integrated with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

2. The method for processing software system benefit evaluation index data according to claim 1, characterized in that, The weights of each indicator level are calculated using the analytic hierarchy process (AHP), including: Based on the comparative quantitative standards, a comparative judgment matrix is ​​constructed for each level of indicators, and then normalized. Perform consistency checks on the matrix data of each comparison judgment matrix; After the consistency check passes, the weight of each indicator level is calculated.

3. The method for processing software system benefit evaluation index data according to claim 1 or 2, characterized in that, Obtain the tertiary indicator data of the software system to be evaluated, and use fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators, including: Obtain the three-level indicator data of the software system to be evaluated, and input them into the trapezoidal membership function model to obtain the corresponding level membership matrix; By combining the membership degree level division intervals, the evaluation level membership degree matrix is ​​converted into a fuzzy evaluation matrix; Frequency statistics are performed on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

4. The method for processing software system benefit evaluation index data according to claim 1, 2, or 3, characterized in that, The fuzzy relation matrix is ​​fused with the weights of the third-level, second-level, and first-level indicators in a stepwise manner to obtain the system evaluation results, including: The data in each column of the fuzzy relation matrix is ​​multiplied one-to-one with the weights of the third-level indicators, and all products are summed to obtain the evaluation result matrix of the second-level indicators. Multiply the data in each column of the evaluation result matrix of each secondary indicator with the weight of the secondary indicator one-to-one, and sum all the products to obtain the evaluation result matrix of the primary indicator. The system evaluation results are obtained by multiplying the data of the primary indicator evaluation result matrix with the weights of the primary indicators one-to-one, and summing all the products.

5. A software system benefit evaluation index data processing device applying the method described in any one of claims 1 to 4, characterized in that, include: The indicator determination unit determines the various benefit indicators in each indicator level and calculates the weight of each indicator level, where the indicator levels include primary indicators, secondary indicators and tertiary indicators. The fuzzy evaluation unit acquires the tertiary indicator data of the software system to be evaluated and uses fuzzy evaluation to obtain the fuzzy relation matrix of the tertiary indicators. The fusion unit integrates the fuzzy relation matrix with the weights of the third-level indicators, the second-level indicators, and the first-level indicators in a stepwise manner to obtain the system evaluation results.

6. The software system benefit evaluation index data processing device according to claim 5, characterized in that, Fuzzy evaluation units include: The first analysis module acquires the three-level indicator data of the software system to be evaluated and inputs it into the trapezoidal distribution membership function model to obtain the corresponding level membership matrix. The second analysis module, combined with the membership degree level division interval, converts the evaluation level membership degree matrix into a fuzzy evaluation matrix; The third analysis module performs frequency statistics on each evaluation level in the fuzzy evaluation matrix to obtain the corresponding fuzzy relation matrix.

7. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which is loaded and executed by the processor to implement the steps in the software system benefit evaluation index data processing method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the steps in the software system benefit evaluation index data processing method as described in any one of claims 1 to 5 when it runs.

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