Comprehensive product type selection evaluation method based on autonomy of information system

Abstract

The invention discloses a comprehensive product type selection evaluation method based on autonomy of an information system, and relates to the technical field of autonomy of the information system, and the method comprises the steps: carrying out the all-directional and multi-angle evaluation of candidate products through setting a plurality of dimensions, including performance, safety and technical suitability; specific characteristic parameters are subdivided under each dimension, in the performance aspect, such as processing speed, response time and resource occupancy rate, in the security aspect, such as encryption, authentication, authorization, auditing and vulnerability management, in the technical suitability aspect, such as compatibility with the system and integration difficulty. In addition, big data and Internet acquisition technologies are adopted to obtain feedback records of the user on the information system product, and bid evaluation of the characteristic parameters is determined according to the feedback records; meanwhile, by calculating the distance from the characteristic parameter corresponding to each quality attribute of each candidate product to bid evaluation and dividing a characteristic parameter evaluation interval, powerful support is provided for product model selection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information system autonomy, and particularly relates to a comprehensive product selection evaluation method based on information system autonomy. BACKGROUND

[0002] In the information age, enterprises increasingly rely on information systems, and the stability, efficiency and security of information systems are directly related to the business operation and market competitiveness of enterprises. With the continuous progress of technology, numerous information system products have emerged in the market, each with its own characteristics and different functions, providing enterprises with rich selection space. However, in the face of so many product options, how to scientifically and objectively evaluate and select the most suitable product for enterprise needs has become a problem to be solved.

[0003] Traditional product selection evaluation methods often focus on single-dimensional considerations such as function satisfaction and price advantage, while ignoring key factors such as product compatibility with existing systems, integration difficulty and security features. These one-sided evaluation methods not only may lead to selection errors, but also may pose potential risks to the information security and business continuity of enterprises. In order to overcome the shortcomings of existing evaluation methods, the present application proposes a comprehensive product selection evaluation method based on information system autonomy. The method aims to develop a comprehensive and scientific evaluation standard, focusing on technical adaptability, performance and security, to conduct a comprehensive and in-depth evaluation of products. SUMMARY

[0004] The present application aims to provide a comprehensive product selection evaluation method based on information system autonomy to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: a comprehensive product selection evaluation method based on information system autonomy, the evaluation method comprising: Step S1: collecting all information system product test records through the Internet, wherein each test record includes a test environment, various quality attributes of the product and various characteristic parameters corresponding to the various quality attributes; Step S2: obtaining user feedback records for information system products through big data, and screening the feedback records to obtain evaluation of each characteristic parameter; Step S3: calculating the distance from the evaluation of each characteristic parameter corresponding to the various quality attributes of all information system products to the evaluation under each test environment according to the evaluation of the characteristic parameters; Step S4: screening the distance from the evaluation of the same characteristic parameter of all information system products to the evaluation under each test environment, and dividing the characteristic parameter evaluation interval; Step S5: deploying the test environment, testing the candidate information system product, and recording the characteristic parameters corresponding to each quality attribute; Step S6: scoring the characteristic parameters corresponding to each quality attribute of the same candidate information system product in different test environments according to the characteristic parameter score interval, and scoring each quality attribute of the information system product according to the characteristic parameter score; Step S7: calculating the score of the same candidate information system product in different test environments according to the quality attribute score; Step S8: comparing the scores of different candidate information system products in the same test environment, ranking all candidate information system products in the same test environment, and calculating the comprehensive score of all information system products to form a score table.

[0006] Further, the quality attributes of the information system product in step S1 include performance, security, and technical adaptability; the characteristic parameters corresponding to the performance attribute include processing speed, response time, and resource occupancy; the characteristic parameters corresponding to the security attribute include encryption, authentication, authorization, audit, and vulnerability management; and the characteristic parameters corresponding to the technical adaptability include compatibility and integration difficulty of the information system product and the system; wherein the values of the characteristic parameters are used as the evaluation standard of each quality attribute of the information system product, and the closer the value is to the ideal threshold, the better the quality attribute.

[0007] Further, step S2 obtains the feedback records of the users on the information system product through big data, filters the feedback records, and the process of obtaining the evaluation standard of each characteristic parameter in the test record includes: Step S2-1: obtaining the feedback records of the users on the information system product through big data, wherein each feedback record contains a test environment in the test record, each quality attribute of the product, each characteristic parameter corresponding to each quality attribute, and a parameter value corresponding to each characteristic parameter; Step S2-2: counting the parameter values corresponding to each characteristic parameter of all products in each test environment in the feedback record, and recording the number of occurrences of each parameter value corresponding to each characteristic parameter; Step S2-3: counting the parameter value with the most occurrences, and taking the parameter value as the evaluation standard of each characteristic parameter in the test record.

[0008] In the above steps, because the quality attributes of the information system product in actual use are different from the quality attributes in the laboratory test environment, and the quality attributes of the information system product in different use environments are also different, the feedback record of the user for the information system product is obtained, wherein the feedback record includes a use environment, quality attributes of the product, feature parameters corresponding to the quality attributes, and parameter values corresponding to the feature parameters; for the same use environment, the parameter values of the feature parameters corresponding to the quality attributes of the same information system product may also be different, in order to avoid errors, the number of occurrences of the parameter values of the feature parameters is counted, and the parameter value with the most occurrences is taken as the evaluation threshold, where the evaluation threshold refers to the threshold for evaluating the feature parameters, so that the influence of errors can be avoided, and the evaluation threshold is more representative.

[0009] Further, step S3 calculates the distance of each feature parameter corresponding to the quality attributes of all information system products in each test environment to the evaluation threshold according to the evaluation threshold of the feature parameter, and the calculation formula is as follows: ; Wherein, d k is the distance of the kth feature parameter to the evaluation threshold, ┌┐ is the integer symbol representing the minimum integer greater than or equal to d k , T ijk is the kth feature parameter corresponding to the jth quality attribute of the ith information system product, and B ijk is the evaluation threshold corresponding to the feature parameter T ijk . In the above steps, the distance of each feature parameter corresponding to the quality attributes of each information system product in the same test environment to the evaluation threshold is calculated, wherein the evaluation threshold refers to the evaluation threshold obtained by screening in step S2, and for different test environments, the evaluation threshold of the feature parameter corresponding to the quality attributes of the same product is also different, which lays the foundation for subsequent comprehensive scoring of the candidate information system product.

[0010] Further, step S4 screens the distance of the same feature parameter of all information system products in each test environment to the evaluation threshold, and divides the feature parameter evaluation interval; Wherein, the process of dividing the feature parameter evaluation interval includes: Step S4-1: comparing the distance of the same feature parameter of all products in each test environment to the evaluation threshold by a comparator, and screening out the maximum distance d max and the minimum distance d min ; Step S4-2: according to the maximum distance d max and the minimum distance d min, establish interval [d min ,d max ] interval total length L = d max -d min , take L any one of the factors n except 1, divide the interval into n equal parts, the formula is as follows: ; Wherein, t is the length of each interval; Step S4-3: set the total score S, mark the score for n intervals, divide the characteristic parameter evaluation interval, [d min ,d min +t) corresponding score S / n points, [d min +t,d min +2t) corresponding score 2S / n points, …, [d min +(n-1)t,d max ] corresponding score S points, wherein S is an integer multiple of n.

[0011] In the above steps, the distance of the same characteristic parameter of all products in the same test environment to the evaluation is screened, the maximum distance and the minimum distance are screened out, here, for example, a system response time threshold is set, which is the optimal value of the system response time, then the response time of the Android system and the Apple system to the same instruction is recorded, the distance of the two response times to the threshold response time is calculated, the shorter the distance, the better the system; In the above steps, the interval composed of the maximum and minimum distances is selected to divide the characteristic parameter evaluation interval, in order to make the interval more refined and avoid invalid interval, the invalid interval here refers to the interval without any information system product corresponding to the characteristic parameter value; The interval is divided into n equal parts in order to allocate corresponding scores to each interval, score the candidate information system products according to the scoring standard, and the length of each interval is t.

[0012] Further, in step S5, the test environment is deployed, and the candidate information system products are tested. The test records of the candidate information system products include: in different test environments, the characteristic parameters corresponding to each quality attribute of the same information system product; in the same test environment, the characteristic parameters corresponding to each quality attribute of different information system products. In the above step, the deployed test environment is arranged according to the test environment in the collected test record, the simulation test environment is further used to perform performance test, security test and technical adaptability test on the candidate information system product, so that the characteristic parameter score interval is also applicable to the candidate information system product; in the above step, the characteristic parameters corresponding to each quality attribute of the same information system product under different test environments are recorded, which is used to lay a foundation for subsequent comprehensive scoring of the same candidate information system product; in the above step, the characteristic parameters corresponding to each quality attribute of different information system products under the same test environment are recorded, which is used to subsequently screen out the product with the best effect under the same test environment.

[0013] Further, the process of scoring each quality attribute of the candidate information system product in step S6 includes: Step S6-1: scoring each characteristic parameter corresponding to each quality attribute of the same information system product under different test environments according to the characteristic parameter evaluation interval; Step S6-2: calculating the score of each quality attribute of the same information system product under different test environments according to the characteristic parameter score, according to the following formula: ; Wherein, B i is the score of the i-th quality attribute of the information system product; W j is the weight value corresponding to the j-th characteristic parameter; A ij is the score of the j-th characteristic parameter corresponding to the i-th quality attribute.

[0014] For the above steps, for example, the characteristic parameters corresponding to the performance attribute of the Android system include the response time, processing speed and resource occupancy rate, etc. According to the score interval, the characteristic parameters are scored, such as response time 90 points, processing speed 80 points, and resource occupancy rate 85 points. The weighted average of the three scores is the performance attribute score of the Android system of the Android system.

[0015] Further, the comprehensive score calculation formula of the same candidate information system product under different test environments in step S7 is: ; Wherein, f x is the score of the candidate information system product x; W xi is the weight value corresponding to the i-th quality attribute score of the candidate information system product x; B xi is the i-th quality attribute score of the candidate information system product x. In the above steps, the comprehensive score of the same candidate information system product in different test environments can be calculated by distance. For example, in a test environment, the performance attribute of the Android system is 85 points, the security attribute is 90 points, and the technical adaptability attribute is 95 points. The weighted average of the three quality attributes can obtain the score of the Android system in the test environment.

[0016] Further, the process of forming the score table in step S8 includes: Step S8-1: comparing the scores of different candidate information system products in the same test environment by the comparator, and ranking them in order from high to low; Step S8-2: calculating the comprehensive score of the candidate information system product according to the score and ranking of the candidate information system in the same test environment, and the calculation formula is as follows: ; Wherein, F x is the comprehensive score of the candidate information system product x; n i is the ranking of the candidate information system product x in the test environment i; f ix is the score of the candidate information system product x in the test environment i; Step S8-3: ranking the candidate information system product according to the comprehensive score of the product, and forming a score table, the content of each row of the score table includes ranking, the name of the candidate information system product and the comprehensive score; In the above steps, for example, there are three test environments, the score ranking of the Android system in the test environment one is 1, the score ranking in the test environment two is 3, and the score ranking in the test environment three is 4. The reciprocal of the ranking of the Android system in the three test environments is multiplied by the score and accumulated, thereby obtaining the comprehensive score of the Android system, wherein the reciprocal of the ranking corresponds to the weight of the score of the Android system in each test environment.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention evaluates candidate products comprehensively and from multiple perspectives by setting multiple dimensions, including performance, security, and technical adaptability. Each dimension is further subdivided into specific characteristic parameters. For performance, these include processing speed, response time, and resource utilization; for security, they include encryption, authentication, authorization, auditing, and vulnerability management; and for technical adaptability, they include system compatibility and integration difficulty. This invention also utilizes big data and internet data collection technologies to obtain user feedback records on information system products and determines the evaluation criteria for characteristic parameters accordingly. This approach avoids the influence of subjective judgment in traditional evaluation methods and improves the objectivity of the evaluation. Simultaneously, by calculating the distance from the characteristic parameters corresponding to each quality attribute of each candidate product to the evaluation criteria and dividing the characteristic parameter evaluation intervals, the merits and demerits of each product can be evaluated more accurately, providing strong support for product selection. Furthermore, this invention deploys a testing environment to test candidate products and records the characteristic parameters corresponding to each quality attribute. Based on different testing environments, it achieves a comprehensive evaluation of candidate products and ranks them according to the evaluation results. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a comprehensive product selection and evaluation method based on information system autonomy, according to the present invention. Detailed Implementation

[0019] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] Example: Figure 1 As shown, this invention provides a technical solution: a comprehensive product selection and evaluation method based on information system autonomy. The evaluation method includes... Step S1: Collect test records of all information system products via the Internet. Each test record includes a test environment, various quality attributes of the product, and various characteristic parameters corresponding to each quality attribute. In step S1, the quality attributes of the information system product include performance, security, and technical compatibility; the characteristic parameters corresponding to performance include processing speed, response time, and resource utilization; the characteristic parameters corresponding to security include encryption, authentication, authorization, auditing, and vulnerability management; and the characteristic parameters corresponding to technical compatibility include the compatibility between the information system product and the system, and the difficulty of integration.

[0021] In the embodiment of the present application, by using the Internet crawler technology, all information system product test records are collected by automatically traversing major information system product test platforms, manufacturer official websites and third-party evaluation agencies; the collected test records need to include test environment description (such as operating system version, hardware configuration, etc.), quality attributes and each characteristic parameter corresponding to each quality attribute in detail; wherein, the quality attributes include performance for evaluating the processing speed, response time and resource occupancy rate of the information system product, security for investigating the encryption capability, authentication mechanism, authorization, audit and vulnerability management, and performance adaptability for evaluating the compatibility of the information system product with the existing system and the integration difficulty.

[0022] Step S2: obtaining the evaluation of each characteristic parameter in the test record by acquiring the feedback records of the information system product from big data, and screening the feedback records; In step S2, the process of obtaining the evaluation of the characteristic parameter includes: Step S2-1: obtaining the feedback records of the information system product from big data, wherein each feedback record includes a test environment in the test record, each quality attribute of the product, each characteristic parameter corresponding to each quality attribute and the parameter value corresponding to each characteristic parameter; Step S2-2: counting the parameter values corresponding to each characteristic parameter of all products under each test environment in the feedback records, and recording the number of occurrences of each parameter value corresponding to each characteristic parameter; Step S2-3: counting the parameter value with the most occurrences, and taking the parameter value as the evaluation of each characteristic parameter in the test record.

[0023] In the embodiment of the present application, by using big data, the feedback records of the information system product from users are collected from channels such as social media, product forums and online evaluation systems. These feedback records include the use environment of the product and the characteristic parameters corresponding to each quality attribute mentioned by the user, the characteristic parameters corresponding to performance include processing speed, response time and resource occupancy rate, the characteristic parameters corresponding to security include encryption, authentication, authorization, audit and vulnerability management, and the characteristic parameters corresponding to technical adaptability include compatibility with existing systems and integration difficulty. The characteristic parameter values expected by the user are set as the expected values of the characteristic parameters; and the expected values of the characteristic parameters are screened to determine the evaluation of each characteristic parameter.

[0024] Step S3: calculating the distance from the evaluation of each characteristic parameter corresponding to each quality attribute of all information system products under each test environment to the evaluation according to the evaluation of the characteristic parameter; In step S3, the distance from the evaluation of each characteristic parameter corresponding to each quality attribute of all information system products under each test environment to the evaluation is calculated, and the calculation formula is as follows: ; wherein d k is the distance of the kth characteristic parameter to the evaluation mark, ┌ ┐ is the integral symbol representing the minimum integer greater than or equal to d k T ijk is the kth characteristic parameter corresponding to the jth quality attribute of the ith product, B ijk is the evaluation mark corresponding to the characteristic parameter T ijk .

[0025] In the embodiment of the present application, for each test environment, all information system products, according to the characteristic parameter values corresponding to each quality attribute and the determined evaluation mark, the distance of each characteristic parameter to the evaluation mark is calculated according to the above formula.

[0026] Step S4: screening the distance of the same characteristic parameter of all information system products under each test environment to the evaluation mark, and dividing the characteristic parameter evaluation interval; wherein the process of dividing the characteristic parameter evaluation interval in step S4 includes: Step S4-1: comparing the distance of the same characteristic parameter of all products under each test environment to the evaluation mark through a comparator, and screening the maximum distance d max and the minimum distance d min ; Step S4-2: according to the maximum distance d max and the minimum distance d min , establishing the interval [d min , d max ], the total length of the interval is L=d max -d min , taking any one common factor n of L except 1, dividing the interval into n equal parts, and the calculation formula is as follows: ; wherein t is the length of each interval; Step S4-3: setting the total score as S, marking the score for n intervals, dividing the characteristic parameter evaluation interval, and the score corresponding to [d min , d min +t) is S / n points, the score corresponding to [d min +t, d min +2t) is 2S / n points, …, the score corresponding to [d min +(n-1)t, d max ] is S points, wherein S is an integer multiple of n.

[0027] In the embodiment of the present application, the maximum distance and the minimum distance are screened out by the comparator, the interval is established according to the maximum distance and the minimum distance, the interval is divided into ten equal parts and each interval is marked with a corresponding score, and the specific division is as follows: [d min ,d min +t) is 10 points, [d min +t,d min +2t) is 20 points, [d min +2t,d min +3t) is 30 points, [d min +3t,d min +4t) is 40 points, [d min +4t,d min +5t) is 50 points, [d min +5t,d min +6t) is 60 points, [d min +6t,d min +7t) is 70 points, [d min +7t,d min +8t) is 80 points, [d min +8t,d min +9t) is 90 points, [d min +9t,d max ] is 100 points.

[0028] Step S5: deploying the test environment, testing the candidate information system product, and recording each characteristic parameter corresponding to each quality attribute; In step S5, the test environment is deployed, and the candidate information system product is tested, and the test record of the candidate information system product includes: each characteristic parameter corresponding to each quality attribute of the same information system product in different test environments; and each characteristic parameter corresponding to each quality attribute of different information system products in the same test environment.

[0029] In the embodiment of the present application, all test environments are built according to the test record to simulate real user usage scenarios; the same information system product is tested in different test environments, and each characteristic parameter corresponding to each quality attribute of the same information system product in different test environments is recorded; different information system products are tested in the same test environment, and the characteristic parameters corresponding to each quality attribute of the different information system products in the same test environment are recorded and compared to identify the performance, security and technical adaptability differences of different products in the same environment.

[0030] Step S6: Based on the feature parameter scoring range, score the feature parameters corresponding to each quality attribute of the same candidate information system product under different test environments, and score the quality attributes of the information system product based on the feature parameter scores; The process of scoring the various quality attributes of the candidate information system products in step S6 includes: Step S6-1: Based on the feature parameter evaluation range, score the feature parameters corresponding to the various quality attributes of the same information system product under different test environments. Step S6-2: Based on the score of the aforementioned feature parameters, calculate the scores of various quality attributes of the same information system product under different test environments, according to the following formula: ; Among them, B i Score the i-th quality attribute of the information system product; W j A is the weight corresponding to the j-th feature parameter; ij The score is given for the j-th feature parameter corresponding to the i-th quality attribute.

[0031] In an embodiment of the present invention, taking the evaluation of a quality attribute of an information system product as an example, it is assumed that the quality attribute includes three characteristic parameters: failure rate, recovery time, and availability. First, the characteristic parameters are scored under different test environments according to the evaluation range of the characteristic parameters. Then, the weight of each characteristic parameter is determined according to the expert scores, such as a failure rate weight of 0.5, a recovery time weight of 0.3, and an availability weight of 0.2. Finally, the quality attribute score is calculated according to the quality attribute scoring formula.

[0032] Step S7: Calculate the scores of the same candidate information system products under different test environments based on the quality attribute scores; The formula for calculating the score of the same candidate information system product under different test environments in step S7 is as follows: ; Among them, f x Scoring of candidate information system product x; W xi B is the weight corresponding to the score of the i-th quality attribute of the candidate information system product x; xi Score the i-th quality attribute of candidate information system product x.

[0033] In an embodiment of the present invention, there are 5 candidate versions of the product, which need to be tested in 3 different testing environments. According to the above calculation formula, the scores of the 5 candidate products in the three different testing environments are calculated; for example, in testing environment 1, the score of candidate product A is f. A =1 / 3*(0.3´BA1 +0.25´B A2 +0.2´B A3 ), where are the safety, performance and technical compatibility scores of product A under the test environment.

[0034] Step S8: Compare the scores of different candidate information system products under the same test environment, rank all candidate information system products under the same test environment, calculate the comprehensive score of all information system products, and form a score table; The process of forming the scoring table in step S8 includes: Step S8-1: Using a comparator, compare the scores of different candidate information system products under the same test environment and rank them in descending order; Step S8-2: Calculate the comprehensive score of the candidate information system products based on their scores and rankings under the same testing environment. The calculation formula is as follows: ; Among them, F x The overall score for candidate information system product x; n i Rank candidate information system product x in test environment i; ix The score of the candidate information system product x in test environment i; Step S8-3: Rank the candidate information system products according to their comprehensive scores to form a scoring table. Each row of the scoring table includes the ranking, the name of the candidate information system product, and the comprehensive score.

[0035] In an embodiment of the present invention, in test environment one, candidate product A is ranked according to the score, and candidate product A ranks first with a score of 90; in test environment two, candidate product A ranks third with a score of 80; in test environment three, candidate product A ranks second with a score of 85; according to the above calculation formula, the comprehensive score of candidate product A is calculated as (1 / 1 × 90) + (1 / 3 × 80) + (1 / 2 × 85) = 90 + 26.67 + 42.5 = 159.17; the same calculation is performed on all candidate products to obtain the comprehensive score of each candidate product, and the candidate products are ranked according to the comprehensive score to form a score table, as shown below: Ranking Candidate Information System Product Name Comprehensive Score 1. Candidate Product A 159.17 2. Candidate Product B: 145.83 3 Candidate Product C 140.00 4. Candidate Product D 130.00 5 Candidate Product E 115.00 Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive product selection evaluation method based on information system autonomy, characterized in that: The evaluation method comprises: Step S1: collecting test records of all information system products through the Internet, wherein each test record comprises a test environment, quality attributes of the product, and characteristic parameters corresponding to the quality attributes; Step S2: obtaining feedback records of users on the information system products through big data, screening the feedback records, and obtaining evaluation marks of the characteristic parameters in the test records; Step S3: calculating distances from the characteristic parameters corresponding to the quality attributes of all information system products in each test environment to the evaluation marks according to the evaluation marks of the characteristic parameters; Step S4: screening the distances from the same characteristic parameters of all information system products in each test environment to the evaluation marks, and dividing the characteristic parameter evaluation intervals; Step S5: deploying the test environment, testing the candidate information system products, and recording the characteristic parameters corresponding to the quality attributes; Step S6: scoring the characteristic parameters corresponding to the quality attributes of the same candidate information system products in different test environments according to the characteristic parameter scoring intervals, and scoring the quality attributes of the information system products according to the characteristic parameter scores; Step S7: calculating scores of the same candidate information system products in different test environments according to the quality attribute scores; Step S8: comparing the scores of different candidate information system products in the same test environment, ranking all candidate information system products in the same test environment, and calculating comprehensive scores of all information system products to form a score table.

2. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The quality attributes of the information system products in the step S1 comprise performance, security, and technical adaptability; the characteristic parameters corresponding to the performance comprise processing speed, response time, and resource occupancy rate; the characteristic parameters corresponding to the security comprise encryption, authentication, authorization, audit, and vulnerability management; and the characteristic parameters corresponding to the technical adaptability comprise compatibility of the information system product with a system and integration difficulty.

3. The comprehensive product selection evaluation method based on information system autonomy according to claims 1-2, characterized in that: The process of obtaining the feedback records of users on the information system products through big data, screening the feedback records, and obtaining the evaluation marks of the characteristic parameters in the step S2 comprises: Step S2-1: obtaining the feedback records of users on the information system products through big data, wherein each feedback record comprises a test environment, quality attributes of the product, characteristic parameters corresponding to the quality attributes, and parameter values corresponding to the characteristic parameters in the test record; Step S2-2: statistically analyzing the parameter values corresponding to the characteristic parameters of all products in each test environment in the feedback record, and recording the number of occurrences of each parameter value corresponding to the characteristic parameters; Step S2-3: statistically analyzing the parameter value with the largest number of occurrences, and taking the parameter value as the evaluation mark of the characteristic parameters in the test record.

4. The integrated product selection evaluation method based on information system according to claim 1, characterized in that: The calculation formula of the distances from the characteristic parameters corresponding to the quality attributes of all information system products in each test environment to the evaluation marks in the step S3 is as follows: ； wherein d k is the distance of the kth characteristic parameter to the evaluation mark, ┌┐ is the integer symbol representing the minimum integer greater than or equal to d k T ijk is the jth quality attribute of the ith product, B ijk is the evaluation mark corresponding to the characteristic parameter T ijk .

5. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The process of dividing the characteristic parameter evaluation intervals in the step S4 comprises: Step S4-1: comparing the distance of the same characteristic parameters of all products in each test environment to the evaluation distance by a comparator, and screening out the maximum distance d max and the minimum distance d min ; Step S4-2: Establishing interval [d max , d min ] according to the maximum distance d min and the minimum distance d max , the total length of the interval being L = d max - d min , taking any one common factor n of L except 1 to divide the interval into n equal parts, the calculation formula being as follows: ； wherein t is the length of each interval. Step S4-3: set the total score as S, score mark the n intervals, divide the feature parameter evaluation interval, [d min ,d min The corresponding score is S / n points, [d min +t,d min The corresponding score is 2S / n points, …, [d min +(n-1)t,d max The corresponding score is S points, wherein S is an integer multiple of n.

6. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The step S5 deploys the test environment shown in the step S5, and tests the candidate information system product. The test record of the candidate information system product includes: in different test environments, the characteristic parameters corresponding to each quality attribute of the same information system product; and in the same test environment, the characteristic parameters corresponding to each quality attribute of different information system products.

7. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The step S6 includes the process of scoring each quality attribute of the candidate information system product, which includes: Step S6-1: according to the characteristic parameter evaluation interval, scoring each quality attribute of the same information system product in different test environments; Step S6-2: according to the characteristic parameter score, calculating the score of each quality attribute of the same information system product in different test environments, according to the following formula: ； Wherein, B i is the score of the i-th quality attribute of the information system product; W j is the weight value corresponding to the j-th characteristic parameter; A ij is the score of the j-th characteristic parameter corresponding to the i-th quality attribute.

8. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The step S7 includes the process of calculating the score of the same candidate information system product in different test environments, and the calculation formula is as follows: ； wherein f x is the score of the candidate information system product x; W xi is the weight value corresponding to the score of the i-th quality attribute of the candidate information system product x; B xi is the score of the i-th quality attribute of the candidate information system product x.

9. The comprehensive product selection evaluation method based on information system autonomy according to claim 1, characterized in that: The step S8 includes the process of forming a score table, which includes: Step S8-1: comparing the scores of different candidate information system products in the same test environment through a comparator, and ranking them in order from high to low; Step S8-2: calculating the comprehensive score of the candidate information system product according to the score and ranking of the candidate information system in the same test environment, and the calculation formula is as follows: ； where F x is the overall score for candidate information system product x; n i is the rank of candidate information system product x in test environment i; f ix is the score of candidate information system product x in test environment i. Step S8-3: ranking the candidate information system product according to the comprehensive score of the product, and forming a score table. The content of each row of the score table includes the ranking, the name of the candidate information system product and the comprehensive score.

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