Software product information management system based on big data

Abstract

The invention discloses a software product information management system based on big data, relates to the technical field of computer application, and comprehensively considers the average activeness value Aavg of a user, the influence index Pi of the user on a system software product and the performance M of the software product in the market through algorithm combination. Software system comprehensive score values H of different software products are calculated and compared with a good threshold value Y1 of the software system comprehensive score values, the software with the software system comprehensive score values H larger than Y1 is classified into an interval I, the software with the software system score values H smaller than Y1 is classified into an interval II, and after the software products in the computer are classified, the software products in the interval II are classified into an interval II; according to the method, the software with the low H value in the first interval is maintained and updated and iterated preferentially, it is ensured that limited resources are efficiently utilized on the software product most needing to be improved, and therefore the quality of the whole software system and the user experience are improved, and the problem that due to the fact that the superiority of the software product is reduced, customers are lost is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer application technology, and in particular to a software product information management system based on big data. Background Art

[0002] Software products refer to computer software provided to users, software embedded in information systems or devices, or computer software provided when providing technical services such as computer information system integration and application services.

[0003] Big data, or massive data, refers to information so large in volume that it cannot be captured, managed, processed, and organized into information that helps businesses make more proactive business decisions within a reasonable timeframe using mainstream software tools.

[0004] Software product information management systems on computers or mobile devices (such as mobile phones) can usually only passively record and store usage records of multiple software on the computer or mobile device and interaction information with users. They cannot timely update or maintain software products based on the usage of multiple software products on the computer or mobile device by multiple users. As the computer or mobile device is used, the superiority of the software product may decrease, leading to customer churn. On the contrary, timely maintenance and updating of software products will improve user experience.

[0005] In response to the above problems, the present invention provides a software product information management system based on big data. Summary of the Invention

[0006] The purpose of the present invention is to provide a software product information management system based on big data to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a software product information management system based on big data, the system comprising the following steps:

[0008] Information data collection, including:

[0009] In the software database, the average number of clicks after each user login is calculated by analyzing and calculating the system records of the user's interaction with the software. i ;

[0010] Take weeks as the time unit and obtain the total login time T in the software database i And the number of times the user logs in during the week L i ;

[0011] In the user feedback interface of the software, obtain the highest feedback score F for the software product settings max, and the average score F reported by all users avg ;

[0012] Data preprocessing, collect the average number of clicks C i , total login time T i , and the number of logins L i , the highest feedback score is F max , average score F avg The data is transmitted to the data processing module for decoding preprocessing to obtain the average activity value A of the affected users. avg And the user influence index P i important parameters.

[0013] Software product information management system, specifically including:

[0014] Substitute the decoded parameter values ​​into the activity value algorithm unit and the user influence index algorithm unit to calculate the average activity value A avg And the user influence index P i , two parameters that have an important impact on the comprehensive score value of the software system;

[0015] Next, the calculated average activity value A avg And the user influence index P i Substitute the two parameter values ​​into the software system comprehensive score algorithm unit, and combine the software product's market share M relative to the previous quarter to calculate the software system's comprehensive score H.

[0016] Afterwards, the calculated software system comprehensive score H is recorded and stored in the database, and the good threshold value Y1 of the software system comprehensive score is set to 0.2. The software system comprehensive score H is compared with the good threshold value Y1, and the software with a software system comprehensive score H greater than Y1 is classified into interval one. Software updates and iterations within interval one are prioritized for software with lower software system comprehensive scores H. Software with a software system score H less than Y1 is classified into interval two. Software within interval two will not undergo software system updates and iterations unless its software system comprehensive score H rises above Y1.

[0017] Iterative feedback: After the calculation of the system comprehensive score H of the software product, the next user average activity value A of the software product is calculated. avg During the calculation process, the system comprehensive score H obtained in the previous calculation will be substituted into the activity value algorithm unit for iterative calculation, and the average activity value A of the new round of users will be calculated. avg Calculation has a positive impact.

[0018] Optionally, the software product information management system operates using an information data collection module, a data processing module, a calculation processing module and a data storage module.

[0019] Optionally, the information data collection module is used to analyze and calculate the average number of clicks C after each user logs in the software database through the system records when the user interacts with the software. i ;

[0020] Take weeks as the time unit and obtain the total login time T in the software database i And the number of times the user logs in during the week L i ;

[0021] Get the highest feedback score F for software product settings from the software's user feedback interface max , and the average score F reported by all users avg ;

[0022] The data processing module is used to pre-process the obtained data to obtain the average activity value A of the affected users. avg And the user influence index P i The important parameters of the calculation module are substituted into the calculation processing module to calculate the average activity value A avg And the user influence index P i ;

[0023] The average activity value A avg And the user influence index P i Combined with the growth share M of the software product in the market relative to the previous quarter, these are substituted into the calculation processing module to calculate the comprehensive score H of the software system. The calculated comprehensive score H of the software system is recorded and stored in the database through the data storage module, and compared with the good threshold Y1;

[0024] Classify software with a software system comprehensive score H greater than Y1 into interval one, and classify software with a software system score H less than Y1 into interval two. Software system updates and iterations within interval one will be prioritized for software with lower software system comprehensive score H. Software in interval two will not be updated or iterated unless its software system comprehensive score H rises above Y1.

[0025] Optionally, the calculation and processing module includes an activity value algorithm unit, a user influence index algorithm unit, and a water system comprehensive score value algorithm unit.

[0026] Optionally, the activity value algorithm unit is as follows:

[0027]

[0028] in:

[0029] A avg Represents the average activity value of all users;

[0030] n represents the number of users;

[0031] H prev represents the comprehensive score of the software system calculated last time; θ is the iterative adjustment coefficient;

[0032] A i Represents the activity value of a single user. The specific algorithm is as follows:

[0033]

[0034] in:

[0035] C i Represents the number of clicks by the user;

[0036] L i Represents the number of logins of the user;

[0037] T i Indicates the user's online time.

[0038] Optionally, the user influence index algorithm unit is as follows:

[0039]

[0040] in:

[0041] P i Represents the user impact index;

[0042] A avg Represents the average activity value of all users;

[0043] A max Represents the maximum activity value of a single user in the software system;

[0044] A min Represents the minimum activity value of a single user in the software system;

[0045] F avg Represents the average feedback score of all users;

[0046] F max Represents the highest score of feedback;

[0047] S represents the stability index of the software system. The specific algorithm is as follows:

[0048]

[0049] in:

[0050] E r represents the error rate;

[0051] R t Represents the corresponding time;

[0052] R tmax Represents the maximum response time that the software system can accept

[0053] Optionally, the software system comprehensive score value algorithm unit is as follows:

[0054]

[0055] in:

[0056] H represents the comprehensive score of the software system;

[0057] A avg Represents the average activity value of all users;

[0058] A max Represents the maximum activity value of a single user in the software system;

[0059] P i Represents the user impact index;

[0060] M represents the growth rate of software product’s market share;

[0061] α and β are the average activity values ​​of users A avg , User Influence Index P i The weight factor of .

[0062] Optionally, a good threshold value Y1 of the software system comprehensive score value is set to 0.2.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention uses a combination of algorithms to comprehensively consider the average activity value A of the user avg , User influence index on system software products P i And the performance M of the software product in the market, calculate the software system comprehensive score H of different software products and compare it with the good threshold Y1, classify the software with a software system comprehensive score H greater than Y1 into interval one, and classify the software with a software system score H less than Y1 into interval two. After classifying the software products in the computer, give priority to maintaining and updating the software with a lower H value in interval one, ensuring that limited resources are efficiently used on the software products that need improvement the most, thereby improving the quality of the overall software system and user experience, and avoiding the problem of customer loss due to reduced superiority of software products. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flowchart of the software product information management system based on big data;

[0066] Figure 2 This is a schematic diagram of the overall structure of the software product information management system based on big data. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example 1: Please refer to Figure 1 and Figure 2 The present invention provides a technical solution: a software product information management system based on big data, the system includes the following steps:

[0069] Information data collection, including:

[0070] In the software database, the average number of clicks after each user login is calculated by analyzing and calculating the system records of the user's interaction with the software. i ;

[0071] Take weeks as the time unit and obtain the total login time T in the software database i And the number of times the user logs in during the week L i ;

[0072] In the user feedback interface of the software, obtain the highest feedback score F for the software product settings max , and the average score F reported by all users avg ;

[0073] Data preprocessing, collect the average number of clicks C i , total login time T i , and the number of logins L i , the highest feedback score is F max , average score F avg The data is transmitted to the data processing module for decoding preprocessing to obtain the average activity value A of the affected users. avg And the user influence index P i important parameters.

[0074] Software product information management system, specifically including:

[0075] Substitute the decoded parameter values ​​into the activity value algorithm unit and the user influence index algorithm unit to calculate the average activity value A avg And the user influence index P i , two parameters that have an important impact on the comprehensive score value of the software system;

[0076] Next, the calculated average activity value A avg And the user influence index P i Substitute the two parameter values ​​into the software system comprehensive score algorithm unit, and combine the software product's market share M relative to the previous quarter to calculate the software system's comprehensive score H.

[0077] Afterwards, the calculated software system comprehensive score H is recorded and stored in the database, and the good threshold value Y1 of the software system comprehensive score is set to 0.2. The software system comprehensive score H is compared with the good threshold value Y1, and the software with a software system comprehensive score H greater than Y1 is classified into interval one. Software updates and iterations within interval one are prioritized for software with lower software system comprehensive scores H. Software with a software system score H less than Y1 is classified into interval two. Software within interval two will not undergo software system updates and iterations unless its software system comprehensive score H rises above Y1.

[0078] Iterative feedback: After the calculation of the system comprehensive score H of the software product, the next user average activity value A of the software product is calculated. avg During the calculation process, the system comprehensive score H obtained in the previous calculation will be substituted into the activity value algorithm unit for iterative calculation, and the average activity value A of the new round of users will be calculated. avg Calculation has a positive impact.

[0079] The software product information management system operates using an information data collection module, a data processing module, a calculation processing module, and a data storage module.

[0080] In this embodiment, the present invention forms a complete software product information management system based on big data through three algorithms, and all three algorithms have substantial effects:

[0081] Activity value algorithm 1:

[0082] Formula 1 combines the number of clicks C i , the number of user logins L i And online time T i These factors together determine the user's average activity value A avgBy incorporating these factors into a unified calculation formula, the user activity of different software products can be more accurately reflected. The average user activity value A is calculated. avg The software product information management system can understand the user's experience and feelings when using different software products. For software products with a low average activity index, the system can analyze the possible problems of the software products based on the information of customer interactions with them, such as unfriendly interface, complicated operation, etc., and make corresponding optimizations and improvements to enhance the user experience.

[0083] User Influence Index Algorithm 2:

[0084] Formula 2 combines the user's average activity value A avg , the average feedback score of users F avg and the stability index S of the software system. These factors together determine the user's impact index P on the system software product. i , by calculating the user influence index P i The software product information management system can more accurately identify user groups with high activity and active feedback participation. For these users, the system can provide more personalized services and optimization suggestions based on big data analysis, thereby improving their user experience;

[0085] Including the system stability index S in the calculation of the user impact index will help the software product information management system to promptly discover and address potential problems that may affect system stability, and ensure stability for users when using system software products by continuously optimizing system performance and improving stability.

[0086] Software system comprehensive score algorithm three:

[0087] Comprehensively consider the user's average activity value A avg , User influence index on system software products P i , and the performance M of the software product in the market, the comprehensive score H of the software system is calculated, which enables the software product information management system to have a more comprehensive understanding of the overall status of all software products.

[0088] Through the combination of three algorithms, the comprehensive score value H of the software system is calculated and compared with the good threshold value Y1 of the software system comprehensive score value. The software with a software system comprehensive score value H greater than Y1 is classified into interval one, and the software with a software system score value H less than Y1 is classified into interval two. The software system update iteration within interval one will give priority to the software with a lower software system comprehensive score value H. The software in interval two will not be updated or iterated unless its software system comprehensive score value H rises to a value higher than Y1.

[0089] By categorizing software products in a computer according to the software system's comprehensive score H, the system prioritizes maintenance and updates for software with lower H scores. This approach ensures that limited resources are efficiently utilized on software that needs improvement the most, thereby improving the overall software system quality and user experience, and avoiding customer churn due to reduced software product superiority.

[0090] Similarly, for the software in interval two, since its comprehensive score H is low and does not exceed the set good threshold Y1, it will be considered for abandonment in the subsequent software product information management system, and therefore will not be maintained or updated for the time being. This approach avoids wasting resources on unnecessary or ineffective software product maintenance and updates, so that resources can be allocated more reasonably and centrally, thereby improving the maintenance efficiency of the software product information management system for product software.

[0091] See also Figure 1 and Figure 2 , the activity value algorithm unit is as follows:

[0092]

[0093] in:

[0094] A avg Represents the average activity value of all users;

[0095] n represents the number of users;

[0096] H prev Represents the comprehensive score of the software system calculated last time by the software;

[0097] θ is the iterative adjustment coefficient, which is a positive number and is used to control the last software system comprehensive score H prev Average activity value of users A avg The degree of impact can be adjusted automatically as the software product information management system is developed;

[0098] A i Represents the activity value of a single user. The specific algorithm is as follows:

[0099]

[0100] in:

[0101] C i The number of clicks on behalf of the user is the average number of clicks each time the user logs into the software system. This is calculated by analyzing the system records of the user's interaction with the software.

[0102] L i Represents the number of logins by the user in a week;

[0103] T i Represents the online time of the user in a week, in minutes;

[0104] Get the total login time T in the software database i And the number of times the user logs in during the week L i ;

[0105] In the formula, This part first passes the number of clicks by the user C i The number of times a user logs in during a week L i After multiplication, we can get the total number of clicks on the software product in a week, and then take the square root of the total number of clicks. Since the number of clicks and logins of users may vary from person to person, some users may be very active with high clicks and logins, while other users may have relatively few. By taking the square root of the product of these two variables, we can reduce the impact of extreme values ​​on the final result, so that the calculated average activity value of users A avg The values ​​are more stable.

[0106] In this embodiment: combined with the number of clicks C of the user i , the number of user logins L i And online time T i These factors together determine the user's average activity value A avg , in order to more accurately reflect the user activity of different software products, the average user activity value A is calculated avg The software product information management system can understand the user's experience and feelings when using different software products. For software products with a low average activity index, the system can analyze the possible problems of the software products based on the information of customer interactions with them, such as unfriendly interface, complicated operation, etc., and make corresponding optimizations and improvements to enhance the user experience.

[0107] See also Figure 1 and Figure 2 ,The user influence index algorithm unit is as follows:

[0108]

[0109] in:

[0110] P i Represents the user impact index;

[0111] A avg Represents the average activity value of all users;

[0112] A maxRepresents the maximum activity value of a single user in the software system;

[0113] A min Represents the minimum activity value of a single user in the software system;

[0114] F avg Represents the average feedback score of all users;

[0115] F max Represents the highest score of the feedback;

[0116] In the user feedback interface of the software product, obtain the highest feedback score F set by the software product max , and the average score F reported by all users avg ;

[0117] S represents the stability index of the software system. The specific algorithm is as follows:

[0118]

[0119] in:

[0120] E r represents the error rate, which is obtained by dividing the number of system errors in 100 user clicks by 100;

[0121] R t Represents the corresponding time in milliseconds;

[0122] R tmax Represents the maximum response time that the software system can accept, also in milliseconds.

[0123] This part summarizes the maximum activity value A of a single user in the software system max , and the minimum activity value A of a single user in the software system min , the user's average activity value A avg Normalize to a value between 0 and 1 to participate in subsequent operations;

[0124] This part will take the user's average feedback score F avg The highest score F relative to all user feedback scores max Normalize and smoothly reflect the user feedback score's impact index P on the user through square root operation i impact.

[0125] This part is used as the numerator in the calculation formula of the stability index S of the software system, which represents the error rate E. rThe higher it is, the lower the stability is. By taking the square root and subtracting it from 1, the error rate can be converted into a parameter that has a positive impact on the stability index S of the software system;

[0126] This part represents the response time R t The larger it is, the lower the stability is. The response time is converted into a parameter that has a negative impact on stability by dividing the response time by the maximum response time, adding 1, and taking the reciprocal.

[0127] In this embodiment: Combined with the average activity value A of the user avg , the average feedback score of users F avg The three key factors, namely, the stability index S of the software system, jointly determine the user's impact index P on the system software product. i , by calculating the user influence index P i The software product information management system can more accurately identify user groups with high activity and active participation in feedback. For these users, the system can provide more personalized services and optimization suggestions based on big data analysis, thereby improving their user experience.

[0128] See also Figure 1 and Figure 2 ,

[0129] The software system comprehensive score value algorithm unit is as follows:

[0130]

[0131] in:

[0132] H represents the comprehensive score of the software system;

[0133] A avg Represents the average activity value of all users;

[0134] A max Represents the maximum activity value of a single user in the software system;

[0135] P i Represents the user impact index;

[0136] M represents the growth rate of software product share in the market, M∈(-1,1),

[0137] α and β are the average activity values ​​of users A avg , User Influence Index P i Weight factor, used to control the average user activity value A avg And the user influence index P i The degree of influence on the system comprehensive score H, α and β can be self-adjusted as the software product information management system is managed.

[0138] In this embodiment:

[0139] Through three formulas, the average activity value A of the user is comprehensively considered avg , User influence index on system software products P i , and the performance M of the software product in the market, calculate the software system score H, and compare it with the good threshold Y1, classify the software with a software system comprehensive score H greater than Y1 into interval one, and classify the software with a software system score H less than Y1 into interval two. The software system update iteration in interval one will be prioritized for the software with a lower software system comprehensive score H. The software in interval two will not be updated or iterated unless its software system comprehensive score H rises to a level higher than Y1.

[0140] The system can prioritize the maintenance and update of software with lower H values ​​within the classified range. This approach can ensure that limited resources are efficiently utilized on software that most needs improvement, thereby improving the quality of the overall software system and user experience, and avoiding the problem of customer loss due to reduced superiority of software products.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A software product information management system based on big data, characterized by: The system comprises the following steps: Information data collection, including: In the software database, the average number of clicks after each user login is calculated by analyzing and calculating the system records of the user's interaction with the software. i ; Take weeks as the time unit and obtain the total login time T in the software database i And the number of times the user logs in during the week L i ; In the user feedback interface of the software, obtain the highest feedback score F for the software product settings max , and the average score F reported by all users avg ; Data preprocessing, collect the average number of clicks C i , total login time T i , and the number of logins L i , the highest feedback score is F max , average score F avg The data is transmitted to the data processing module for decoding preprocessing to obtain the average activity value A of the affected users. avg And user influence index P i important parameters. Software product information management system, specifically including: Substitute the decoded parameter values ​​into the activity value algorithm unit and the user influence index algorithm unit to calculate the average activity value A avg And user influence index P i , two parameters that have an important impact on the comprehensive score value of the software system; Next, the calculated average activity value A avg And user influence index P i Substitute the two parameter values ​​into the software system comprehensive score algorithm unit, and combine the software product's market share M relative to the previous quarter to calculate the software system's comprehensive score H. Afterwards, the calculated software system comprehensive score H is recorded and stored in the database, and the good threshold value Y1 of the software system comprehensive score is set to 0.

2. The software system comprehensive score H is compared with the good threshold value Y1, and the software with a software system comprehensive score H greater than Y1 is classified into interval one. Software updates and iterations within interval one are prioritized for software with lower software system comprehensive scores H. Software with a software system score H less than Y1 is classified into interval two. Software within interval two will not undergo software system updates and iterations unless its software system comprehensive score H rises above Y1. Iterative feedback: After the calculation of the system comprehensive score H of the software product, the next user average activity value A of the software product is calculated. avg During the calculation process, the system comprehensive score H obtained in the previous calculation will be substituted into the activity value algorithm unit for iterative calculation, and the average activity value A of the new round of users will be calculated. avg Calculation has a positive impact.

2. The software product information management system based on big data according to claim 1, characterized in that: The software product information management system is operated by an information data collection module, a data processing module, a calculation processing module and a data storage module.

3. The software product information management system based on big data according to claim 2, characterized in that: The information data collection module is used to analyze and calculate the average number of clicks C after each user logs in the software database through the system records when the user interacts with the software. i ; Take weeks as the time unit and obtain the total login time T in the software database i And the number of times the user logs in during the week L i ; Get the highest feedback score F for software product settings from the software's user feedback interface max , and the average score F reported by all users avg ; The data processing module is used to pre-process the obtained data to obtain the average activity value A of the affected users. avg And user influence index P i The important parameters of the calculation module are substituted into the calculation processing module to calculate the average activity value A avg And user influence index P i ; The average activity value A avg And user influence index P i Combined with the growth share M of the software product in the market relative to the previous quarter, these are substituted into the calculation processing module to calculate the comprehensive score H of the software system. The calculated comprehensive score H of the software system is recorded and stored in the database through the data storage module, and compared with the good threshold Y1; Classify software with a software system comprehensive score H greater than Y1 into interval one, and classify software with a software system score H less than Y1 into interval two. Software system updates and iterations within interval one will be prioritized for software with lower software system comprehensive score H. Software in interval two will not be updated or iterated unless its software system comprehensive score H rises above Y1.

4. The software product information management system based on big data according to claim 2, characterized in that: The calculation and processing module includes an activity value algorithm unit, a user influence index algorithm unit and a water system comprehensive score value algorithm unit.

5. The software product information management system based on big data according to claim 4, characterized in that: The activity value algorithm unit is as follows: in: A avg Represents the average activity value of all users; n represents the number of users; H prev Represents the comprehensive score of the software system calculated last time by the software; θ is the iterative adjustment coefficient; A i Represents the activity value of a single user. The specific algorithm is as follows: in: C i Represents the number of clicks by the user; L i Represents the number of logins of the user; T i Indicates the user's online time.

6. The software product information management system based on big data according to claim 5, characterized in that: The user influence index algorithm unit is as follows: in: P i Represents the user impact index; A avg Represents the average activity value of all users; A max Represents the maximum activity value of a single user in the software system; A min Represents the minimum activity value of a single user in the software system; F avg Represents the average feedback score of all users; F max Represents the highest score of feedback; S represents the stability index of the software system. The specific algorithm is as follows: in: E r represents the error rate; R t Represents the corresponding time; R tmax Represents the maximum response time that the software system can accept.

7. The software product information management system based on Big P data according to claim 4, characterized in that: The software system comprehensive score value algorithm unit is as follows: in: H represents the comprehensive score of the software system; A avg Represents the average activity value of all users; A max Represents the maximum activity value of a single user in the software system; P i Represents the user impact index; M represents the growth rate of software product’s market share; α and β are the average activity values ​​of users A avg , User Influence Index P i The weight factor of .

8. The software product information management system based on big data according to claim 7, characterized in that: The good threshold value Y1 of the comprehensive score of the software system is set to 0.2.

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