Digital application recommendation method, device, equipment and storage medium

Abstract

The present invention relates to the field of precision marketing technology, and provides a digital application recommendation method, device, equipment, and storage medium. The method comprises: obtaining application basic data of a user's existing applications, evaluating and analyzing the application basic data based on a preset indicator evaluation system, and obtaining evaluation results for the existing applications; determining a solution recommendation rule based on the evaluation results; querying and matching applications in a preset application resource pool based on the solution recommendation rule to obtain a list of required applications and a list of to-be-selected applications; based on the constraints in the solution recommendation rule, screening target applications from the to-be-selected application list, adding them to the list of required applications, and combining them with the required applications to obtain an application combination solution and recommend it to the user. By constructing an indicator evaluation system to evaluate and analyze the user's existing applications, user needs can be quickly discovered, thereby recommending application combination solutions that meet their needs to the user, improving application recommendation efficiency while reducing the user's application costs.

Description

Technical Field

[0001] The present invention relates to the field of precision marketing technology, and in particular to a digital application recommendation method, device, equipment and storage medium. Background Art

[0002] With the rapid development of artificial intelligence and big data technologies, the importance of data assets has become increasingly prominent. Digital transformation is becoming a key component of corporate competitiveness. Most companies have gradually integrated digital capabilities into their production and operations to improve production capacity and efficiency. As an important component of realizing digital capabilities, digital applications make the selection and use of digital applications by enterprises particularly important. However, due to the wide variety and different functions of digital applications, enterprises are unable to select applications that meet their own needs from the vast number of digital applications in a self-service manner. Existing methods for recommending digital applications mainly focus on typical cases for specific users and solutions for industries or scenarios. This is an extensive application combination recommendation model. For different users in different industries, customized application solutions require extensive preliminary research, analysis and verification, which leads to problems such as high cost and low efficiency. Summary of the Invention

[0003] The present invention provides a digital application recommendation method, device, equipment and storage medium, which are used to solve the defects of high cost and low efficiency in the existing extensive application combination recommendation model.

[0004] The present invention provides a digital application recommendation method, comprising:

[0005] Obtaining basic application data of the user's existing applications, and evaluating and analyzing the basic application data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications;

[0006] Determine a solution recommendation rule based on the evaluation results; the solution recommendation rule includes constraints;

[0007] According to the solution recommendation rules, the applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications;

[0008] Based on the constraint condition, a target application is screened from the candidate application list, added to the mandatory application list, and combined with each mandatory application in the mandatory application list to obtain an application combination solution, and the application combination solution is recommended to the user.

[0009] According to the digital application recommendation method provided by the present invention, based on the constraint condition, the target application is screened from the candidate application list, added to the mandatory application list, and combined with each mandatory application in the mandatory application list to obtain an application combination solution, including:

[0010] Determining, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system;

[0011] Calculate the difference in full scores of each indicator in the indicator evaluation system for each mandatory application in the mandatory application list based on the first evaluation score and the preset full score of each indicator in the indicator evaluation system;

[0012] Determine the target indicator with the largest full score difference, and calculate the recommendation score of each candidate application in the candidate application list under the target indicator;

[0013] sorting the candidate applications in the candidate application list in descending order according to the recommendation score;

[0014] selecting a target application according to the sorting order of the candidate applications in the candidate application list, and combining the target application with the required applications in the required application list to obtain an application combination solution; the target application is a candidate application marked as unselected;

[0015] Calculating a solution score of the application combination solution, and determining whether the application combination solution satisfies the constraint condition according to the solution score;

[0016] If the application solution does not satisfy the constraint condition, the target application is removed from the list of candidate applications, and the process returns to and executes the step of selecting the target application according to the sorting order of the candidate applications in the list of candidate applications;

[0017] If the application combination scheme meets the constraint conditions, the target application is marked as selected and added to the required application list, and the step of selecting the target application according to the sorting order of the candidate applications in the candidate application list is returned and executed until the target application is the last candidate application in the candidate application list, or the number of target applications added to the required application list reaches the preset number of applications.

[0018] According to the digital application recommendation method provided by the present invention, the calculating of the solution score of the application combination solution includes:

[0019] Determining a second evaluation score for each indicator of the target application under the indicator evaluation system according to the evaluation result;

[0020] Calculating target evaluation scores for each indicator of the application combination solution under the indicator evaluation system based on the first evaluation score and the second evaluation score;

[0021] The target evaluation scores are summed to obtain a solution score of the application combination solution.

[0022] According to the digital application recommendation method provided by the present invention, determining, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system includes:

[0023] Determining, based on the evaluation results, an indicator value and an indicator score for each mandatory application in the mandatory application list under a first indicator; the first indicator being any one of the indicators under the indicator evaluation system;

[0024] Obtaining a preset indicator weight for the first indicator, and weighting the indicator value of the first indicator based on the indicator weight to obtain a weighted indicator value;

[0025] The sum of the weighted index value and the index score of the first index is calculated to obtain a first evaluation score of each mandatory application in the mandatory application list under the first index.

[0026] According to the digital application recommendation method provided by the present invention, the calculation of the recommendation score of each candidate application in the candidate application list under the target indicator includes:

[0027] Determine the target indicator value of each candidate application in the candidate application list under the target indicator according to the evaluation result, obtain the recommendation weight of each candidate application in the candidate application list, and the target full score value preset for the target indicator;

[0028] The ratio of the target indicator value to the target full score value is calculated, and the ratio and the recommendation weight are weightedly summed to obtain the recommendation score of each candidate application in the candidate application list under the target indicator.

[0029] According to the digital application recommendation method provided by the present invention, the application in the preset application resource pool is queried and matched according to the solution recommendation rule to obtain a required application list and a candidate application list, including:

[0030] According to the mandatory application configuration in the solution recommendation rule, query and match the applications in the preset application resource pool to obtain an initial mandatory application list;

[0031] Deduplicating the initial mandatory application list based on the existing applications to obtain a mandatory application list;

[0032] According to the optional application configuration in the solution recommendation rule, query and match the applications in the application resource pool to obtain an initial list of candidate applications;

[0033] The initial list of candidate applications is deduplicated based on the existing applications to obtain a list of candidate applications.

[0034] According to the digital application recommendation method provided by the present invention, the application basic data is evaluated and analyzed based on a preset indicator evaluation system to obtain an evaluation result of the existing application, including:

[0035] Based on the finest-grained indicator in the preset indicator evaluation system, the application basic data is evaluated and analyzed to determine the indicator value of the finest-grained indicator;

[0036] Determining an indicator score of the finest-grained indicator according to the indicator value of the finest-grained indicator;

[0037] Based on the indicator values ​​and indicator scores of the finest-grained indicators, the multi-level indicators in the indicator evaluation system are evaluated step by step to obtain an evaluation result of the existing application; wherein, the indicator value of any N-th level indicator in the indicator evaluation system is the sum of the indicator values ​​of the N+1-th level indicators under the N-th level indicator, and the indicator score of the N-th level indicator is the sum of the indicator scores of the N+1-th level indicators under the N-th level indicator.

[0038] The present invention also provides a digital application recommendation device, comprising the following modules:

[0039] An indicator evaluation module is used to obtain application basic data of the user's existing applications, evaluate and analyze the application basic data based on a preset indicator evaluation system, and obtain an evaluation result of the existing application;

[0040] A rule determination module is used to determine a solution recommendation rule based on the evaluation result; the solution recommendation rule includes a constraint condition;

[0041] An application matching module is used to query and match applications in a preset application resource pool according to the solution recommendation rules to obtain a list of required applications and a list of candidate applications;

[0042] The application recommendation module is used to filter out target applications from the candidate application list based on the constraint conditions, add the target applications to the mandatory application list, combine them with the mandatory applications in the mandatory application list, obtain an application combination solution, and recommend the application combination solution to the user.

[0043] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the above-described digital application recommendation methods are implemented.

[0044] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described digital application recommendation methods.

[0045] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above-described digital application recommendation methods are implemented.

[0046] The digital application recommendation method, apparatus, device, and storage medium provided by the present invention evaluate and analyze a user's existing applications to determine solution recommendation rules, thereby querying and matching a list of required applications and a list of candidate applications. Based on the constraints in the solution recommendation rules, target applications that meet the constraints are screened from the candidate application list and added to the required application list. These applications are then combined with the required applications in the required application list to obtain an application combination solution and recommend it to the user. By constructing an indicator evaluation system to evaluate and analyze a user's existing applications, it is possible to quickly discover the user's actual needs for digital applications, thereby recommending application combination solutions that meet their needs to the user, improving the efficiency of application recommendation while reducing the user's application costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 It is a flow chart of the digital application recommendation method provided by the present invention.

[0049] Figure 2 It is a structural diagram of the indicator evaluation system provided by the present invention.

[0050] Figure 3 It is a structural diagram of the digital application recommendation device provided by the present invention.

[0051] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] Figure 1 is a flow chart of the digital application recommendation method provided by the present invention, such as Figure 1 As shown, the method includes the following steps:

[0054] Step 100: Obtain application basic data of the user's existing applications, and evaluate and analyze the application basic data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications;

[0055] Step 200: determining a solution recommendation rule based on the evaluation result; the solution recommendation rule includes a constraint condition;

[0056] Step 300: query and match applications in a preset application resource pool according to the solution recommendation rule to obtain a required application list and a candidate application list;

[0057] Step 400 : Based on the constraint condition, select a target application from the candidate application list, add the target application to the mandatory application list, combine the target application with each mandatory application in the mandatory application list, obtain an application combination solution, and recommend the application combination solution to the user.

[0058] When recommending digital applications to users, we first obtain the basic application data of the user's existing applications. It should be noted that digital applications are software or hardware systems used by users in production and operation processes. For ease of calculation, digital applications have inherent attributes such as application type, application function, and application effect, as well as additional attributes such as recommendation weight and price. Users here can be enterprises, organizations, or individuals, and basic application data includes data such as application type, usage, and user behavior of existing applications.

[0059] Based on the preset indicator evaluation system, the basic application data is evaluated and analyzed to obtain the evaluation results of existing applications. The solution recommendation rules are determined based on the evaluation results. The solution recommendation rules contain constraints on application recommendations, which include but are not limited to the types of applications that can be recommended, the number of recommendations, and the types of applications that must be applied.

[0060] In one embodiment, solution recommendation rules are the rules used when recommending applications. When determining solution recommendation rules based on evaluation results, the application scenarios are determined based on the evaluation results of existing applications. Different solution recommendation rules correspond to different scenarios. Furthermore, different solution recommendation rules are differentiated based on the rule content, which specifically includes the rule name, applicable scenario, and constraints.

[0061] Based on the solution recommendation rules, applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications. The application resource pool contains a vast number of digital applications, and each application in the application resource pool has a unique application identifier to distinguish between different applications. The required application list includes one or more required applications, and the candidate application list includes one or more candidate applications.

[0062] When recommending apps, we recommend them in the form of an app combination scheme, which is a recommendation scheme generated by combining multiple apps. Specifically, based on the constraints in the scheme recommendation rules, we filter out target apps from the list of candidate apps and add them to the list of required apps. We then combine the filtered target apps with the required apps in the list to generate an app combination scheme, which we then recommend to the user.

[0063] Optionally, the target application added to the mandatory application list includes one or more target applications, and the number of target applications added to the mandatory application list can be determined according to the number of recommended applications configured in the constraint conditions and / or the number of candidate applications in the candidate application list.

[0064] In this embodiment, by evaluating and analyzing the user's existing applications, solution recommendation rules are determined, thereby querying and matching a list of required applications and a list of candidate applications. Based on the constraints in the solution recommendation rules, target applications that meet the constraints are screened from the candidate application list and added to the required application list. These applications are then combined with the required applications in the required application list to generate an application combination solution and recommend it to the user. By building an indicator evaluation system to evaluate and analyze users' existing applications, we can quickly discover their actual needs for digital applications and recommend application combination solutions that meet their needs. This improves the efficiency of application recommendation and reduces the user's application costs.

[0065] In one embodiment, a pre-set indicator evaluation system includes multiple levels of indicators. In the indicator evaluation system, each first-level indicator is further subdivided into one or more second-level indicators, and each second-level indicator is further subdivided into one or more third-level indicators, and so on, until the finest-grained indicator items are finally subdivided as leaf nodes. Furthermore, the sum of the maximum score values ​​of the finest-grained indicator items is a preset value, such as 100. For any two adjacent levels of indicators, the maximum score value of the upper-level indicator is the sum of the full scores of the lower-level indicators. Digital applications set the indicator values ​​of the finest-grained indicator items based on application effects and functional characteristics.

[0066] Optional, Figure 2 The three-level indicator evaluation system of digital application is given as an example. Figure 2 In the example, the first-level indicators include three indicators: first-level indicator 1, first-level indicator 2, and first-level indicator 3. Furthermore, first-level indicator 1 is further subdivided into second-level indicator 1, second-level indicator 2, and second-level indicator 3. First-level indicator 2 is further subdivided into second-level indicator 4 and second-level indicator 5. First-level indicator 3 is further subdivided into second-level indicator 6 and second-level indicator 7. Furthermore, second-level indicator 1 is further subdivided into third-level indicator 1, third-level indicator 2, and third-level indicator 3. Second-level indicator 7 is further subdivided into third-level indicator n-2, third-level indicator n-1, and third-level indicator n. If the third-level indicator is the finest-grained indicator, then in the indicator evaluation system of digital applications, the third-level indicator includes n finest-grained indicators, and the full score of the n finest-grained indicators is the preset value. The full score of the second-level indicator 7 is the sum of the full scores of the three indicator items of the third-level indicator n-2, the third-level indicator n-1 and the third-level indicator n. The full score of the second-level indicator 1 is the sum of the full scores of the three indicator items of the third-level indicator 1, the third-level indicator 2 and the third-level indicator 3. The full score of the first-level indicator 1 is the sum of the full scores of the three indicator items of the second-level indicator 1, the second-level indicator 2 and the second-level indicator 3. The full score of the second-level indicator 2 is the sum of the full scores of the two indicator items of the second-level indicator 4 and the second-level indicator 5. The full score of the first-level indicator 3 is the sum of the full scores of the two indicator items of the second-level indicator 6 and the second-level indicator 7.

[0067] Based on this, in step 100, based on a preset indicator evaluation system, the application basic data of the existing application is evaluated and analyzed to obtain the evaluation results of the existing application, including:

[0068] Step 101: Based on the finest-grained indicator in a preset indicator evaluation system, the application basic data is evaluated and analyzed to determine the indicator value of the finest-grained indicator;

[0069] Step 102: determining an indicator score of the finest-grained indicator according to the indicator value of the finest-grained indicator;

[0070] Step 103: Based on the indicator value and indicator score of the finest-grained indicator, the multi-level indicators in the indicator evaluation system are evaluated step by step to obtain the evaluation result of the existing application; wherein, the indicator value of any N-th level indicator in the indicator evaluation system is the sum of the indicator values ​​of the N+1-th level indicators under the N-th level indicator, and the indicator score of the N-th level indicator is the sum of the indicator scores of the N+1-th level indicators under the N-th level indicator.

[0071] When evaluating and analyzing the application basic data of existing applications, the application basic data is evaluated and analyzed based on the finest-grained indicators in the indicator evaluation system, the indicator value of the finest-grained indicator of the finest-grained indicator is determined, and the indicator score of the finest-grained indicator is determined based on the indicator value of the finest-grained indicator.

[0072] Furthermore, based on the indicator values ​​and indicator scores of the finest-grained indicators, the multi-level indicators in the indicator evaluation system are evaluated level by level to obtain the evaluation results of the existing applications. During the level-by-level evaluation, the indicator value of any N-th level indicator in the indicator evaluation system is the sum of the indicator values ​​of the N+1-th level indicators under the N-th level indicator, and the indicator score of the N-th level indicator is the sum of the indicator scores of the N+1-th level indicators under the N-th level indicator.

[0073] For example, Figure 2 In the indicator evaluation system shown, the index value of the first-level indicator is the sum of the index values ​​of the second-level indicators, the index value of the first-level indicator 1 is the sum of the index values ​​of the three indicator items of second-level indicators 1, second-level indicators 2 and second-level indicators 3, and the index score of the first-level indicator 1 is the sum of the index scores of the three indicator items of second-level indicators 1, second-level indicators 2 and second-level indicators 3.

[0074] Among them, the indicator score of the most fine-grained indicator can be obtained by scoring the indicator value, and the scoring method includes but is not limited to scoring the indicator value based on scoring rules, or scoring the indicator value based on an AI model.

[0075] In one embodiment, the mandatory application list and the candidate application list are obtained by querying and matching solution recommendation rules with applications in the application resource pool, and then deduplicating the queried applications based on the user's existing applications.

[0076] Specifically, step 300 further includes:

[0077] Step 301: According to the mandatory application configuration in the solution recommendation rule, the applications in the preset application resource pool are queried and matched to obtain an initial mandatory application list;

[0078] Step 302: Deduplication is performed on the initial mandatory application list based on the existing applications to obtain a mandatory application list;

[0079] Step 303: According to the optional application configuration in the solution recommendation rule, the applications in the application resource pool are queried and matched to obtain an initial list of candidate applications;

[0080] Step 304: De-duplicate the initial list of candidate applications based on the existing applications to obtain a list of candidate applications.

[0081] The solution recommendation rules include mandatory application configurations and optional application configurations. When querying and matching the application resource pool, the preset application resource pool is queried and matched according to the mandatory application configurations in the solution recommendation rules to obtain an initial mandatory application list. Then, based on the user's existing applications, the mandatory applications in the initial mandatory application list are deduplicated, and the user's existing applications are filtered out from the initial mandatory application list to obtain a mandatory application list.

[0082] Furthermore, according to the optional application configuration in the solution recommendation rules, the applications in the application resource pool are queried and matched to obtain an initial list of candidate applications. Then, based on the user's existing applications, the candidate applications in the initial list of candidate applications are deduplicated, and the user's existing applications are filtered out from the initial list of candidate applications to obtain a list of candidate applications.

[0083] The solution recommendation rules also include constraints, which include application type and recommended quantity. Based on the constraints, without exceeding the recommended quantity for digital applications, the target application is screened from the list of candidate applications and added to the list of required applications, and combined with the required applications in the list of required applications to obtain an application combination solution.

[0084] In step 400, based on the constraint conditions, the target application is selected from the candidate application list and added to the mandatory application list. The target application is then combined with each mandatory application in the mandatory application list to obtain an application combination solution, which specifically includes:

[0085] Step 401: Determine, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system;

[0086] Step 402: Calculate the difference in full scores of each indicator in the indicator evaluation system for each mandatory application in the mandatory application list based on the first evaluation score and the preset full score of each indicator in the indicator evaluation system.

[0087] Step 403: determining the target indicator with the largest full score difference, and calculating the recommendation score of each candidate application in the candidate application list under the target indicator;

[0088] Step 404: sort the candidate applications in the candidate application list in descending order according to the recommendation score;

[0089] Step 405: Select a target application according to the sorting order of the candidate applications in the candidate application list, and combine the target application with the required applications in the required application list to obtain an application combination solution; the target application is the candidate application marked as unselected;

[0090] Step 406: Calculate the solution score of the application combination solution, and determine whether the application combination solution satisfies the constraint condition according to the solution score;

[0091] Step 407: If the application solution does not satisfy the constraint condition, the target application is removed from the list of candidate applications, and the process returns to and executes the step of selecting the target application according to the sorting order of the candidate applications in the list of candidate applications.

[0092] Step 408: If the application combination solution satisfies the constraint condition, the target application is marked as selected and added to the required application list, and the step of selecting the target application according to the sorting order of the candidate applications in the candidate application list is returned and executed until the target application is the last candidate application in the candidate application list, or the number of target applications added to the required application list reaches the preset number of applications.

[0093] When screening each candidate application in the list of candidate applications, the evaluation score for each indicator of the indicator evaluation system for each mandatory application in the list of required applications is determined based on the evaluation results, thereby obtaining a first evaluation score for each indicator of the mandatory application. Furthermore, based on the first evaluation score and the preset full score values ​​for each indicator in the indicator evaluation system, the full score difference for each mandatory application in the list of required applications under the indicator evaluation system is calculated. For any mandatory application, the full score for each indicator in the indicator evaluation system is subtracted from the corresponding first evaluation score to obtain the full score difference.

[0094] The indicators are sorted according to their full score differences under the indicator evaluation system, so as to select the target indicator with the largest full score difference, calculate the recommended score of each candidate application in the candidate application list under the target indicator, and sort the candidate applications in descending order according to the recommended score.

[0095] Furthermore, according to the sorting order of the candidate applications, the target application with the highest recommendation score is selected, and the target application is combined with each mandatory application in the mandatory application list to generate an application combination solution. It should be noted that the selected target application is the candidate application marked as unselected.

[0096] For the generated application combination solution, calculate the solution score of the application combination solution, and determine whether the generated application combination solution meets the constraints in the solution recommendation rules based on the solution score. If the application combination solution meets the constraints, mark the target application as selected and add it to the list of required applications, then reselect the next candidate application marked as unselected as the target application according to the sorting order of the candidate applications, and repeat the above steps. If the generated application combination solution does not meet the constraints, remove the current target application from the list of candidate applications, then reselect the next candidate application marked as unselected as the target application according to the sorting order of the candidate applications, and repeat the above steps. This continues until the selected target application is the last candidate application in the list of candidate applications, or the number of target applications added to the list of required applications reaches the preset number of applications, where the preset number of applications is included in the constraints.

[0097] Optionally, in step 403, calculating the recommendation score of each candidate application in the candidate application list under the target indicator includes:

[0098] Step 413: Determine the target indicator value of each candidate application in the candidate application list under the target indicator based on the evaluation result, obtain the recommendation weight of each candidate application in the candidate application list, and the target full score preset for the target indicator;

[0099] Step 423 , calculating the ratio of the target indicator value to the target full score value, and performing weighted summation on the ratio and the recommendation weight to obtain the recommendation score of each candidate application in the candidate application list under the target indicator.

[0100] When calculating the recommendation score of each candidate application in the candidate application list under the target indicator, first determine the target indicator value of each candidate application in the candidate application list under the target indicator based on the evaluation results, obtain the recommendation weight of each candidate application in the candidate application list, and the preset target full score value of the target indicator.

[0101] Then, the ratio of the target indicator value to the target full score value is calculated, and the ratio is weightedly summed with the recommendation weight to obtain the recommendation score of each candidate application in the candidate application list under the target indicator.

[0102] For example, for the candidate application a, its corresponding recommendation weight is , its index value under target index i is , the target full score of target indicator i is , calculate the recommendation score of the candidate application under the target indicator i according to the following formula 1 :

[0103] ; (1)

[0104] in, 、 、 、 is a configurable constant coefficient, is the price in the additional attributes of application a, The maximum price of similar applications in the list of applications to be selected. In this embodiment, when the index value of the application to be selected is scored, additional attributes such as the price of the application are combined.

[0105] Furthermore, in step 401, based on the evaluation results, determining the first evaluation score of each indicator of each mandatory application in the mandatory application list under the indicator evaluation system also includes:

[0106] Step 411: Determine, based on the evaluation result, the index value and index score of each mandatory application in the mandatory application list under a first index; the first index is any one of the various indicators under the index evaluation system;

[0107] Step 421: Obtain a preset indicator weight for the first indicator, and weight the indicator value of the first indicator based on the indicator weight to obtain a weighted indicator value;

[0108] Step 431 : Calculate the sum of the weighted index value and the index score of the first index to obtain a first evaluation score of each mandatory application in the mandatory application list under the first index.

[0109] When calculating the evaluation scores of each required application in the required application list under the indicator evaluation system, first determine the indicator value and indicator score of each required application in the selected application list under the first indicator based on the evaluation results. The first indicator is any one of the indicators under the indicator evaluation system.

[0110] Furthermore, the indicator weight preset for the first indicator is obtained, and the indicator value of the first indicator is weighted based on the indicator weight to obtain a weighted indicator value. The weighted indicator value and the indicator score of the first indicator are then summed to obtain the first evaluation score for each required application in the required application list under the first indicator. In this manner, the first evaluation score for each indicator in the indicator evaluation system can be calculated for each required application.

[0111] For example, It is the score of the required application a under the first indicator I. It is the index value of the mandatory application a under the first index I, The indicator weight of the first indicator I is a configurable constant coefficient, and the first evaluation score of a under the first indicator must be applied. It can be calculated according to the following formula 2:

[0112] ; (2)

[0113] For the application combination solution, its solution score is the sum of all indicator evaluation scores under the indicator evaluation system. Therefore, the application combination solution , if the indicator evaluation score of any indicator is greater than its full score, the evaluation score of the indicator is set to the full score, that is, ,but .

[0114] Based on this, in step 406, calculating the solution score of the application combination solution may also include:

[0115] Step 416, determining a second evaluation score for each indicator of the target application under the indicator evaluation system based on the evaluation result;

[0116] Step 426 , calculating a target evaluation score for each indicator of the application combination solution under the indicator evaluation system based on the first evaluation score and the second evaluation score;

[0117] Step 436: sum the target evaluation scores to obtain a solution score for the application combination solution.

[0118] The second evaluation score of each indicator of the target application under the indicator evaluation system is determined based on the evaluation results. The calculation method of the second evaluation score can be the same as the calculation method of the first evaluation score, which will not be repeated here.

[0119] Furthermore, based on the first and second evaluation scores, a target evaluation score for each indicator of the application combination solution under the indicator evaluation system is calculated. The first and second evaluation scores for the same indicator under the indicator evaluation system are added together to obtain the target evaluation score for that indicator. In other words, the evaluation scores for the same indicator under the indicator evaluation system for different applications in the application combination solution are added together to obtain the target evaluation score for the same indicator. The target evaluation scores for each indicator are then summed to obtain the solution score for the application combination solution.

[0120] Optionally, the full score difference of each indicator is calculated according to the following formula 3:

[0121] ; (3)

[0122] in, Indicates the full score difference of indicator I, represents the evaluation score of indicator I, Indicates the full score of indicator I.

[0123] Optionally, when selecting a target application from the candidate applications in the candidate application list, sort the various indicators under the indicator evaluation system in descending order by full score difference, then select indicator j with the largest full score difference according to the sorted order, and then calculate the recommended score for each candidate application under indicator j. Among them, the indicator with the largest full score difference is the part that needs to be improved in the digital application. Based on the recommended score of the candidate application under the indicator with the largest full score difference, the candidate application with the largest recommended score is selected as the target application and combined with the required application. The resulting application combination solution can improve the indicator with the largest full score difference, thereby improving the digitalization effect.

[0124] Optionally, when determining whether the application combination solution meets the constraint conditions, the application combination solution score is compared with the score threshold preset in the constraint conditions. Compare, if the solution scores If the application combination plan satisfies the constraint condition, otherwise, the application combination plan does not satisfy the constraint condition. The score threshold in the constraint condition is the target score of the application combination plan set according to user needs.

[0125] It should be noted that for indicator j with the largest full score difference, if there is no target application that meets the constraint conditions in the list of candidate applications, indicator j will be marked as an invalid indicator. Then, according to the sorting order of the full score difference of each indicator under the indicator evaluation system, the next indicator will be selected for screening until the generated application combination plan meets the constraint conditions.

[0126] In this embodiment, in order to solve the problem that users cannot reasonably select digital applications based on their own needs among a wide variety of digital applications with different functions, the user's existing applications are evaluated and analyzed through an indicator evaluation system to determine the required applications and candidate applications required by the user. Then, based on the configurable constraints in the user's needs, the target applications that meet the constraints are screened out from the candidate applications, and combined with the required applications to generate an application combination plan and recommend it to the user. This is conducive to improving the user's selection efficiency of digital applications and meeting the user's digital construction needs.

[0127] Furthermore, when screening target applications that meet the constraints from the candidate applications, the recommendation score of each candidate application under the indicator with the largest gap between the expected target and the user needs under the required application combination scheme is calculated, thereby screening out the applications that contribute most to the improvement of the indicator, improving the adaptability of the selected digital applications to user needs, and the rationality of the combination scheme of digital applications, which is conducive to reducing the user's investment cost in digital applications and increasing the user's digital benefits.

[0128] The digital application recommendation device provided by the present invention is described below. The digital application recommendation device described below and the digital application recommendation method described above can be referenced to each other.

[0129] Reference Figure 3 , the digital application recommendation device provided by the embodiment of the present invention includes:

[0130] The indicator evaluation module 10 is used to obtain application basic data of the user's existing applications, evaluate and analyze the application basic data based on a preset indicator evaluation system, and obtain an evaluation result of the existing applications;

[0131] A rule determination module 20 is configured to determine a solution recommendation rule based on the evaluation result; the solution recommendation rule includes a constraint condition;

[0132] An application matching module 30 is configured to query and match applications in a preset application resource pool according to the solution recommendation rules to obtain a mandatory application list and a candidate application list;

[0133] The application recommendation module 40 is configured to filter out target applications from the candidate application list based on the constraint conditions, add the target applications to the mandatory application list, combine the target applications with the mandatory applications in the mandatory application list, obtain an application combination solution, and recommend the application combination solution to the user.

[0134] In one embodiment, the application recommendation module 40 is further configured to:

[0135] Determining, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system;

[0136] Calculate the difference in full scores of each indicator in the indicator evaluation system for each mandatory application in the mandatory application list based on the first evaluation score and the preset full score of each indicator in the indicator evaluation system;

[0137] Determine the target indicator with the largest full score difference, and calculate the recommendation score of each candidate application in the candidate application list under the target indicator;

[0138] sorting the candidate applications in the candidate application list in descending order according to the recommendation score;

[0139] selecting a target application according to the sorting order of the candidate applications in the candidate application list, and combining the target application with the required applications in the required application list to obtain an application combination solution; the target application is a candidate application marked as unselected;

[0140] Calculating a solution score of the application combination solution, and determining whether the application combination solution satisfies the constraint condition according to the solution score;

[0141] If the application solution does not satisfy the constraint condition, the target application is removed from the list of candidate applications, and the process returns to and executes the step of selecting the target application according to the sorting order of the candidate applications in the list of candidate applications;

[0142] If the application combination scheme meets the constraint conditions, the target application is marked as selected and added to the required application list, and the step of selecting the target application according to the sorting order of the candidate applications in the candidate application list is returned and executed until the target application is the last candidate application in the candidate application list, or the number of target applications added to the required application list reaches the preset number of applications.

[0143] In one embodiment, the application recommendation module 40 is further configured to:

[0144] Determining a second evaluation score for each indicator of the target application under the indicator evaluation system according to the evaluation result;

[0145] Calculating target evaluation scores for each indicator of the application combination solution under the indicator evaluation system based on the first evaluation score and the second evaluation score;

[0146] The target evaluation scores are summed to obtain a solution score of the application combination solution.

[0147] In one embodiment, the application recommendation module 40 is further configured to:

[0148] Determining, based on the evaluation results, an indicator value and an indicator score for each mandatory application in the mandatory application list under a first indicator; the first indicator being any one of the indicators under the indicator evaluation system;

[0149] Obtaining a preset indicator weight for the first indicator, and weighting the indicator value of the first indicator based on the indicator weight to obtain a weighted indicator value;

[0150] The sum of the weighted index value and the index score of the first index is calculated to obtain a first evaluation score of each mandatory application in the mandatory application list under the first index.

[0151] In one embodiment, the application recommendation module 40 is further configured to:

[0152] Determine the target indicator value of each candidate application in the candidate application list under the target indicator according to the evaluation result, obtain the recommendation weight of each candidate application in the candidate application list, and the target full score value preset for the target indicator;

[0153] The ratio of the target indicator value to the target full score value is calculated, and the ratio and the recommendation weight are weightedly summed to obtain the recommendation score of each candidate application in the candidate application list under the target indicator.

[0154] In one embodiment, the application matching module 30 is further configured to:

[0155] According to the mandatory application configuration in the solution recommendation rule, query and match the applications in the preset application resource pool to obtain an initial mandatory application list;

[0156] Deduplicating the initial mandatory application list based on the existing applications to obtain a mandatory application list;

[0157] According to the optional application configuration in the solution recommendation rule, query and match the applications in the application resource pool to obtain an initial list of candidate applications;

[0158] The initial list of candidate applications is deduplicated based on the existing applications to obtain a list of candidate applications.

[0159] In one embodiment, the indicator evaluation module 10 is further configured to:

[0160] Based on the finest-grained indicator in the preset indicator evaluation system, the application basic data is evaluated and analyzed to determine the indicator value of the finest-grained indicator;

[0161] Determining an indicator score of the finest-grained indicator according to the indicator value of the finest-grained indicator;

[0162] Based on the indicator value and indicator score of the finest-grained indicator, the multi-level indicators in the indicator evaluation system are evaluated step by step to obtain the evaluation results of the existing application; wherein, the indicator value of any N-th level indicator in the indicator evaluation system is the sum of the indicator values ​​of the N+1-th level indicators under the N-th level indicator, and the indicator score of the N-th level indicator is the sum of the indicator scores of the N+1-th level indicators under the N-th level indicator.

[0163] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the steps of the digital application recommendation method, for example, including:

[0164] Obtaining basic application data of the user's existing applications, and evaluating and analyzing the basic application data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications;

[0165] Determine a solution recommendation rule based on the evaluation results; the solution recommendation rule includes constraints;

[0166] According to the solution recommendation rules, the applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications;

[0167] Based on the constraint condition, a target application is screened from the candidate application list, added to the mandatory application list, and combined with each mandatory application in the mandatory application list to obtain an application combination solution, and the application combination solution is recommended to the user.

[0168] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0169] In another aspect, the present invention further provides a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the digital application recommendation method provided by the above methods, for example, including:

[0170] Obtaining basic application data of the user's existing applications, and evaluating and analyzing the basic application data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications;

[0171] Determine a solution recommendation rule based on the evaluation results; the solution recommendation rule includes constraints;

[0172] According to the solution recommendation rules, the applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications;

[0173] Based on the constraint condition, a target application is screened from the candidate application list, added to the mandatory application list, and combined with each mandatory application in the mandatory application list to obtain an application combination solution, and the application combination solution is recommended to the user.

[0174] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the digital application recommendation method provided by the above methods are implemented, for example, including:

[0175] Obtaining basic application data of the user's existing applications, and evaluating and analyzing the basic application data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications;

[0176] Determine a solution recommendation rule based on the evaluation results; the solution recommendation rule includes constraints;

[0177] According to the solution recommendation rules, the applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications;

[0178] Based on the constraint condition, a target application is screened from the candidate application list, added to the mandatory application list, and combined with each mandatory application in the mandatory application list to obtain an application combination solution, and the application combination solution is recommended to the user.

[0179] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0180] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A digital application recommendation method, characterized in that: include: Obtaining basic application data of the user's existing applications, and evaluating and analyzing the basic application data based on a preset indicator evaluation system to obtain an evaluation result of the existing applications; Determine a solution recommendation rule based on the evaluation results; the solution recommendation rule includes constraints; According to the solution recommendation rules, the applications in the preset application resource pool are queried and matched to obtain a list of required applications and a list of candidate applications; Based on the constraint condition, a target application is screened from the list of candidate applications, added to the list of mandatory applications, and combined with each mandatory application in the list of mandatory applications to obtain an application combination solution, and the application combination solution is recommended to the user; The step of screening a target application from the candidate application list based on the constraint condition, adding the target application to the mandatory application list, and combining the target application with each mandatory application in the mandatory application list to obtain an application combination solution includes: Determining, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system; Calculate the difference in full scores of each indicator in the indicator evaluation system for each mandatory application in the mandatory application list based on the first evaluation score and the preset full score of each indicator in the indicator evaluation system; Determine the target indicator with the largest full score difference, and calculate the recommendation score of each candidate application in the candidate application list under the target indicator; sorting the candidate applications in the candidate application list in descending order according to the recommendation score; selecting a target application according to the sorting order of the candidate applications in the candidate application list, and combining the target application with the required applications in the required application list to obtain an application combination solution; the target application is a candidate application marked as unselected; Calculating a solution score of the application combination solution, and determining whether the application combination solution satisfies the constraint condition according to the solution score; If the application solution does not satisfy the constraint condition, the target application is removed from the list of candidate applications, and the process returns to and executes the step of selecting the target application according to the sorting order of the candidate applications in the list of candidate applications; If the application combination scheme meets the constraint conditions, the target application is marked as selected and added to the required application list, and the step of selecting the target application according to the sorting order of the candidate applications in the candidate application list is returned and executed until the target application is the last candidate application in the candidate application list, or the number of target applications added to the required application list reaches the preset number of applications.

2. The digital application recommendation method according to claim 1, characterized in that: The calculating of the solution score of the application combination solution includes: Determining a second evaluation score for each indicator of the target application under the indicator evaluation system according to the evaluation result; Calculating target evaluation scores for each indicator of the application combination solution under the indicator evaluation system based on the first evaluation score and the second evaluation score; The target evaluation scores are summed to obtain a solution score of the application combination solution.

3. The digital application recommendation method according to claim 1, characterized in that: Determining, based on the evaluation results, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system includes: Determining, based on the evaluation results, an indicator value and an indicator score for each mandatory application in the mandatory application list under a first indicator; the first indicator being any one of the indicators under the indicator evaluation system; Obtaining a preset indicator weight for the first indicator, and weighting the indicator value of the first indicator based on the indicator weight to obtain a weighted indicator value; The sum of the weighted index value and the index score of the first index is calculated to obtain a first evaluation score of each mandatory application in the mandatory application list under the first index.

4. The digital application recommendation method according to claim 1, characterized in that: The calculating of the recommendation score of each candidate application in the candidate application list under the target indicator includes: Determine the target indicator value of each candidate application in the candidate application list under the target indicator according to the evaluation result, obtain the recommendation weight of each candidate application in the candidate application list, and the target full score value preset for the target indicator; The ratio of the target indicator value to the target full score value is calculated, and the ratio and the recommendation weight are weightedly summed to obtain the recommendation score of each candidate application in the candidate application list under the target indicator.

5. The digital application recommendation method according to claim 1, characterized in that: The query and matching of applications in a preset application resource pool according to the solution recommendation rule to obtain a mandatory application list and a candidate application list includes: According to the mandatory application configuration in the solution recommendation rule, query and match the applications in the preset application resource pool to obtain an initial mandatory application list; Deduplicating the initial mandatory application list based on the existing applications to obtain a mandatory application list; According to the optional application configuration in the solution recommendation rule, query and match the applications in the application resource pool to obtain an initial list of candidate applications; The initial list of candidate applications is deduplicated based on the existing applications to obtain a list of candidate applications.

6. The digital application recommendation method according to claim 1, characterized in that: The evaluation results of the existing applications are obtained by evaluating and analyzing the application basic data based on the preset indicator evaluation system, including: Based on the finest-grained indicator in the preset indicator evaluation system, the application basic data is evaluated and analyzed to determine the indicator value of the finest-grained indicator; Determining an indicator score of the finest-grained indicator according to the indicator value of the finest-grained indicator; Based on the indicator values ​​and indicator scores of the finest-grained indicators, the multi-level indicators in the indicator evaluation system are evaluated step by step to obtain an evaluation result of the existing application; wherein, the indicator value of any N-th level indicator in the indicator evaluation system is the sum of the indicator values ​​of the N+1-th level indicators under the N-th level indicator, and the indicator score of the N-th level indicator is the sum of the indicator scores of the N+1-th level indicators under the N-th level indicator.

7. A digital application recommendation device, characterized in that: include: An indicator evaluation module is used to obtain application basic data of the user's existing applications, evaluate and analyze the application basic data based on a preset indicator evaluation system, and obtain an evaluation result of the existing application; A rule determination module is used to determine a solution recommendation rule based on the evaluation result; the solution recommendation rule includes a constraint condition; An application matching module is used to query and match applications in a preset application resource pool according to the solution recommendation rules to obtain a list of required applications and a list of candidate applications; an application recommendation module, configured to, based on the constraint conditions, filter out a target application from the list of candidate applications, add the target application to the list of mandatory applications, combine the target application with each mandatory application in the list of mandatory applications, obtain an application combination solution, and recommend the application combination solution to the user; The application recommendation module is further configured to determine, based on the evaluation result, a first evaluation score for each indicator of each mandatory application in the mandatory application list under the indicator evaluation system; Calculate the difference in full scores of each indicator in the indicator evaluation system for each mandatory application in the mandatory application list based on the first evaluation score and the preset full score of each indicator in the indicator evaluation system; Determine the target indicator with the largest full score difference, and calculate the recommendation score of each candidate application in the candidate application list under the target indicator; sorting the candidate applications in the candidate application list in descending order according to the recommendation score; Selecting a target application according to the sorting order of each candidate application in the candidate application list, and combining the target application with each mandatory application in the mandatory application list to obtain an application combination solution; The target application is a candidate application marked as unselected; Calculating a solution score of the application combination solution, and determining whether the application combination solution satisfies the constraint condition according to the solution score; If the application solution does not satisfy the constraint condition, the target application is removed from the list of candidate applications, and the process returns to and executes the step of selecting the target application according to the sorting order of the candidate applications in the list of candidate applications; If the application combination scheme meets the constraint conditions, the target application is marked as selected and added to the required application list, and the step of selecting the target application according to the sorting order of the candidate applications in the candidate application list is returned and executed until the target application is the last candidate application in the candidate application list, or the number of target applications added to the required application list reaches the preset number of applications.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the digital application recommendation method according to any one of claims 1 to 6 are implemented.

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

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