System operation intelligent assistant method and system based on AI direction

Abstract

The invention belongs to the technical field of system operation, and particularly relates to a system operation intelligent assistant method and system based on an AI direction. Comprising the following steps: S1, establishing an operation process after establishing a new system; sorting out a corresponding solution initial database according to error points of the initial error point database; s2, judging whether the data information filled by the user is correct or not; s3, preferentially displaying the error points meeting the preset matching degree and the solutions; s4, entering the next step and then repeating the step S2; s5, enabling a plurality of matching results to be judged by a user; s6, performing fuzzy matching optimization on the data information filled by the user again according to a fuzzy matching result for solving the problem, so that the selection of the user reaches a preset matching degree, and updating a fuzzy matching scheme; s7, matching in a starting point database according to a result of the questionnaire; s8, the second matching result is displayed to the user, and if the problem is solved, the step S6 is executed; if the solution is not solved, executing the step S9; and S9, connecting manual guidance. The method is high in intelligent degree.

Description

Technical Field

[0001] The present invention relates to the field of system operation technology, and in particular to an AI-based system operation intelligent assistant method and system. Background Art

[0002] The description of the background technology in the present invention belongs to the related technology related to the present invention, which is only used to illustrate and facilitate the understanding of the invention content of the present invention, and should not be understood as the applicant explicitly believes or inferred that the applicant believes that it is the prior art of the present invention on the filing date of the first application.

[0003] After the company's new business system is launched, the various system operation training, daily operation instructions, and solutions to abnormal interactions with peripheral systems involved in the business system are very resource-consuming and time-consuming. Therefore, based on this demand background, we can introduce system intelligent assistants to meet the above needs and help the company solve the above problems quickly, effectively and at a low cost. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide an AI-based system operation intelligent assistant method and system. The method of the present invention adopts an artificial intelligence learning method to continuously update the database and fuzzy matching scheme to make the guidance closer to the user's needs, so that the system is continuously improved, and the guidance on user's incorrect use is more accurate, saving time and cost.

[0005] The purpose of the embodiment of the present invention is achieved through the following technical solutions:

[0006] An AI-based system operation intelligent assistant method includes the following steps:

[0007] S1. After establishing a new system, establish an operating process; predict problems for the new system and start system usage investigation and testing according to the operating process, summarize the predicted error points and the investigation and test error points to establish an initial error point database, and sort out the corresponding solution initial database based on the error points in the initial error point database;

[0008] S2. Determine whether the data filled in by the user is correct. If correct, proceed to the next step. If wrong, fuzzy match the data filled in by the user with the initial database of the error point; if there is a fuzzy matching result, execute step S3; if there is no matching result, execute step S7;

[0009] S3. Prioritize displaying the error points and solutions that meet the preset matching degree; if the user confirms that the problem is solved and correctly proceeds to the next step, execute step S4; if the user's problem is not solved, execute step S5;

[0010] S4. Repeat step S2 after entering the next step;

[0011] S5. Display multiple matching results in order of matching degree and highlight the matching points between the initial error point database and the data filled in by the user for the user to judge; if the user's problem is solved, execute step S6; if the user's problem is not solved, execute step S7;

[0012] S6. According to the fuzzy matching result of the problem, the fuzzy matching optimization is performed again on the data filled in by the user so that the user's selection reaches the preset matching degree and the fuzzy matching solution is updated;

[0013] S7. Displaying a questionnaire for the user with incorrect data filled in by the user, and after the user completes the questionnaire, matching the questionnaire in the starting point database according to the results of the questionnaire, if there is a second matching result different from the previous matching result, executing step S8; if there is no matching result, executing step S9;

[0014] S8. Display the second matching result to the user. If the problem is solved, execute step S6; if not, execute step S9;

[0015] S9. Connect to manual guidance, complete the input of data according to manual guidance and successfully enter the next step, establish new matching results according to the manual solution process, update the initial error point database and the corresponding solution database and execute step S6.

[0016] Furthermore, step S41 is also included after step S4. According to the fuzzy matching result of solving the problem, the data filled in by the user is re-optimized for fuzzy matching so that the user's choice becomes the solution with the highest matching degree, and the fuzzy matching solution is updated.

[0017] Furthermore, in step S5, if the user problem is solved, step S6 is executed; before executing step S6, step S51 is also included. The user is asked whether the matching points between the highlighted initial error point database and the data filled in by the user are correct. If not, a comparison analysis is performed between the matching points between the initial error point database and the data filled in by the user and the originally highlighted matching points, and a matching relationship is re-established based on the matching points pointed out by the user, so that the matching points pointed out by the user are displayed first.

[0018] Furthermore, step S6 also includes step S61, after updating the fuzzy matching scheme, comparing and analyzing the matching points of the initial error point database pointed out by the user and the data filled out by the user with the matching points originally highlighted, re-analyzing and fuzzy matching optimization according to the matching points pointed out by the user so that the solution selected by the user becomes the solution with the highest matching degree and the fuzzy matching scheme is updated.

[0019] Furthermore, if there is a second matching result different from the previous matching result in step S7, before executing step S8, the step of applying the problem-solving method of the questionnaire to each step of the system operation, establishing new problem points according to the questionnaire and updating the initial error point database is included.

[0020] Furthermore, in steps S6 to S9, the fuzzy matching scheme, the initial database of error points and the corresponding solution database are updated in a label manner, and the steps are as follows:

[0021] (1) storing each error point, fuzzy matching scheme and corresponding solution in the error point initial database into a tab page, wherein the tab page includes a title, a body and a tab bar;

[0022] (2) the main body stores error points, fuzzy matching schemes and corresponding solutions, and stores updated error points, fuzzy matching schemes and corresponding solutions in the form of revision annotations in the tag column, wherein the revision annotations are in the form of hyperlinks;

[0023] (3) Clicking on the hyperlink of the revision annotation generates a new tab page, which contains the new error points, fuzzy matching schemes and corresponding solutions after the revision annotation;

[0024] (4) The generated new error points, fuzzy matching schemes and corresponding solutions are stored in the main body of a new tab page, with the title set as a subpage of the original tab page title. The subpages are marked with features for distinguishing each subpage, including: time, reason for revision and number of users targeted by the revision;

[0025] (5) Repeat step (2) on each subpage.

[0026] Furthermore, after the user completes the use of the new system, a statistical analysis is performed on the data filled in by the user to find defective items in the data filled in by the user in the specific business during use. If the defective item does not reach the set standard for affecting normal work, the defective item continues to be recorded; if the defective item has reached the warning value for affecting normal work, the system operation process is re-specified based on the defective item analysis and the defective item solution is obtained to obtain a new process, and steps S1-S9 are re-executed according to the new process.

[0027] Furthermore, the monitoring system checks whether there are any matters that require approval. If so, the early warning channel reminds the user to complete the approval process of the business document.

[0028] Furthermore, the warning channels include text messages, enterprise WeChat and pop-up services.

[0029] An AI-based system operation intelligent assistant system is used to complete the above assistant method, including:

[0030] Operating system, including operating procedures and operation tutorials;

[0031] Initial database: including an initial database of error points and a corresponding solution database, used to store initial error points and corresponding solutions sorted out according to the error points in the initial error point database;

[0032] The judgment module is used to judge whether the user operation process is correct;

[0033] Guidance module: if the user operates incorrectly, it will analyze the error and provide solutions for the user to choose;

[0034] The updating module is configured to update the initial error point database and the corresponding solution database according to the user's feedback in the guidance to form a new database.

[0035] The embodiments of the present invention have the following beneficial effects:

[0036] The method of the present invention adopts an artificial intelligence learning method to continuously update the database and fuzzy matching scheme to make the guidance closer to the user's needs, so that the system is continuously improved, and the guidance on user's incorrect use is more accurate, saving time and cost. DETAILED DESCRIPTION

[0037] The present application is further described below in conjunction with embodiments.

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, in the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. Different embodiments can be replaced or combined, and for ordinary technicians in this field, other implementation methods can be obtained based on these embodiments without creative work.

[0039] An AI-based system operation intelligent assistant method includes the following steps:

[0040] S1. After establishing a new system, establish an operating process; predict problems for the new system and start system usage investigation and testing according to the operating process, summarize the predicted error points and the investigation and test error points to establish an initial error point database, and sort out the corresponding solution initial database based on the error points in the initial error point database;

[0041] There are two initial ways to generate error points here. One is based on intelligent prediction, and the other is investigation and testing before the system is officially launched. The two methods combine to get a more comprehensive picture of possible error points, and provide corresponding solutions for the above possible error points, which is convenient for dealing with problems that may arise in subsequent system use.

[0042] S2. Determine whether the data filled in by the user is correct. If correct, proceed to the next step. If wrong, fuzzy match the data filled in by the user with the initial database of the error point; if there is a fuzzy matching result, execute step S3; if there is no matching result, execute step S7;

[0043] Based on the information filled in by the user, determine whether it is the required item. If it is, proceed to the next item to fill in. If not, the user needs to be guided. The basis of the guidance is the initial database of error points and the database of solutions established earlier. For example, in a certain step, the customer entered a mobile phone number when he needed to enter an ID number. This will cause the system to report an error, and the system will guide the user to fill in the ID number. Of course, this is just an extremely simple example, which is given for the sake of ease of understanding. There are often many complex error points in actual operations, which will not be elaborated here.

[0044] S3. Prioritize displaying error points and solutions that meet the preset matching degree; if the user confirms that the problem is solved and correctly proceeds to the next step, execute step S4; if the user's problem is not solved, execute step S5;

[0045] What needs to be understood here is that the preset matching degree is not necessarily a numerical matching degree, but may also be the top few matching degrees, such as the top 3 error points and solutions of the matching degree; the reason for not displaying all of them at the beginning is to facilitate user browsing. Too many display items may easily cause trouble to users and waste time. Generally speaking, the number of displays is derived from a large amount of historical data. When a considerable number of solutions are displayed, the problems in most cases can be solved. It can also be said that if the problems cannot be solved by displaying the solutions in the above proportion, the situations that can be solved when all are displayed are relatively few. Therefore, displaying a considerable number of solutions is the best way to deal with it. If displaying a considerable number of solutions cannot solve the problem, then it will be optimized in subsequent steps so that the problem can be solved within a considerable number of display items.

[0046] S4. Repeat step S2 after entering the next step; each step is started according to the above process.

[0047] S5. Display multiple matching results in order of matching degree and highlight the matching points between the initial error point database and the data filled in by the user for the user to judge; if the user's problem is solved, execute step S6; if the user's problem is not solved, execute step S7;

[0048] There are two purposes of highlighting. One is to make it clear to users at a glance. Second, if the highlighted part is not what the user thinks, it can be corrected in time according to user needs, laying the foundation for the update and practicality of the database.

[0049] S6. According to the fuzzy matching result of the problem, the fuzzy matching optimization is performed again on the data filled in by the user so that the user's selection reaches the preset matching degree and the fuzzy matching solution is updated;

[0050] If displaying a considerable number of results fails to solve the problem, but increasing the number of results displayed solves the problem, then the database is complete, but there is a deviation between the fuzzy matching rules and the required results. The fuzzy matching scheme needs to be reversed based on the required results so that the required data is displayed first.

[0051] S7. Displaying a questionnaire for the user with incorrect data filled in by the user, and after the user completes the questionnaire, matching the questionnaire in the starting point database according to the results of the questionnaire, if there is a second matching result different from the previous matching result, executing step S8; if there is no matching result, executing step S9;

[0052] When all solutions still cannot solve the problem, new possibly suitable data can be matched through questionnaires. At this time, it means that there are big problems with the original fuzzy matching, and it is necessary to make reverse corrections based on the data selected after the questionnaire. After the correction, when encountering such problems in the future, there is no need to go to the questionnaire step, making the system more intelligent.

[0053] S8. Display the second matching result to the user. If the problem is solved, execute step S6; if not, execute step S9;

[0054] S9. Connect to manual guidance, complete the input of data according to manual guidance and successfully enter the next step, establish new matching results according to the manual solution process, update the initial error point database and the corresponding solution database and execute step S6.

[0055] The solution obtained after manual guidance is analyzed manually and intelligently, and the database and fuzzy matching scheme are updated to make the system more perfect.

[0056] The method of the present invention adopts an artificial intelligence learning method to continuously update the database and fuzzy matching scheme to make the guidance closer to the user's needs, so that the system is continuously improved, and the guidance on user's incorrect use is more accurate, saving time and cost.

[0057] In some embodiments of the present invention, step S4 also includes step S41. According to the fuzzy matching result of solving the problem, the data filled in by the user is re-optimized for fuzzy matching so that the user's choice becomes the solution with the highest matching degree, and the fuzzy matching solution is updated.

[0058] Making the result selected by the user the best result is an optimization of system usage. A continuously optimized system can save time in subsequent use.

[0059] In some embodiments of the present invention, in step S5, if the user problem is solved, step S6 is executed; before executing step S6, step S51 is also included. The user is asked whether the matching points between the highlighted initial error point database and the data filled in by the user are correct. If not, a comparison analysis is performed between the matching points between the initial error point database and the data filled in by the user and the originally highlighted matching points, and a matching relationship is re-established based on the matching points pointed out by the user, so that the matching points pointed out by the user are displayed first.

[0060] In some embodiments of the present invention, step S61 is also included after step S6. After the fuzzy matching scheme is updated, a comparison analysis is performed between the initial error point database pointed out by the user and the matching points of the data filled out by the user and the originally highlighted matching points, and the fuzzy matching optimization is re-analyzed and performed according to the matching points pointed out by the user so that the solution selected by the user becomes the solution with the highest matching degree and the fuzzy matching scheme is updated.

[0061] The highlighted points may be the core points of the fuzzy matching, and the user's error correction of the highlighted points is an indirect feedback to the fuzzy matching solution, which is conducive to the improvement of the system.

[0062] In some embodiments of the present invention, if there is a second matching result different from the previous matching result in step S7, before executing step S8, the following step is also included: applying the problem-solving method of the questionnaire to each step of the system operation, establishing new problem points based on the questionnaire and updating the initial error point database.

[0063] When the questionnaire form is enabled, there is a second result that can be solved, which means that there is a solution in the database, but the previous fuzzy matching failed to give a suitable result. This means that there may be parts in the questionnaire that were not included in the previous fuzzy matching, and these parts are quite important. At this time, all fuzzy matching solutions should be revised in combination with the questionnaire.

[0064] In some embodiments of the present invention, the updating of the fuzzy matching scheme, the updating of the initial database of error points and the corresponding solution database in steps S6 to S9 is updated in a label manner, and the steps are as follows:

[0065] (1) storing each error point, fuzzy matching scheme and corresponding solution in the error point initial database into a tab page, wherein the tab page includes a title, a body and a tab bar;

[0066] (2) the main body stores error points, fuzzy matching schemes and corresponding solutions, and stores updated error points, fuzzy matching schemes and corresponding solutions in the form of revision annotations in the tag column, wherein the revision annotations are in the form of hyperlinks;

[0067] (3) Clicking on the hyperlink of the revision annotation generates a new tab page, which contains the new error points, fuzzy matching schemes and corresponding solutions after the revision annotation;

[0068] (4) The generated new error points, fuzzy matching schemes and corresponding solutions are stored in the main body of a new tab page, with the title set as a subpage of the original tab page title. The subpages are marked with features for distinguishing each subpage, including: time, reason for revision and number of users targeted by the revision;

[0069] (5) Repeat step (2) on each subpage.

[0070] The advantage of this storage method is that each version is stored and related revisions are stored through revision mode, so that related items are linked together. After finding a tab, the pages before and after it can be accessed through hyperlinks to obtain the complete pages, so that the revision records are saved continuously.

[0071] In some embodiments of the present invention, after the user completes the use of the new system, a statistical analysis is performed on the data filled in by the user to find defective items in the data filled in by the user in the specific business during use. If the defective item does not reach the set standard for affecting normal work, the defective item continues to be recorded; if the defective item has reached the warning value that affects normal work, the system operation process is re-specified based on the defective item analysis and the defective item solution is obtained to obtain a new process, and steps S1-S9 are re-executed according to the new process.

[0072] In some embodiments of the present invention, it is monitored whether there are matters that need approval in the system. If so, the user is reminded through an early warning channel to complete the approval process of the business document.

[0073] In some embodiments of the present invention, the warning channel includes text messages, enterprise WeChat and pop-up services.

[0074] An AI-based system operation intelligent assistant system is used to complete the above assistant method, including:

[0075] Operating system, including operating procedures and operation tutorials;

[0076] Initial database: including an initial database of error points and a corresponding solution database, used to store initial error points and corresponding solutions sorted out according to the error points in the initial error point database;

[0077] The judgment module is used to judge whether the user operation process is correct;

[0078] Guidance module: if the user operates incorrectly, it will analyze the error and provide solutions for the user to choose;

[0079] The updating module is configured to update the initial error point database and the corresponding solution database according to the user's feedback in the guidance to form a new database.

[0080] It should be noted that the above embodiments can be freely combined as needed. The above description is only the preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A system operation intelligent assistant method based on AI, characterized in that: The steps include: S1. After establishing a new system, establish an operating process; predict problems for the new system and start system usage investigation and testing according to the operating process, summarize the predicted error points and the investigation and test error points to establish an initial error point database, and sort out the corresponding solution initial database based on the error points in the initial error point database; S2. Determine whether the data filled in by the user is correct. If correct, proceed to the next step. If wrong, fuzzy match the data filled in by the user with the initial database of the error point; if there is a fuzzy match result, execute step S3; If there is no matching result, execute step S7; S3. Prioritize displaying the error points and solutions that meet the preset matching degree; if the user confirms that the problem is solved and correctly proceeds to the next step, execute step S4; if the user's problem is not solved, execute step S5; S4. Repeat step S2 after entering the next step; S5. Display multiple matching results in order of matching degree and highlight the matching points of the initial error point database and the data filled in by the user for the user to judge; If the user problem is solved, execute step S6; if the user problem is not solved, execute step S7; S6. According to the fuzzy matching result of the problem, the fuzzy matching optimization is performed again on the data filled in by the user so that the user's selection reaches the preset matching degree and the fuzzy matching solution is updated; S7. Displaying a questionnaire for the wrong data filled in by the user to the user, after the user completes the questionnaire, matching in the starting point database according to the results of the questionnaire, if there is a second matching result different from the previous matching result, executing step S8; If there is no matching result, execute step S9; S8. Display the second matching result to the user. If the problem is solved, execute step S6; If the problem is not solved, execute step S9; S9. Connect to manual guidance, complete the input of data according to manual guidance and successfully enter the next step, establish new matching results according to the manual solution process, update the initial error point database and the corresponding solution database and execute step S6.

2. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: Step S4 also includes step S41. According to the fuzzy matching result of solving the problem, the data filled in by the user is re-optimized for fuzzy matching so that the user's choice becomes the solution with the highest matching degree, and the fuzzy matching solution is updated.

3. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: In step S5, if the user problem is solved, step S6 is executed; before executing step S6, step S51 is also included. The user is asked whether the matching points between the highlighted initial error point database and the data filled in by the user are correct. If not, a comparison analysis is performed between the matching points between the initial error point database and the data filled in by the user and the originally highlighted matching points, and a matching relationship is re-established based on the matching points pointed out by the user, so that the matching points pointed out by the user are displayed first.

4. The AI-based system operation intelligent assistant method according to claim 3, characterized in that: Step S6 also includes step S61, after updating the fuzzy matching scheme, comparing and analyzing the matching points of the initial error point database pointed out by the user and the data filled out by the user with the matching points originally highlighted, re-analyzing and fuzzy matching optimization according to the matching points pointed out by the user so that the solution selected by the user becomes the solution with the highest matching degree and the fuzzy matching scheme is updated.

5. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: If there is a second matching result different from the previous matching result in step S7, before executing step S8, the step of applying the problem-solving method of the questionnaire to each step of the system operation, establishing new problem points according to the questionnaire and updating the initial error point database is also included.

6. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: In steps S6 to S9, the fuzzy matching scheme, the initial database of error points and the corresponding solution database are updated in a label manner, and the steps are as follows: (1) storing each error point, fuzzy matching scheme and corresponding solution in the error point initial database into a tab page, wherein the tab page includes a title, a body and a tab bar; (2) the main body stores error points, fuzzy matching schemes and corresponding solutions, and stores updated error points, fuzzy matching schemes and corresponding solutions in the form of revision annotations in the tag column, wherein the revision annotations are in the form of hyperlinks; (3) Clicking on the hyperlink of the revision annotation generates a new tab page, which contains the new error points, fuzzy matching schemes and corresponding solutions after the revision annotation; (4) The generated new error points, fuzzy matching schemes and corresponding solutions are stored in the main body of a new tab page, with the title set as a subpage of the original tab page title. The subpages are marked with features for distinguishing each subpage, including: time, reason for revision and number of users targeted by the revision; (5) Repeat step (2) on each subpage.

7. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: After the user has finished using the new system, the data entered by the user is statistically analyzed to find defects in the data entered by the user in specific business. If the defective items do not meet the set standards that affect normal work, the defective items will continue to be recorded; If the defect item has reached the warning value that affects normal operation, the system operation process is re-specified to obtain a new process based on the defect item analysis and defect item solution, and steps S1-S9 are re-executed according to the new process.

8. The AI-based system operation intelligent assistant method according to claim 1, characterized in that: Monitor whether there are matters that require approval in the system. If so, remind the user through the early warning channel to complete the approval process of the business document.

9. The AI-based system operation intelligent assistant method according to claim 7, characterized in that: The warning channels include SMS, enterprise WeChat and pop-up services.

10. An AI-based system operation intelligent assistant system, characterized in that: The assistant method for completing any one of claims 1 to 9 comprises: Operating system, including operating procedures and operation tutorials; Initial database: including an initial database of error points and a corresponding solution database, used to store initial error points and corresponding solutions sorted out according to the error points in the initial error point database; The judgment module is used to judge whether the user operation process is correct; Guidance module: if the user operates incorrectly, it will analyze the error and provide solutions for the user to choose; The updating module is configured to update the initial error point database and the corresponding solution database according to the user's feedback in the guidance to form a new database.