User operation guidance method, device, equipment and medium based on Naive Bayes

Abstract

The embodiment of the present invention discloses a user operation guidance method, device, equipment and medium based on Naive Bayes. The present invention relates to the field of artificial intelligence technology, which includes: if a user login instruction is received, the user login information is obtained according to the user login instruction, and whether the current user meets the preset guidance conditions is determined according to the user login information; if the current user meets the preset guidance conditions, the behavior breakpoint data corresponding to the current user is obtained from the preset database according to the user login information; according to the behavior breakpoint data, the target operation step is determined by the preset step decision model, and the target operation template is matched from the preset operation template library by the preset similarity matching method according to the target operation step; according to the target operation step and the target operation template, the voice operation step guidance is performed on the current user through the speech synthesis technology. The embodiment of the present invention not only saves time and effort, but also reduces costs and improves operating efficiency.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a user operation guidance method, device, equipment and medium based on Naive Bayes. Background Art

[0002] Currently, most financial products on the market provide operation step prompt documents, or manually intervene to assist customers to complete the operation according to the customer's operation behavior. Although the above two implementation solutions can solve customer problems, they have the following disadvantages: first, customers need to spend a lot of energy and time to familiarize themselves with the operation step documents; second, a large number of manual assistance is required to complete the work, which is time-consuming and labor-intensive, and the operation efficiency is low. Summary of the invention

[0003] The embodiments of the present invention provide a user operation guidance method, device, equipment and medium based on Naive Bayes, aiming to solve the problems of time-consuming, labor-intensive and low operation efficiency in the existing user operation guidance process.

[0004] In a first aspect, an embodiment of the present invention provides a user operation guidance method based on naive Bayes, which is applied to a client and includes:

[0005] If a user login instruction is received, obtaining user login information according to the user login instruction, and judging whether the current user meets the preset guidance condition according to the user login information;

[0006] If the current user meets the preset guidance condition, then obtaining the behavior breakpoint data corresponding to the current user from a preset database according to the user login information;

[0007] Determine the target operation step through a preset step decision model according to the behavior breakpoint data, and match the target operation template from a preset operation template library through a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps;

[0008] According to the target operation steps and the target operation template, the current user is guided through voice operation steps through speech synthesis technology.

[0009] In a second aspect, an embodiment of the present invention further provides a user operation guiding device based on naive Bayes, which includes:

[0010] a judgment unit, configured to, if receiving a user login instruction, obtain user login information according to the user login instruction, and judge whether the current user meets the preset guidance condition according to the user login information;

[0011] an acquisition unit, configured to acquire behavior breakpoint data corresponding to the current user from a preset database according to the user login information if the current user meets the preset guidance condition;

[0012] A decision unit, configured to determine a target operation step through a preset step decision model according to the behavior breakpoint data, and to match a target operation template from a preset operation template library through a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps;

[0013] A guiding unit is used to guide the current user through voice operation steps according to the target operation steps and the target operation template through speech synthesis technology.

[0014] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program can implement the above method when executed by a processor.

[0016] The embodiment of the present invention provides a user operation guidance method, device, equipment and medium based on Naive Bayes. The method includes: if a user login instruction is received, user login information is obtained according to the user login instruction, and whether the current user meets the preset guidance condition is determined according to the user login information; if the current user meets the preset guidance condition, the behavior breakpoint data corresponding to the current user is obtained from the preset database according to the user login information; according to the behavior breakpoint data, the target operation step is determined by a preset step decision model, and according to the target operation step, the target operation template is matched from the preset operation template library by a preset similarity matching method, wherein the preset step decision model is obtained by training the Naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps; according to the target operation step and the target operation template, the voice operation step guidance is performed on the current user through voice synthesis technology. The technical solution of the embodiment of the present invention uses behavioral breakpoint data and operation steps to train the naive Bayes algorithm model to obtain a preset step decision model. The preset step decision model can accurately predict the user's operation behavior, and use speech synthesis technology to guide the voice operation steps without manual assistance or following the step operation documents. It not only saves time and effort, but also reduces costs and improves operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0018] Figure 1 A flowchart of a user operation guidance method based on Naive Bayes provided in one embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a sub-process of a user operation guidance method based on Naive Bayes provided in one embodiment of the present invention;

[0020] Figure 3 A schematic diagram of a sub-process of a user operation guidance method based on Naive Bayes provided in one embodiment of the present invention;

[0021] Figure 4 A schematic diagram of a sub-process of a user operation guidance method based on Naive Bayes provided in one embodiment of the present invention;

[0022] Figure 5 A flowchart of a user operation guidance method based on Naive Bayes provided by another embodiment of the present invention;

[0023] Figure 6 A schematic block diagram of a user operation guiding device based on Naive Bayes provided by an embodiment of the present invention; and

[0024] Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0028] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0030] See also Figure 1 , Figure 1 1 is a flow chart of a user operation guidance method based on naive Bayesian provided by an embodiment of the present invention. The user operation guidance method based on naive Bayesian in the embodiment of the present invention can be applied to a client, and the user operation guidance method based on naive Bayesian is implemented by an application installed on the client, which not only saves time and effort, but also reduces costs and improves operation efficiency. Figure 1 As shown, the method includes the following steps S100-S130.

[0031] S100: If a user login instruction is received, obtain user login information according to the user login instruction, and determine whether the current user meets a preset guidance condition according to the user login information.

[0032] The embodiments of the present invention can be used in various scenarios where the client needs to interact with the user through the interface, such as in the shopping guide operation of financial products. In the embodiment of the present invention, the user can log in by entering the user name and password in the login interface of the APP where the financial product is located and clicking the login button. At this time, a user login instruction containing the user login information will be generated. The APP where the financial product is located receives the preset login instruction, obtains the user login information according to the preset login instruction, and determines whether the current user meets the preset guidance conditions according to the user login information. Specifically, the login information includes a user name and a password. The number of logins of the user can be determined based on the user name, and whether the current user meets the preset guidance conditions can be determined based on the number of logins. It can be understood that in actual applications, for unregistered users, user registration is performed first, and the user is determined as a new user.

[0033] See also Figure 2 In one embodiment, for example in this embodiment, the step S100 includes the following steps S101-S103.

[0034] S101, judging whether the current user is a new user according to the user login information, if the current user is not a new user, executing step S102, otherwise executing step S104;

[0035] S102, obtaining the login times of the current user from a preset database according to the login information;

[0036] S103, if the number of login times is not less than a preset number of login times, determining that the current user meets a preset guidance condition;

[0037] S104: Send a registration reminder.

[0038] In an embodiment of the present invention, it is determined whether the current user meets the preset guidance conditions according to the user login information. Specifically, firstly, a search is performed in the preset database according to the user name corresponding to the current user. If the user name does not exist in the preset database, indicating that the current user is a new user, a registration prompt is sent to the current user to remind the current user to register; if the user name exists in the preset database, indicating that the current user is not a new user but an old user, the number of logins of the current user is obtained from the preset database according to the user name, and it is determined whether the number of logins of the current user is less than the preset number of logins. If the number of logins is less than the preset number of logins, it indicates that the current user has not logged into the APP where the financial product is located a few times and is not familiar with the financial product. The current user should be allowed to operate according to the operation steps in the preset specification template to better understand the financial product, wherein the preset specification template is a template including all operation steps. It can be understood that if the number of logins is not less than the preset number of logins, it indicates that the current user has logged into the APP where the financial product is located many times and is relatively familiar with the financial product. The current user does not need to follow the operating steps in the preset specification template, and user operation guidance should be performed to skip the steps that the user is not interested in and directly enter the steps that the user is more concerned about, so that the user can complete the financial product shopping guide operation faster. That is, if the number of logins is not less than the preset number of logins, it is determined that the current user meets the preset guidance conditions and enters the user operation guidance strategy.

[0039] S110: If the current user meets the preset guidance condition, the behavior breakpoint data corresponding to the current user is obtained from a preset database according to the user login information.

[0040] In an embodiment of the present invention, if the current user meets the preset guidance condition, the behavior breakpoint data corresponding to the current user is obtained from the preset database according to the user login information, wherein the operation behavior data in which the operation time exceeds the preset operation time or the operation number exceeds the preset operation number is used as the behavior breakpoint data of the current user, and the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data. Specifically, the behavior breakpoint data corresponding to the current user can be obtained from the first preset table in the preset database according to the user name in the user login information.

[0041] S120. Determine a target operation step through a preset step decision model according to the behavior breakpoint data, and match a target operation template from a preset operation template library through a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps.

[0042] In an embodiment of the present invention, after obtaining the behavior breakpoint data, the target operation step is decided by a preset step decision model according to the behavior breakpoint data, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps. After obtaining the target operation step, the target operation template is matched from the preset operation template library according to the target operation step by a preset similarity matching method, wherein the preset similarity matching method is to first match an operation template group from the preset operation template library according to the target operation step, and then respectively calculate the similarity between the operation templates in the operation template group and the preset specification template, and select the operation template with the largest similarity value as the target operation template. For example, if there are three operation templates and one preset specification template in the preset operation template, the three operation templates are operation template 1, operation template 2, and operation template 3. Among them, the preset standard template includes steps 1 to 9; operation template 1 includes steps 1, step 2, step 4, step 6, step 8, and step 9; operation template 2 includes steps 2, step 4, step 5, step 7, and step 9; operation template 3 includes steps 3, step 4, step 5, step 8, and step 9; if the target operation step is step 2, the matched operation template group is operation template 1 and operation template 2, and the similarities between operation template 1 and operation template 2 and the preset standard template are calculated respectively. Since operation template 1 has more steps in common with the preset standard template than operation template 2, the similarity value is larger, and operation template 1 is used as the target operation template.

[0043] See also Figure 3 In one embodiment, for example in this embodiment, the step S120 includes the following steps S121-S127.

[0044] S121, obtaining sample behavior breakpoint data and sample operation steps from a preset sample library, and using the sample behavior breakpoint data and the sample operation steps as a target data set;

[0045] S122, dividing the target data set into a training data set and a verification data set according to a preset ratio;

[0046] S123, inputting the training data set into the naive Bayes algorithm model for training until a preset number of training times;

[0047] S124, inputting the verification data set into the trained naive Bayes algorithm model to obtain prediction operation steps;

[0048] S125, calculating the prediction accuracy according to the prediction operation steps and the operation steps in the verification data set, and judging whether the preset prediction accuracy exceeds the preset evaluation value, if the preset prediction accuracy exceeds the preset evaluation value, executing step S126, otherwise executing step S127;

[0049] S126. Using the trained Naive Bayes algorithm model as the step decision model.

[0050] S127, resetting the preset ratio and the preset number of training times, and returning to execute the step of dividing the target data set into a training data set and a verification data set according to the preset ratio.

[0051] In an embodiment of the present invention, sample behavior breakpoint data and sample operation steps are obtained from a preset sample library, wherein the sample operation steps are steps in a preset specification template, and each user among the sample users has corresponding operation behavior data when operating each step in the preset specification template, and the operation behavior data in which the operation time exceeds the preset operation time or the operation number exceeds the preset operation number is used as the sample behavior breakpoint data. After obtaining the sample behavior breakpoint data and the sample operation steps, the sample behavior breakpoint data and the sample operation steps are used as the target data set, and then the target data set is divided into a training data set and a verification data set according to a preset ratio, wherein the preset ratio is 7:3. It can be understood that the preset ratio can also be set to other ratio values ​​and flexibly adjusted according to actual conditions; the training data set is input into the naive Bayes algorithm model for training until a preset number of training times is reached, wherein the preset number of training times can be flexibly set according to actual training needs; after the model training is completed, the verification data set is input into the trained naive Bayes algorithm model for prediction to obtain the predicted operation steps; the prediction accuracy can be calculated by dividing the number of times the predicted operation steps are consistent with the operation steps in the verification data set by the total number of predictions, and it is determined whether the preset test accuracy exceeds the preset evaluation value; if the preset test accuracy exceeds the preset evaluation value, it indicates that the step decision model meets the step decision requirements, and the trained naive Bayes algorithm model is used as the step decision model. If the preset measurement accuracy does not exceed the preset evaluation value, it indicates that the accuracy of the step decision is low and needs to be retrained and evaluated, then the preset ratio and the preset number of training times are reset, and the process returns to step S122.

[0052] S130: According to the target operation steps and the target operation template, the current user is guided through voice operation steps by using speech synthesis technology.

[0053] In the embodiment of the present invention, after matching the target operation template, the target operation step is used as the current operation step to perform the operation, and the current user is guided through the voice operation step by voice synthesis technology according to the target operation template until the operation is completed.

[0054] See also Figure 4 In one embodiment, for example in this embodiment, the step S130 includes the following steps S131-S135.

[0055] S131, performing the target operation step as the current operation step;

[0056] S132, providing a voice prompt for the next operation step of the current operation step by using speech synthesis technology according to the target operation template;

[0057] S133, determining whether the current user performs the next operation step according to the voice prompt, if the current user performs the next operation step according to the voice prompt, executing step S134, otherwise executing step S135;

[0058] S134, taking the next operation step as the current operation step, and returning to execute step S132 until the operation is completed;

[0059] S135: Taking the current operation step as the target operation step, and returning to execute the step of matching the target operation template from a preset operation template library by a preset similarity matching method according to the target operation step.

[0060] In an embodiment of the present invention, after matching the target operation template, the target operation step is performed as the current operation step, and a voice prompt is given for the next operation step of the current operation step through speech synthesis technology according to the target operation template, wherein the speech synthesis technology converts the next operation step into speech and plays it to the user to guide the user. Then, determine whether the current user performs the next operation step according to the voice prompt. For example, assume that the target operation step is step 2, and the target operation template is operation template 1, wherein operation template 1 includes step 2, step 4, step 6, step 8, step 9, and step 10, and the next step of step 2 is step 4. If the current user performs the next operation step according to the voice prompt, that is, operates step 4, then the next operation step 4 is used as the current operation step, and the step of giving a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template is returned to be executed until the operation is completed. If the current user does not perform the next operation step according to the voice prompt, that is, does not operate step 4, but operates step 6, then the current operation step 6 is used as the target operation step, and the step of matching the target operation template from the preset operation template library through a preset similarity matching method according to the target operation step is returned to be executed.

[0061] Figure 5 The following is a flow chart of a user operation guidance method based on naive Bayes provided in another embodiment of the present invention, wherein the method is run on a client. Figure 5 As shown, in this embodiment, the method includes steps S100-S140. That is, in this embodiment, after step S100 of the above embodiment, the method further includes step S140.

[0062] S140. If the current user does not meet the preset guidance condition, the operation behavior data corresponding to the step operation performed by the current user is saved to the preset database, wherein the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data.

[0063] In an embodiment of the present invention, if the current user does not meet the preset guidance condition, the operation behavior data corresponding to the step operation performed by the current user is saved to the preset database, wherein the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data. In practical applications, the operation behavior data in the operation behavior data whose operation time exceeds the preset operation time or the number of operations exceeds the preset number of operations is used as the behavior breakpoint data of the current user, that is, the operation step corresponding to the behavior breakpoint data requires the current user to spend more time and energy, that is, the step that the current user focuses on. Understandably, the operation behavior data in the operation behavior data whose operation time does not exceed the preset operation time and the number of operations does not exceed the preset number of operations is used as the non-behavior breakpoint data of the current user, that is, the operation step corresponding to the non-behavior breakpoint data is relatively familiar to the current user and does not need to be focused on. More specifically, the behavior breakpoint data is saved to the first preset table in the preset database, and the non-behavior breakpoint data is saved to the second preset table in the preset database. In an embodiment of the present invention, the behavior breakpoint data and the non-behavior breakpoint data are separately saved in different preset tables to facilitate data acquisition and query.

[0064] It should be noted that in the embodiments of the present invention, the preset database can be a database server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms, as long as it can provide data.

[0065] Figure 6 is a schematic block diagram of a user operation guiding device 200 based on naive Bayes provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above user operation guidance method based on naive Bayes, the present invention also provides a user operation guidance device 200 based on naive Bayes. The user operation guidance device 200 based on naive Bayes includes a unit for executing the above user operation guidance method based on naive Bayes, and the device can be configured on the client. Specifically, please refer to Figure 6 The user operation guiding device 200 based on naive Bayes includes a judging unit 201 , an acquiring unit 202 , a decision unit 203 and a guiding unit 204 .

[0066] Among them, the judgment unit 201 is used to obtain user login information according to the user login instruction if a user login instruction is received, and to determine whether the current user meets the preset guidance conditions according to the user login information; the acquisition unit 202 is used to obtain the behavior breakpoint data corresponding to the current user from the preset database according to the user login information if the current user meets the preset guidance conditions; the decision unit 203 is used to decide the target operation step according to the behavior breakpoint data through a preset step decision model, and match the target operation template from the preset operation template library through a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training the naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps; the guidance unit 204 is used to guide the current user through voice operation steps through speech synthesis technology according to the target operation step and the target operation template.

[0067] In some embodiments, such as this embodiment, the judgment unit 201 includes a first judgment subunit 2011 , a first acquisition subunit 2012 , and a determination unit 2013 .

[0068] Among them, the first judgment subunit 2011 is used to judge whether the current user is a new user based on the user login information; the first acquisition subunit 2012 is used to obtain the login times of the current user from the preset database based on the login information if the current user is not a new user; the judgment unit 2013 is used to judge that the current user meets the preset guidance condition if the login times are not less than the preset login times.

[0069] In some embodiments, such as the present embodiment, the decision unit 203 includes a second acquisition subunit 2031, a division unit 2032, a training unit 2033, an input unit 2034, a calculation unit 2035, an acting unit 2036, a first return unit 2037, a matching unit 2038 and a selection unit 2039.

[0070] The second acquisition subunit 2031 is used to obtain sample behavior breakpoint data and sample operation steps from a preset sample library, and use the sample behavior breakpoint data and the sample operation steps as the target data set; the dividing unit 2032 is used to divide the target data set into a training data set and a verification data set according to a preset ratio; the training unit 2033 is used to input the training data set into a naive Bayes algorithm model for training until a preset number of training times; the input unit 2034 is used to input the verification data set into the trained naive Bayes algorithm model to obtain the predicted operation steps; the calculation unit 2035 is used to calculate the prediction accuracy according to the predicted operation steps and the operation steps in the verification data set, and to determine whether the preset accuracy exceeds the preset accuracy. Set an evaluation value; the acting unit 2036 is used to use the trained naive Bayes algorithm model as the step decision model if the preset measurement accuracy exceeds the preset evaluation value; the first returning unit 2037 is used to reset the preset ratio and the preset number of training times if the preset measurement accuracy does not exceed the preset evaluation value, and return to execute the step of dividing the target data set into a training data set and a verification data set according to the preset ratio; the matching unit 2038 is used to match an operation template group from a preset operation template library according to the target operation step; the selecting unit 2039 is used to respectively calculate the similarity between the operation templates in the operation template group and the preset specification template, and select the operation template with the largest similarity value as the target operation template.

[0071] In some embodiments, such as the present embodiment, the guiding unit 204 includes a prompting unit 2041 , a second judging subunit 2042 , a second returning unit 2043 and a third returning unit 2044 .

[0072] Among them, the prompt unit 2041 is used to take the target operation step as the current operation step to perform the operation, and to give a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template; the second judgment subunit 2042 is used to judge whether the current user performs the next operation step according to the voice prompt; the second return unit 2043 is used to take the next operation step as the current operation step if the current user performs the next operation step according to the voice prompt, and return to execute the step of giving a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template until the operation is completed; the third return unit 2044 is used to take the current operation step as the target operation step if the current user does not perform the next operation step according to the voice prompt, and return to execute the step of matching the target operation template from the preset operation template library according to the target operation step through a preset similarity matching method.

[0073] In some embodiments, such as this embodiment, the device 200 further includes a storage unit 205 .

[0074] The saving unit 205 is used to save the operation behavior data corresponding to the step operation performed by the current user to the preset database if the current user does not meet the preset guidance condition, wherein the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data.

[0075] The specific implementation of the user operation guidance device 200 based on naive Bayes in the embodiment of the present invention corresponds to the above-mentioned user operation guidance method based on naive Bayes, which will not be repeated here.

[0076] The user operation guiding device based on naive Bayes can be implemented in the form of a computer program. Figure 7 Runs on the computer device shown.

[0077] See also Figure 7 , Figure 7 1 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device 900 is a client, and the client is an intelligent terminal device such as a laptop, a notebook computer, a desktop computer, etc.

[0078] See also Figure 7 The computer device 900 includes a processor 902 , a memory and a network interface 905 connected via a system bus 901 , wherein the memory may include a storage medium 903 and an internal memory 904 .

[0079] The storage medium 903 may store an operating system 9031 and a computer program 9032. When the computer program 9032 is executed, the processor 902 may execute a user operation guiding method based on naive Bayes.

[0080] The processor 902 is used to provide computing and control capabilities to support the operation of the entire computer device 900 .

[0081] The internal memory 904 provides an environment for the operation of the computer program 9032 in the storage medium 903. When the computer program 9032 is executed by the processor 902, the processor 902 can execute a user operation guidance method based on naive Bayes.

[0082] The network interface 905 is used to communicate with other devices over the network. Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 900 to which the solution of the present invention is applied. The specific computer device 900 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0083] Among them, the processor 902 is used to run the computer program 9032 stored in the memory to implement the following steps: if a user login instruction is received, the user login information is obtained according to the user login instruction, and whether the current user meets the preset guidance conditions is determined according to the user login information; if the current user meets the preset guidance conditions, the behavior breakpoint data corresponding to the current user is obtained from the preset database according to the user login information; the target operation steps are determined by a preset step decision model according to the behavior breakpoint data, and the target operation template is matched from the preset operation template library according to the target operation steps through a preset similarity matching method, wherein the preset step decision model is obtained by training the naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps; according to the target operation steps and the target operation template, the voice operation steps of the current user are guided by voice synthesis technology.

[0084] In certain embodiments, such as the present embodiment, when the processor 902 implements the step of determining whether the current user meets the preset guidance condition based on the user login information, it specifically implements the following steps: determining whether the current user is a new user based on the user login information; if the current user is not a new user, obtaining the number of logins of the current user from a preset database based on the login information; if the number of logins is not less than the preset number of logins, determining that the current user meets the preset guidance condition.

[0085] In certain embodiments, such as the present embodiment, the processor 902 specifically implements the following steps when implementing the step of training the naive Bayes algorithm model using the sample behavior breakpoint data and the sample operation steps to obtain the preset step decision model step: obtaining the sample behavior breakpoint data and the sample operation steps from the preset sample library, and using the sample behavior breakpoint data and the sample operation steps as the target data set; dividing the target data set into a training data set and a verification data set according to a preset ratio; inputting the training data set into the naive Bayes algorithm model for training until a preset number of training times; inputting the verification data set into the trained naive Bayes algorithm model to obtain the prediction operation steps; calculating the prediction accuracy according to the prediction operation steps and the operation steps in the verification data set, and determining whether the preset prediction accuracy exceeds the preset evaluation value; if the preset prediction accuracy exceeds the preset evaluation value, using the trained naive Bayes algorithm model as the step decision model; if the preset prediction accuracy does not exceed the preset evaluation value, resetting the preset ratio and the preset number of training times, and returning to execute the step of dividing the target data set into the training data set and the verification data set according to the preset ratio.

[0086] In certain embodiments, such as the present embodiment, the processor 902 implements the step of matching the target operation template from the preset operation template library by a preset similarity matching method according to the target operation step, and specifically implements the following steps: matching an operation template group from the preset operation template library according to the target operation step; respectively calculating the similarity between the operation templates in the operation template group and the preset specification template, and selecting the operation template with the largest similarity value as the target operation template.

[0087] In certain embodiments, such as the present embodiment, the processor 902 implements the step of guiding the current user through voice operation steps according to the target operation step and the target operation template through speech synthesis technology, and specifically implements the following steps: taking the target operation step as the current operation step to perform the operation, and giving a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template; judging whether the current user performs the next operation step according to the voice prompt; if the current user performs the next operation step according to the voice prompt, taking the next operation step as the current operation step, and returning to execute the step of giving a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template until the operation is completed; if the current user does not perform the next operation step according to the voice prompt, taking the current operation step as the target operation step, and returning to execute the step of matching the target operation template from a preset operation template library according to the target operation step through a preset similarity matching method.

[0088] In certain embodiments, such as the present embodiment, after the processor 902 implements the step of determining whether the current user meets the preset guidance conditions based on the user login information, the specific implementation also includes the following steps: if the current user does not meet the preset guidance conditions, the operation behavior data corresponding to the current user performing the step operation is saved to the preset database, wherein the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data.

[0089] It should be understood that in the embodiment of the present invention, the processor 902 may be a central processing unit (CPU), and the processor 902 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0090] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0091] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program. When the computer program is executed by a processor, the processor executes any embodiment of the user operation guidance method based on naive Bayes.

[0092] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0093] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0095] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0097] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0099] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A user operation guidance method based on naive Bayes, characterized in that: include: If a user login instruction is received, obtaining user login information according to the user login instruction, and judging whether the current user meets the preset guidance condition according to the user login information; If the current user meets the preset guidance condition, then obtaining the behavior breakpoint data corresponding to the current user from a preset database according to the user login information; According to the behavior breakpoint data, a target operation step is determined by a preset step decision model, and a target operation template is matched from a preset operation template library by a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps; According to the target operation steps and the target operation template, the current user is guided through voice operation steps through speech synthesis technology.

2. The method according to claim 1, characterized in that The determining, according to the user login information, whether the current user meets the preset guidance condition includes: Determine whether the current user is a new user according to the user login information; If the current user is not a new user, obtaining the login times of the current user from a preset database according to the login information; If the number of login times is not less than a preset number of login times, it is determined that the current user meets a preset guidance condition.

3. The method according to claim 1, characterized in that The Naive Bayes algorithm model is trained using sample behavior breakpoint data and sample operation steps to obtain a preset step decision model, including: Acquire sample behavior breakpoint data and sample operation steps from a preset sample library, and use the sample behavior breakpoint data and the sample operation steps as a target data set; Dividing the target data set into a training data set and a verification data set according to a preset ratio; Inputting the training data set into the Naive Bayes algorithm model for training until a preset number of training times; Inputting the verification data set into the trained Naive Bayes algorithm model to obtain prediction operation steps; Calculating the prediction accuracy according to the prediction operation steps and the operation steps in the verification data set, and determining whether the preset prediction accuracy exceeds a preset evaluation value; If the preset accuracy rate exceeds the preset evaluation value, the trained naive Bayes algorithm model is used as the step decision model; If the preset measurement accuracy does not exceed the preset evaluation value, the preset ratio and the preset number of training times are reset, and the process returns to the step of dividing the target data set into a training data set and a verification data set according to the preset ratio.

4. The method according to claim 1, characterized in that: The step of matching a target operation template from a preset operation template library by a preset similarity matching method according to the target operation step includes: Matching an operation template group from a preset operation template library according to the target operation steps; The similarities between the operation templates in the operation template group and the preset standard templates are calculated respectively, and the operation template with the largest similarity value is selected as the target operation template.

5. The method according to claim 1, characterized in that: The step of guiding the current user through voice operation steps by using speech synthesis technology according to the target operation steps and the target operation template includes: Performing the target operation step as the current operation step, and providing a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template; Determine whether the current user performs the next operation step according to the voice prompt; If the current user performs the next operation step according to the voice prompt, the next operation step will be used as the current operation step, and the process returns to the step of providing a voice prompt for the next operation step of the current operation step through speech synthesis technology according to the target operation template until the operation is completed.

6. The method according to claim 5, characterized in that After determining whether the current user performs the next operation step according to the voice prompt, the method further includes: If the current user does not perform the next operation step according to the voice prompt, the current operation step is used as the target operation step, and the process returns to execute the step of matching the target operation template from the preset operation template library by a preset similarity matching method according to the target operation step.

7. The method according to claim 1, characterized in that After determining whether the current user meets the preset guidance condition according to the user login information, the method further includes: If the current user does not meet the preset guidance condition, the operation behavior data corresponding to the step operation performed by the current user is saved in the preset database, wherein the operation behavior data includes behavior breakpoint data and non-behavior breakpoint data.

8. A user operation guiding device based on Naive Bayes, characterized in that: include: a judgment unit, configured to, if receiving a user login instruction, obtain user login information according to the user login instruction, and judge whether the current user meets the preset guidance condition according to the user login information; an acquisition unit, configured to acquire behavior breakpoint data corresponding to the current user from a preset database according to the user login information if the current user meets the preset guidance condition; A decision unit, configured to determine a target operation step through a preset step decision model according to the behavior breakpoint data, and to match a target operation template from a preset operation template library through a preset similarity matching method according to the target operation step, wherein the preset step decision model is obtained by training a naive Bayes algorithm model using sample behavior breakpoint data and sample operation steps; A guiding unit is used to guide the current user through voice operation steps according to the target operation steps and the target operation template through speech synthesis technology.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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