CA3153056A1 - Intelligently questioning and answering method, device, computer, equipment and storage medium - Google Patents

Abstract

The present invention discloses an intelligently questioning and answering method, and corresponding device, computer equipment and storage medium. The method comprises: performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement; determining an operation scenario of the questioning statement according to a preset decision model and a preset rule; employing a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result; performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result.

Description

INTELLIGENTLY QUESTIONING AND ANSWERING METHOD, DEVICE, COMPUTER EQUIPMENT AND STORAGE MEDIUM
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of data processing technology, and more particularly to an intelligently questioning and answering method, and corresponding device, computer equipment and storage medium.
Description of Related Art

[0002] With the rapid development of financial businesses, customer service departments put more and more demand on personnel. Problems processed everyday by customer service personnel are repetitive and mechanical works to these service personnel as both the problems for which advices are sought by users and utterances responded by the service personnel are essentially fixed or similar, and huge manpower expenditure has to be additionally provided therefor.

[0003] Over the recent years, with the advent of artificial intelligence, many manpower consuming works can be realized by computers, the development of artificial intelligence has not only become a hotspot of the scientific circle, but has also been the pursuit of various intemet companies. From the perspective of company development, utilization of artificial intelligence technology to aid or even to replace human works not only economizes on the cost but is also a progress of informatization and intellectualization.
For example, the robot Q&A system can superbly help customer service personnel in their work, and consummate Q&A robots can give quick and precise answers to questions put forth by users. However, currently available commercial customer service robots are Date Recue/Date Received 2022-03-22 mostly based on the knowledge base and single-round dialogues lacking contextual association, are rather low in working efficiency, and not so high in the precision in answering questions, whereby user experience is rendered mediocre.

[0004] Accordingly, there is an urgent need to propose a novel intelligently questioning and answering method, so as to address the above problems.
SUMMARY OF THE INVENTION

[0005] In order to deal with problems pending in the state of the art, embodiments of the present invention provide an intelligently questioning and answering method, and corresponding device, computer equipment and storage medium, so as to overcome problems prevailing in prior-art intelligent Q&A technology in which contextual association is lacking and precision in answering questions is relatively low.

[0006] To solve one or more of the aforementioned technical problem(s), the present invention employs the following technical solutions.

[0007] According to the first aspect, there is provided an intelligently questioning and answering method that comprises the following steps:

[0008] performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0009] determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0010] employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;

Date Recue/Date Received 2022-03-22

[0011] performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0012] generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0013] Further, the step of performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result includes:

[0014] calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

[0015] Further, when the recognition result does not contain any current business scenario but contains the current user intention, the method further comprises:

[0016] enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

[0017] Further, when the recognition result does not contain any current business scenario but contains the current user intention, and the current user does not have any historical business scenario or the historical business scenario is not related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:

[0018] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to the current user intention and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

[0019] Further, when the recognition result contains the current business scenario and contains the current user intention, or when the recognition result does not contain any current Date Recue/Date Received 2022-03-22 business scenario but contains the current user intention, and the historical business scenario is related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:

[0020] determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

[0021] Further, when the recognition result contains the current business scenario but does not contain any current user intention, the method further comprises:

[0022] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate user intention selected by the current user.

[0023] Further, when the recognition result does not contain any current business scenario and does not contain any current user intention, the method further comprises:

[0024] pushing preset hotspot questions to the current user for selection.

[0025] According to the second aspect, there is provided an intelligently questioning and answering device that comprises:

[0026] a data processing module, for performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0027] a first recognizing module, for determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0028] a second recognizing module, for employing, when the operation scenario is a Q&A
scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, and Date Recue/Date Received 2022-03-22 determining a target calculating rule of the questioning statement according to the recognition result;

[0029] a data calculating module, for performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0030] a result outputting module, for generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0031] According to the third aspect, there is provided a computer equipment that comprises a memory, a processor and a computer program stored on the memory and operable on the processor, and the following steps are realized when the processor executes the computer program:

[0032] performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0033] determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0034] employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;

[0035] performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0036] generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0037] According to the fourth aspect, there is provided a computer-readable storage medium storing a computer program thereon, and the following steps are realized when the computer program is executed by a processor:
Date Recue/Date Received 2022-03-22

[0038] performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0039] determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0040] employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;

[0041] performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0042] generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0043] The technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.

[0044] In the intelligently questioning and answering method, and corresponding device, computer equipment and storage medium provided by the embodiments of the present invention, by performing word-segmentation processing on a received questioning statement sent from a current user, obtaining a word-segmentation result of the questioning statement, determining an operation scenario of the questioning statement according to a preset decision model and a preset rule, employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, determining a target calculating rule of the questioning statement according to the recognition result, performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, obtaining a Date Recue/Date Received 2022-03-22 calculation result, and generating result data of a preset format according to the calculation result, so as to facilitate check by the current user, and by recognizing the business scenario and the user intention of the questioning statement respectively, precision in answering the question is enhanced, and user experience hence is enhanced.

[0045] In the intelligently questioning and answering method, and corresponding device, computer equipment and storage medium provided by the embodiments of the present invention, after the current user intention and the current business scenario of the questioning statement have been recognized by employing the preset classification model and the word-segmentation result, the recognition result is stored in association with the current user, so that, when a subsequent questioning statement sent from the user for seeking advice for a question lacks business scenario, the stored recognition result is invoked to serve as reference, so as to further enhance precision in answering the question.
BRIEF DESCRIPTION OF THE DRAWINGS

[0046] To more clearly describe the technical solutions in the embodiments of the present invention, drawings required to be used in the description of the embodiments will be briefly introduced below. Apparently, the drawings introduced below are merely directed to some embodiments of the present invention, while it is possible for persons ordinarily skilled in the art to acquire other drawings based on these drawings without spending creative effort in the process.

[0047] Fig. 1 is a flowchart illustrating the intelligently questioning and answering method according to an exemplary embodiment;

[0048] Fig. 2 is a view schematically illustrating the structure of the intelligently questioning and answering device according to an exemplary embodiment; and Date Recue/Date Received 2022-03-22

[0049] Fig. 3 is a view schematically illustrating the internal structure of the computer equipment according to an exemplary embodiment.
DETAILED DESCRIPTION OF THE INVENTION

[0050] In order to make more lucid and clear the objectives, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be more clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention.
Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending creative effort shall all fall within the protection scope of the present invention.

[0051] As noted in the Description of Related Art, in order to catch up with the development of financial businesses, to reduce manpower cost, and to enhance customer service efficiency and user satisfaction, it is usually required for intelligent customer service robots to cooperatively work with the human customer service. However, currently available commercial customer service robots are mostly based on the knowledge base and single-round dialogues lacking contextual association, are relatively low in the precision in answering questions, whereby working efficiency and customer service experience are rendered mediocre.

[0052] To address the aforementioned problems, in the embodiments of the present invention is created a complete financial business intelligently questioning and answering robot system having such functions as intention recognition, contextual association, map retrieval and matching of similar questions by previously combining such technologies as the natural language processing technology, machine learning technology, deep learning technology, and knowledge map technology, etc. During specific Date Recue/Date Received 2022-03-22 implementation, the intelligently questioning and answering robot system can be created on the basis of a Q&A knowledge base and a knowledge map, as a preferred examples, the knowledge base is embodied as an ES knowledge base, and construction of the knowledge base includes a financially professional Q&A library, a chitchat Q&A
library, a hotspot questions library, a user log library, and a scenario-intention relations library.
The knowledge map can be constructed as a financial Q&A map according to data in the financially professional Q&A library.

[0053] Based on such a system can be realized the intelligently questioning and answering method proposed by an embodiment of the present invention, with respect to the operation scenario of a questioning statement sent from the user being a Q&A
scenario, this method further recognizes the current user intention and the current business scenario of the questioning statement, on the one hand, a target calculating rule is determined in conjunction with the recognized current user intention and current business scenario to perform corresponding calculation on the questioning statement, result data of a preset format is thereafter generated according to the calculation result and fed back to the current user, so that precision in answering the question is enhanced, on the other hand, the recognition result is stored in association with the current user, so that, while the user is carrying out a round-to-round dialogue, when a subsequently sent questioning statement lacks business scenario, the stored recognition result is invoked to serve as reference, so as to further enhance precision in answering the question.

[0054] Fig. 1 is a flowchart illustrating the intelligently questioning and answering method according to an exemplary embodiment, with reference to Fig. 1, the method comprises the following steps.

[0055] 51 - performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement.

Date Recue/Date Received 2022-03-22

[0056] Specifically, it is usually required to somehow preprocess in advance the data that enters the intelligently questioning and answering robot system, so as to enhance the precision of subsequent calculation. In the embodiments of the present invention, besides including performing word-segmentation processing on the received questioning statement to obtain the word-segmentation result in the questioning statement, the preprocessing operation can further include such operations as character purification, word segmentation, and correction, etc., to which no restriction is made in this context, and it is possible for the user to set as practically required.

[0057] Taking for example an intelligently questioning and answering robot system of the financial business, a self-defined dictionary pertaining to the field of financial business can be constructed in advance to perform word-segmentation processing on the questioning statement sent from the current user, a self-defined correction dictionary pertaining to the field of financial business can also be constructed in advance to perform correction processing on the questioning statement sent from the current user.

[0058] S2 - determining an operation scenario of the questioning statement according to a preset decision model and a preset rule.

[0059] Specifically, usually speaking, questions for which advices are sought by users are classified into plural operation scenarios. In the embodiments of the present invention, after the word-segmentation result of the questioning statement has been extracted, a preset decision model and a preset rule will be employed to recognize the keyword to determine the specific operation scenario to which the questioning statement corresponds, namely to classify the user's question to the specific operation scenario for directed solution. The preset rule includes, but is not limited to, a keyword rule, for example, different keywords are set in advance for different operation scenarios, the word-segmentation result is matched with preset keywords, once the matching result satisfies Date Recue/Date Received 2022-03-22 the preset requirement, the questioning statement is classified to the operation scenario to which the matched keyword corresponds. During specific implementation, the preset decision model can be trained and obtained in advance by the use of the keyword rule and a machine learning model. As should be noted here, in the embodiments of the present invention, the business scenario includes, but is not limited to, Capricious Loan, Capricious Payment, and Change Treasure, etc., and the user intention includes, but is not limited to, repaying, cancelling, and changing password, etc.

[0060] Likewise taking the financial business for example, in the embodiments of the present invention, the operation scenario can be classified in advance mainly into the following types: a manual scenario, a chitchat scenario, an order scenario, a self-help operation, a Q&A scenario, and a similar question clicking scenario, etc. The manual scenario is used to solve problems proposed on the user's own initiative to be transferred for manual solution or recognized by the system according to a preset rule to be necessarily manually solved, and the keywords as well as the intentions can be configured by the foreground service personnel as keywords and intentions involved and processed in the transfer to manual solution. The chitchat scenario makes use of a colossal chitchat knowledge base to solve such chitchat problems as concerning greeting, praising, and expressing involved in the process of seeking advice by the user. The order scenario solves problems possibly encountered by the user during the process of payment, and subsequently invokes recent orders of the user to present to the user. The self-help operation solves some problems to be completed by a series of operations by providing operation links. What the Q&A
system returns after one round of Q&A may not be the answer, but similar questions for the user to select, these similar questions are already existent in the knowledge base, and their answers can be returned by direct retrieval after clicking of the user, what the similar question clicking scenario solves are precisely these types of questions.

[0061] When the preset decision model recognizes that the operation scenario of the questioning statement is such a scenario as manual, order, or self-help operation, the outputting Date Recue/Date Received 2022-03-22 module is directly entered, and respective identifications are output; when the preset decision model recognizes that the operation scenario of the questioning statement is a chitchat scenario, the knowledge base is entered to match out the answer; when the preset decision model recognizes that the operation scenario of the questioning statement is a Q&A scenario, the financial robot Q&A scenario is entered, and the subsequent flow is continued.

[0062] S3 - employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result.

[0063] Specifically, the method provided by the embodiment of the present invention is proposed to mainly solve questions sent from the user under the Q&A scenario. Since a question usually basically contains two key elements, namely business scenario and user intention, when it is recognized that the operation scenario of the questioning statement is a Q&A
scenario, it is required to further recognize the user intention and the specific business scenario of the questioning statement, and a target calculating rule of the questioning statement is thereafter determined according to the recognition result.

[0064] Specifically, the intention recognizing module is the most core part of the Q&A robot, and intention recognition of the user's question directly affects the Q&A
effect, in the embodiments of the present invention, two independent deep learning classifiers are used for intention recognition, of which one is used to recognize the business scenario of the question, and the other one is used to recognize the intention involved in the question.
During specific implementation, a confusion threshold and certain specific rules can be used to judge whether the results of the classifiers are believable, once a preset condition is met, it is considered that the classification result is confused, that is, the classifier Date Recue/Date Received 2022-03-22 cannot extract the correct business scenario or user intention, conversely, the result is not confused, that is, the classifier can extract the correct business scenario or user intention.

[0065] Specifically, when the user seeks advice for a question, the user mostly directly speaks out the intention, but the scenario is lacking, accordingly, when the intention recognizing module is passed during the process of multiple rounds of Q&A of the user, the scenario recognized by the current model will be stored in association with the current user to serve as reference data for the next Q&A scenario.

[0066] S4 ¨ performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result.

[0067] Specifically, the recognition result obtained by employing a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement may subsume plural circumstances, in the embodiments of the present invention, different follow-up operations are set with respect to the different recognition results, so as to enhance the precision in answering the question ¨ the specific modes of execution will be enunciated later on a one-by-one basis.

[0068] S5 ¨ generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0069] Specifically, in order to enhance user experience, in the embodiments of the present invention, after the calculation result has been obtained, the calculation result can be sorted into various forms required by the frontend to be fed back to the current user. For instance, with respect to a question with a specific special identification as returned by the first recognizing module, the question is sorted into a result with the special identification and returned; with respect to a request for hotspot questions as returned by Date Recue/Date Received 2022-03-22 the second recognizing module, the hotspot questions are retrieved and sorted into the form of a link of similar questions to be returned; with respect to plural options as returned by the map matching module, several top-ranking options in terms of probabilities are selected and sorted into slot candidate options to be returned; with respect to a calculation result as returned by the data calculating module, if the result is an answer, the answer is sorted and returned, if the result is a plurality of similar questions, these are sorted into the form of a link of similar questions and returned; with respect to an answer to a chitchat question as returned by the knowledge base matching module, the answer is sorted and returned, and with respect to similar questions as returned by the data calculating module, the similar questions are sorted into the form of a link of similar questions and returned --- to which no enumeration is made on a one-by-one basis in this context.

[0070] As a preferred mode of execution in the embodiments of the present invention, the step of performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result includes:

[0071] calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

[0072] Specifically, in the case the questioning statement sent from the current user has the two key elements as the complete business scenario and user intention, the questioning statement will enter the data calculating module for calculation. As a preferred example in the embodiments of the present invention, the calculation of the word-segmentation result includes similarity calculation. During specific implementation, a similarity between the questioning statement of the current user and questions (namely candidate sentences) in a preset Q&A library (that includes, but is not limited to, a financially professional Q&A library) is calculated through the word-segmentation result.
The similarity model can be modified on the basis of the Alibaba text matching model (SETM), to conform to the data in the financial Q&A library. What the similarity model outputs Date Recue/Date Received 2022-03-22 are candidate sentences and their similarity scores.

[0073] As a preferred mode of execution in the embodiments of the present invention, two screening thresholds can be set in advance, of which one is a standard question threshold, the question (namely a candidate sentence) satisfying this threshold is determined as the questioning statement sent from the current user, and the answer to this question is taken to serve as the calculation result; the other one is a similar question threshold, the question (namely a candidate sentence) satisfying this threshold can serve as a candidate similar question of the questioning statement sent from the current user, and the candidate similar question is taken to serve as the calculation result. Put in other words, the calculation result returned by the data calculating module is classified into two circumstances, one is the answer to the question, and the other one is a plurality of similar questions whose similarities have been sequenced.

[0074] As a preferred mode of execution in the embodiments of the present invention, when the recognition result does not contain any current business scenario but contains the current user intention, the method further comprises:

[0075] enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

[0076] Specifically, in order to enhance the Q&A precision, in the embodiments of the present invention, when it is recognized that the questioning statement sent from the current user lacks business scenario, it will be enquired whether the current user has any historical business scenario, the historical business scenario here includes, but is not limited to, business scenario data contained in the recognition result obtained as the second recognizing module recognizes the current user intention and the current business scenario of the questioning statement in the process of plural rounds of Q&As by the current user. If it is enquired that the current user has an associated historical business scenario, it is further judged whether the historical business scenario is related to the Date Recue/Date Received 2022-03-22 current user intention.

[0077] As a preferred mode of execution in the embodiments of the present invention, when the recognition result does not contain any current business scenario but contains the current user intention, and the current user does not have any historical business scenario or the historical business scenario is not related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:

[0078] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to the current user intention and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

[0079] Specifically, when the current user intention is recognized, but no current business scenario is recognized or the current user does not have any historical business scenario, or when the current user has a historical business scenario but the historical business scenario is not related to the current user intension, a preset map matching model and the word-segmentation result are employed to retrieve out a plurality of candidate business scenarios related to the current user intention, the plural candidate business scenarios are subsequently fed back to the current user for selection, and the target calculating rule of the keyword is determined according to the candidate business scenario clicked and selected by the current user in conjunction with the current user intention obtained in the foregoing step. The target calculating rule here includes, but is not limited to, calculating the similarity between the keyword and candidate sentences in the preset Q&A
library.

[0080] During specific implementation, taking the financial business for example, a financial Q&A knowledge map can be constructed by means of the questions in the financially professional Q&A library, with scenarios and intentions of financial questions serving as Date Recue/Date Received 2022-03-22 nodes of the map. The knowledge map is mainly used to retrieve according to recognized business scenarios or user intentions when it is recognized that the questioning statement sent from the current user is incomplete in terms of the business scenario or the user intention, and to provide plural candidate business scenarios or candidate user intentions with higher probabilities and to return the same to the current user for selection.

[0081] As a preferred example in the embodiments of the present invention, it is further possible to take the candidate business scenarios or user intentions as the calculation result and to input the same to the outputting module, and to subsequently sort the result into result data of a preset format to be fed back to the current user for selection.

[0082] As a preferred mode of execution in the embodiments of the present invention, when the recognition result contains the current business scenario and contains the current user intention, or when the recognition result does not contain any current business scenario but contains the current user intention, and the historical business scenario is related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:

[0083] determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

[0084] Specifically, when the current user intention is recognized and the current business scenario is recognized, or when the current user intention is recognized, but no current business scenario is recognized but the current user has a historical business scenario and the historical business scenario is related to the current user intention, it is possible to directly determine the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

Date Recue/Date Received 2022-03-22

[0085] As a preferred mode of execution in the embodiments of the present invention, when the recognition result contains the current business scenario but does not contain any current user intention, the method further comprises:

[0086] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate user intention selected by the current user.

[0087] Specifically, when the current business scenario is recognized but no current user intention is recognized, a preset map matching model and the word-segmentation result are employed to retrieve out a plurality of candidate user intentions involved in the current business scenario, the plural candidate user intentions are subsequently fed back to the current user for selection, and the target calculating rule of the keyword is determined according to the candidate user intention clicked and selected by the current user in conjunction with the aforementioned current business scenario. The target calculating rule here includes, but is not limited to, calculating a similarity between the word-segmentation result and candidate sentences in the preset Q&A library, namely calculating the similarity between the questioning statement and candidate sentences in the preset Q&A library.

[0088] As a preferred mode of execution in the embodiments of the present invention, when the recognition result does not contain any current business scenario and does not contain any current user intention, the method further comprises:

[0089] pushing preset hotspot questions to the current user for selection.

[0090] Specifically, when none of the current business scenario and current user intention involved in the questioning statement is recognized, it is now impossible to judge the question specifically asked by the current user, at this time it is possible to push preset Date Recue/Date Received 2022-03-22 hotspot questions to the current user for selection. As should be noted here, the preset hotspot questions in the embodiments of the present invention are marked in advance with their business scenarios and user intentions, once the current user selects a certain hotspot question, the answer to this hotspot question is directly fed back to the current user.

[0091] As a preferred mode of execution in the embodiments of the present invention, a knowledge base is further constructed in advance. When it is recognized that the operation scenario of the questioning statement is a chitchat scenario, the answer is directly retrieved out through matching of the knowledge base, and the answer is input to the result outputting module. Alternatively, after a questioning statement having the complete business scenario and user intention has been calculated through the data calculating module, there might be the circumstance in which the lowest similar question threshold cannot be found. Such a circumstance may be due to the fact that the business personnel has updated the financial Q&A library, or that the similarity model of this questioning statement can indeed not be recognized. In order to solve such problems, analysis is further performed in the embodiments of the present invention by employing ES
retrieval and an algorithm based on word movement distance (WMD) after the data calculating module. The sorting retrieval function carried with the ES is firstly utilized to retrieve 50 pieces of data returned by the financially professional Q&A library, similarities between the user's questions and the returned data are calculated and sorted by means of the WMD
algorithm, and a preset threshold is invoked to the effect that questions satisfying this threshold can be taken as candidate similar questions and input to the result outputting module.

[0092] Fig. 2 is a view schematically illustrating the structure of the intelligently questioning and answering device according to an exemplary embodiment, with reference to Fig.
2, the device comprises:

[0093] a data processing module, for performing word-segmentation processing on a received Date Recue/Date Received 2022-03-22 questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0094] a first recognizing module, for determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0095] a second recognizing module, for employing, when the operation scenario is a Q&A
scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, and determining a target calculating rule of the questioning statement according to the recognition result;

[0096] a data calculating module, for performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0097] a result outputting module, for generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0098] As a preferred mode of execution in the embodiments of the present invention, the data calculating module is specifically employed for:

[0099] calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

[0100] As a preferred mode of execution in the embodiments of the present invention, the second recognizing module is further employed for:

[0101] enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

[0102] As a preferred mode of execution in the embodiments of the present invention, the device further comprises:

[0103] a map matching module, for employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to Date Recue/Date Received 2022-03-22 the current user intention and to feed back the same to the current user for selection;

[0104] the second recognizing module is specifically employed for determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

[0105] As a preferred mode of execution in the embodiments of the present invention, the second recognizing module is specifically employed for:

[0106] determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

[0107] As a preferred mode of execution in the embodiments of the present invention, the map matching module is further employed for:

[0108] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection; and

[0109] the second recognizing module is specifically employed for determining the target calculating rule of the questioning statement according to the candidate user intention selected by the current user.

[0110] As a preferred mode of execution in the embodiments of the present invention, the result outputting module is further employed for:

[0111] pushing preset hotspot questions to the current user for selection.

[0112] Fig. 3 is a view schematically illustrating the internal structure of the computer equipment according to an exemplary embodiment, with reference to Fig. 3, the computer equipment comprises a processor, a memory, and a network interface connected to each other via a system bus. The processor of the computer equipment is employed to provide computing and controlling capabilities. The memory of the computer equipment includes a Date Recue/Date Received 2022-03-22 nonvolatile storage medium and an internal memory. The nonvolatile storage medium stores therein an operating system, a computer program and a database. The internal memory provides environment for the running of the operating system and the computer program in the nonvolatile storage medium. The network interface of the computer equipment is employed to connect to an external terminal via network for communication.
The computer program realizes a method of optimizing an execution plan when it is executed by a processor.

[0113] As understandable to persons skilled in the art, the structure illustrated in Fig. 3 is merely a block diagram of partial structure relevant to the solution of the present invention, and does not constitute any restriction to the computer equipment on which the solution of the present invention is applied, as the specific computer equipment may comprise component parts that are more than or less than those illustrated in Fig. 3, or may combine certain component parts, or may have different layout of component parts.

[0114] As a preferred mode of execution in the embodiments of the present invention, the computer equipment comprises a memory, a processor and a computer program stored on the memory and operable on the processor, and the following steps are realized when the processor executes the computer program:

[0115] performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0116] determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0117] employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;

[0118] performing corresponding calculation on the word-segmentation result of the questioning Date Recue/Date Received 2022-03-22 statement according to the target calculating rule, and obtaining a calculation result; and

[0119] generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0120] As a preferred mode of execution in the embodiments of the present invention, when the processor executes the computer program, the following step is further realized:

[0121] calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

[0122] As a preferred mode of execution in the embodiments of the present invention, when the processor executes the computer program, the following step is further realized:

[0123] enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

[0124] As a preferred mode of execution in the embodiments of the present invention, when the processor executes the computer program, the following steps are further realized:

[0125] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to the current user intention and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

[0126] As a preferred mode of execution in the embodiments of the present invention, when the processor executes the computer program, the following step is further realized:

[0127] determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

[0128] As a preferred mode of execution in the embodiments of the present invention, when the Date Recue/Date Received 2022-03-22 processor executes the computer program, the following steps are further realized:

[0129] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate user intention selected by the current user.

[0130] As a preferred mode of execution in the embodiments of the present invention, when the processor executes the computer program, the following step is further realized:

[0131] pushing preset hotspot questions to the current user for selection.

[0132] In the embodiments of the present invention, there is further provided a computer-readable storage medium storing thereon a computer program, and the following steps are realized when the computer program is executed by a processor:

[0133] performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;

[0134] determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;

[0135] employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;

[0136] performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and

[0137] generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

[0138] As a preferred mode of execution in the embodiments of the present invention, when the Date Recue/Date Received 2022-03-22 computer program is executed by a processor, the following step is further realized:

[0139] calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

[0140] As a preferred mode of execution in the embodiments of the present invention, when the computer program is executed by a processor, the following step is further realized:

[0141] enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

[0142] As a preferred mode of execution in the embodiments of the present invention, when the computer program is executed by a processor, the following steps are further realized:

[0143] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to the current user intention and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

[0144] As a preferred mode of execution in the embodiments of the present invention, when the computer program is executed by a processor, the following step is further realized:

[0145] determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A library.

[0146] As a preferred mode of execution in the embodiments of the present invention, when the computer program is executed by a processor, the following steps are further realized:

[0147] employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate user intention selected by the Date Recue/Date Received 2022-03-22 current user.

[0148] As a preferred mode of execution in the embodiments of the present invention, when the computer program is executed by a processor, the following step is further realized:

[0149] pushing preset hotspot questions to the current user for selection.

[0150] In summary, the technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.

[0151] In the intelligently questioning and answering method, and corresponding device, computer equipment and storage medium provided by the embodiments of the present invention, by performing word-segmentation processing on a received questioning statement sent from a current user, obtaining a word-segmentation result of the questioning statement, determining an operation scenario of the questioning statement according to a preset decision model and a preset rule, employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, determining a target calculating rule of the questioning statement according to the recognition result, performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, obtaining a calculation result, and generating result data of a preset format according to the calculation result, so as to facilitate check by the current user, and by taking the business scenario and the user intention of the questioning statement into overall consideration, precision in answering the question is enhanced, and user experience hence is enhanced.

[0152] In the intelligently questioning and answering method, and corresponding device, computer equipment and storage medium provided by the embodiments of the present invention, after the current user intention and the current business scenario of the Date Recue/Date Received 2022-03-22 questioning statement have been recognized by employing the preset classification model and the word-segmentation result, the recognition result is stored in association with the current user, so that, when a subsequent questioning statement sent from the user for seeking advice for a question lacks business scenario, the stored recognition result is invoked to serve as reference, so as to further enhance precision in answering the question.

[0153] As should be noted, when the intelligently questioning and answering device provided by the aforementioned embodiment triggers a questioning and answering business, the division into the aforementioned various functional modules is merely by way of example, while it is possible, in actual application, to base on requirements to assign the functions to different functional modules for completion, that is to say, to divide the internal structure of the device into different functional modules to complete the entire or partial functions described above. In addition, the intelligently questioning and answering device provided by the aforementioned embodiment pertains to the same conception as the intelligently questioning and answering method provided by the method embodiment, that is to say, the device is based on the intelligently questioning and answering method ¨ see the corresponding method embodiment for its specific realization process, while no repetition will be made in this context.

[0154] As understandable by persons ordinarily skilled in the art, realization of the entire or partial steps of the aforementioned embodiments can be completed by hardware, or by a program instructing relevant hardware, the program can be stored in a computer-readable storage medium, and the storage medium can be a read-only memory, a magnetic disk, or an optical disk, etc.

[0155] What the above describes is merely directed to preferred embodiments of the present invention, and is not meant to restrict the present invention. Any amendment, equivalent replacement or improvement makeable within the spirit and principle of the present invention shall all be covered by the protection scope of the present invention.

Date Recue/Date Received 2022-03-22

Claims (10)

What is claimed is:

1. An intelligently questioning and answering method, characterized in that the method comprises the following steps:
performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;
determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;
employing, when the operation scenario is a questioning and answering (hereinafter referred to as "Q&A") scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, storing a recognition result in association with the current user, and determining a target calculating rule of the questioning statement according to the recognition result;
performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

2. The intelligently questioning and answering method according to Claim 1, characterized in that the step of performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result includes:
calculating a similarity between the questioning statement and candidate sentences in a preset Q&A library according to the word-segmentation result.

3.
The intelligently questioning and answering method according to Claim 1 or 2, characterized in that, when the recognition result does not contain any current business scenario but contains Date Recue/Date Received 2022-03-22 the current user intention, the method further comprises:
enquiring whether the current user has any historical business scenario, if yes, judging whether the historical business scenario is related to the current user intention.

4. The intelligently questioning and answering method according to Claim 3, characterized in that, when the recognition result does not contain any current business scenario but contains the current user intention, and the current user does not have any historical business scenario or the historical business scenario is not related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:
employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate business scenarios related to the current user intention and to feed back the same to the current user for selection, and determining the target calculating rule of the questioning statement according to the candidate business scenario selected by the current user.

5. The intelligently questioning and answering method according to Claim 3, characterized in that, when the recognition result contains the current business scenario and contains the current user intention, or when the recognition result does not contain any current business scenario but contains the current user intention, and the historical business scenario is related to the current user intention, the step of determining a target calculating rule of the questioning statement according to the recognition result includes:
determining the target calculating rule as calculating a similarity between the word-segmentation result of the questioning statement and candidate sentences in the preset Q&A
library.

6.
The intelligently questioning and answering method according to Claim 1 or 2, characterized in that, when the recognition result contains the current business scenario but does not contain any current user intention, the method further comprises:
employing a preset map matching model and the word-segmentation result to retrieve out a plurality of candidate user intentions related to the current business scenario and to feed back the same to the current user for selection, and determining the target calculating rule of the Date Recue/Date Received 2022-03-22 questioning statement according to the candidate user intention selected by the current user.

7.
The intelligently questioning and answering method according to Claim 1 or 2, characterized in that, when the recognition result does not contain any current business scenario and does not contain any current user intention, the method further comprises:
pushing preset hotspot questions to the current user for selection.

8. An intelligently questioning and answering device, characterized in that the device comprises :
a data processing module, for performing word-segmentation processing on a received questioning statement sent from a current user, and obtaining a word-segmentation result of the questioning statement;
a first recognizing module, for determining an operation scenario of the questioning statement according to a preset decision model and a preset rule;
a second recognizing module, for employing, when the operation scenario is a Q&A scenario, a preset classification model and the word-segmentation result to recognize a current user intention and a current business scenario of the questioning statement, and determining a target calculating rule of the questioning statement according to the recognition result;
a data calculating module, for performing corresponding calculation on the word-segmentation result of the questioning statement according to the target calculating rule, and obtaining a calculation result; and a result outputting module, for generating result data of a preset format according to the calculation result, so as to facilitate check by the current user.

9. A computer equipment, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, characterized in that the method steps according to anyone of Claims 1 to 7 are realized when the processor executes the computer program.

10. A computer-readable storage medium, storing a computer program thereon, characterized in Date Recue/Date Received 2022-03-22 that the method steps according to anyone of Claims 1 to 7 are realized when the computer program is executed by a processor.

Date Recue/Date Received 2022-03-22