User intention recognition and accurate answer generation method and system

Abstract

The invention discloses a user intention recognition and accurate answer generation method and system, and belongs to the technical field of data analysis, and the method comprises the steps: obtaining historical intention appeals of a preset user based on big data, generating a training set through voice data corresponding to the historical intention appeals, extracting each sample text in the training set, and generating a recognition model; inputting the sample text into a recognition model, and dividing the training set into a test set and a verification set in the recognition model; the deep learning analysis technology is utilized, the historical intention appeal of the preset user is obtained, data training is generated for the voice data, the recognition model is generated based on the data training, the voice data of the user is trained and recognized in real time, meanwhile, the voice text data of the user is further divided, and the user experience is improved. The user intention is analyzed, recognized and judged according to the divided text data, and the recognition accuracy and analysis recognition efficiency of the user intention are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for generating user intention recognition and accurate answers. Background Art

[0002] In modern society, analyzing user intentions and intelligently replying to them has been applied to many industries. Among them, various voice customer service services are widely used in many scenarios. Most of the existing technologies are based on human-computer voice interaction. At present, intelligent assistants are mainly based on human-computer voice interaction. Usually, word vectors and context word vectors in sample texts are obtained for training to obtain intention recognition models. Then, when identifying user intentions, the trained intention recognition model can be used to extract the word vectors and context word vector matrices of each word in the text to be classified. Then, based on the extracted word vectors and context word vector matrices of each word, the corresponding intention of the user input is determined, and then a series of behaviors and strategies are generated and executed to achieve interaction with the user. However, the text to be classified is often obtained by voice recognition of the user's input voice. Due to the limited accuracy of voice recognition, it is sometimes difficult to determine the user's intention and intention based on the recognized voice analysis, which not only reduces the accuracy of user intention recognition, but sometimes even causes the user to get an analysis reply that is far from his intention, making it difficult to improve the analysis and recognition efficiency. Summary of the invention

[0003] In view of the above problems existing in the technical field of existing data analysis, the present invention is proposed.

[0004] Therefore, one of the objects of the present invention is to provide a method and system for generating user intention recognition and accurate answers, which utilizes deep learning analysis technology to obtain the historical intention demands of preset users, generate data training for voice data, and generate a recognition model based on this to train and recognize the user's voice data in real time. At the same time, the user's voice text data is further divided, and the user's intention is determined based on the divided text data analysis and recognition, thereby improving the accuracy of user intention recognition and the efficiency of analysis and recognition.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a method for identifying user intention and generating accurate answers, comprising:

[0007] Based on big data, the historical intention demands of preset users are obtained, and a training set is generated from the voice data corresponding to the historical intention demands, each sample text in the training set is extracted, a recognition model is generated, and the sample text is input into the recognition model;

[0008] In the recognition model, the training set is divided into a test set and a validation set, and the acquisition time of each sample text in the training set is marked with the preset user's intention demand, and the correlation between the sample text acquisition time and the preset user's intention demand is analyzed, including:

[0009] Acquire voice data sent by a preset user to a customer service representative at the beginning of the month, and collect historical intentions and demands of the preset user at the beginning of the month based on the acquired voice data, wherein the historical intentions and demands include the services that the preset user needs to handle and / or consult;

[0010] Collect word vector features between different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services, and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different service types;

[0011] Generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention. If the verification result is correct, store the reply text and upload it to the recognition model. Otherwise, do not store it and regenerate the reply text until the verification result is correct.

[0012] Analyze the text data features between the reply text with a correct verification result and the reply text with a verification error. When replying to the intended request of the user according to the type of business that the user needs to handle in the future period, automatically remove the text data features contained in the reply text with a verification error.

[0013] Marking the acquired sample texts, inputting the distinguished data information into the recognition model, and updating the recognition model;

[0014] Acquire preset user voice and text data, divide the voice and text data and analyze them one by one;

[0015] The intention data information of the preset user is collected according to the divided voice and text data, and judgment and processing are performed based on the acquired user intention data information and the corresponding number of voice and text data.

[0016] As a preferred solution of the present invention, the obtained sample texts are differentiated, including being differentiated into corresponding business processing texts, complaint and suggestion texts and business consultation texts, wherein the obtained sample texts are marked, including the number of inputs of historical voice texts by preset users counted in each differentiated sample text data, and the differentiated sample text data are sorted according to the number of inputs.

[0017] As a preferred solution of the present invention, the speech text data is divided into θ 1 ,θ 2 , ..., θ n , where n represents the nth divided speech text data, the context word vectors between different speech text data are analyzed, and the currently divided speech text data are recorded and stored.

[0018] As a preferred solution of the present invention, the semantic features between each voice text data are analyzed, and the semantic features are divided into a first semantic feature, a second semantic feature and a third semantic feature, the association rules between each voice feature are obtained, and the association rules are input into the recognition model, wherein the semantic features include image semantic features.

[0019] As a preferred solution of the present invention, judgment and processing are performed based on the acquired user intention data information and the corresponding number of voice and text data to verify the intention analysis and recognition results of the voice and text data. When the intention analysis and recognition results of the corresponding number of voice and text data are consistent with the acquired user intention data information, data features between the corresponding number of voice and text data and the user intention data information are further acquired, and the data features are summarized as correctly recognized data features. Conversely, when the intention analysis and recognition results of the corresponding number of voice and text data are inconsistent with the acquired user intention data information, the data features of the inconsistency are also acquired, and the data features are summarized as incorrectly recognized data features.

[0020] As a preferred solution of the present invention, among the data features summarized as incorrect recognition, the voice text data corresponding to the data features are further divided for a second time, and a new semantic analysis is performed based on the secondarily divided voice text data, and at the same time matched with the user intention data information, the intention analysis result of the user is re-obtained, and a reply text is generated.

[0021] As a preferred solution of the present invention, in the training set, the sample text is trained using the stochastic gradient descent method, and the correlation features between the preset user intention and the sample text are collected to generate a prediction model based on the correlation features.

[0022] In another aspect, the present invention provides a system for user intention recognition and accurate answer generation method, comprising:

[0023] A big data information acquisition module is used to obtain the historical intention demands of a preset user, and generate a training set with the voice data corresponding to the historical intention demands to generate a recognition model;

[0024] A comprehensive data processing module is used to distinguish and mark the acquired sample texts, input the distinguished data information into the recognition model, update the recognition model, divide the training set into a test set and a validation set, and mark the acquisition time of each sample text in the training set with the preset user's intention appeal;

[0025] A fusion analysis module is used to obtain preset user voice and text data, divide the voice and text data and analyze its semantic features and user intention data information one by one, and the fusion analysis module includes a data statistics module, an analysis module, a verification module and a correction module;

[0026] The data statistics module is used to collect historical intention demands of the preset user in the period at the beginning of the month based on the voice data sent by the preset user to the customer service in the period at the beginning of the month, and the historical intention demands include the services that the preset user needs to handle and / or consult;

[0027] The analysis module is used to analyze the word vector features between the different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different business types;

[0028] The verification module is used to generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention and demand. If the verification result is correct, the reply text is stored and uploaded to the recognition model. Otherwise, it is not stored and the reply text is regenerated until the verification result is correct;

[0029] The correction module is used to analyze the text data features between the reply text with a correct verification result and the reply text with a verification error, and when replying to the intended request of the user according to the type of business that the user needs to handle in the future period, the text data features contained in the reply text with a verification error are automatically removed from the reply text;

[0030] The data identification module responds to the voice and text data divided piece by piece by the fusion analysis module, and performs judgment and processing based on the acquired user intention data information and the corresponding number of voice and text data to verify the intention analysis and recognition results of the number of voice and text data.

[0031] A terminal comprises a processor, an input interface, an output interface and a memory, wherein the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 7.

[0032] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 7.

[0033] Beneficial effects:

[0034] The present invention utilizes deep learning analysis technology to obtain the historical intention demands of preset users, generate data training for voice data, and generate a recognition model based on this, so as to train and recognize the user's voice data in real time. At the same time, the user's voice text data is further divided, and the user's intention is judged according to the divided text data analysis and identification, and the user's intention demand is accurately responded to. When a certain divided voice text data fails to correctly identify and judge the user's intention, the voice text data can be further divided for a second time to re-identify and judge the user's intention, thereby continuously improving and optimizing the intention recognition and answer quality, and improving the accuracy of user intention recognition and analysis and recognition efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0036] Figure 1 A schematic diagram of the modular structure of a system for identifying user intent and generating accurate answers according to an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a method flow of an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a method flow of an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of a process tree structure of an embodiment of the present invention;

[0040] Numbers in the figure: 110 - big data information acquisition module; 120 - comprehensive data processing module; 130 - fusion analysis module; 1301 - data statistics module; 1302 - analysis module; 1303 - verification module; 1304 - correction module; 140 - data discrimination module. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0042] Since the existing technology uses the text to be classified to perform speech recognition on the user's input voice, it is sometimes difficult to determine the user's intention and intention based on the recognized voice analysis, which not only leads to a reduction in the accuracy of user intention recognition, but sometimes even causes the user to receive an analysis response that is far from his or her intention, making it difficult to improve the analysis and recognition efficiency.

[0043] Based on this, the present invention proposes a method and system for generating user intention recognition and accurate answers, which utilizes deep learning analysis technology to obtain the historical intention demands of preset users, generate data training for voice data, and generate a recognition model based on this to train and recognize the user's voice data in real time. At the same time, the user's voice text data is further divided, and the user's intention is determined based on the divided text data analysis and recognition, and the user's intention is accurately answered, thereby improving the accuracy of user intention recognition and the efficiency of analysis and recognition.

[0044] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0045] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for identifying user intentions and generating accurate answers, including:

[0046] S10: obtaining the historical intention demands of the preset user based on the big data, generating a training set with the voice data corresponding to the historical intention demands, extracting each sample text in the training set, generating a recognition model, and inputting the sample text into the recognition model;

[0047] It should be noted that in this embodiment, the obtained sample texts are differentiated, including being differentiated into corresponding business handling texts, complaint and suggestion texts, and business consultation texts, wherein the obtained sample texts are marked, including counting the number of inputs of historical voice texts by a preset user in each differentiated sample text data, and sorting each differentiated sample text data according to the number of inputs;

[0048] On the basis of the above, this embodiment further uses the stochastic gradient descent method to train the sample texts in the training set, collects the correlation features between the preset user intention and the sample texts, and generates a prediction model based on the correlation features;

[0049] S20: In the recognition model, the training set is divided into a test set and a validation set, and the acquisition time of each sample text in the training set is marked with the preset user's intention demand, and the correlation between the sample text acquisition time and the preset user's intention demand is analyzed, including:

[0050] S201: Acquire voice data sent by a preset user to a customer service representative at the beginning of the month, and collect historical intention requests of the preset user at the beginning of the month based on the acquired voice data, wherein the historical intention requests include the services that the preset user needs to handle and / or consult;

[0051] S202: Collect word vector features between different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services, and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different service types;

[0052] S203: Generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention. If the verification result is correct, store the reply text and upload it to the recognition model. Otherwise, do not store it and regenerate the reply text until the verification result is correct.

[0053] S204: analyzing the text data features between the reply text with a correct verification result and the reply text with a verification error, and automatically removing the text data features contained in the reply text with a verification error from the reply text when replying to the intended request of the user according to the type of business that the user needs to handle in the future period;

[0054] S30: Marking the acquired sample text, inputting the distinguished data information into the recognition model, and updating the recognition model;

[0055] Specifically, in this embodiment, the speech text data is divided into θ 1 ,θ 2 , ..., θ n , where n represents the nth divided voice text data, analyzes the context word vectors between different voice text data, and records and stores the currently divided voice text data;

[0056] S40: Acquire preset user voice and text data, divide the voice and text data and analyze them one by one;

[0057] S50: collecting the preset user's intention data information according to the divided voice and text data, and performing judgment and processing according to the acquired user's intention data information and the corresponding number of voice and text data;

[0058] This embodiment further performs judgment processing based on the acquired user intention data information and the corresponding number of voice and text data to verify the intention analysis and recognition results of the corresponding number of voice and text data. When the intention analysis and recognition results of the corresponding number of voice and text data are consistent with the acquired user intention data information, data features between the corresponding number of voice and text data and the user intention data information are further acquired, and the data features are summarized as correctly recognized data features. Conversely, when the intention analysis and recognition results of the corresponding number of voice and text data are inconsistent with the acquired user intention data information, data features when inconsistent are also acquired, and the data features are summarized as incorrectly recognized data features.

[0059] On the basis of the above, in the data features summarized as incorrect recognition, the voice text data corresponding to the data features are further divided twice, and a new semantic analysis is performed based on the secondarily divided voice text data, and at the same time, it is matched with the user intention data information, the user intention analysis result is re-obtained, and the answer text is generated;

[0060] Furthermore, the semantic features between each voice text data are analyzed, and the semantic features are divided into first semantic features, second semantic features and third semantic features, the association rules between each voice feature are obtained, and the association rules are input into the recognition model, wherein the semantic features include image semantic features.

[0061] Based on the above, it can be seen that the present application utilizes deep learning analysis technology to generate data training for voice data by obtaining the historical intention demands of preset users, and generates a recognition model based on this, so as to train and recognize the user's voice data in real time. At the same time, the user's voice text data is further divided, and the user's intention is determined based on the divided text data analysis and recognition, and the user's intention is accurately responded to, thereby improving the accuracy of user intention recognition and the efficiency of analysis and recognition.

[0062] This embodiment combines the above-mentioned method for identifying user intention and generating accurate answers, and also proposes a system applied to the method, including:

[0063] The big data information acquisition module 110 is used to acquire the historical intention demands of the preset user, and generate a training set with the voice data corresponding to the historical intention demands to generate a recognition model;

[0064] The integrated data processing module 120 is used to distinguish and mark the acquired sample texts, input the distinguished data information into the recognition model, update the recognition model, divide the training set into a test set and a validation set, and mark the acquisition time of each sample text in the training set with the preset user's intention appeal;

[0065] The fusion analysis module 130 is used to obtain preset user voice and text data, divide the voice and text data and analyze its semantic features and user intention data information one by one, and the fusion analysis module includes a data statistics module 1301, an analysis module 1302, a verification module 1303 and a correction module 1304;

[0066] The data statistics module 1301 is used to collect historical intention demands of the preset user at the beginning of the month based on the voice data sent by the preset user to the customer service at the beginning of the month, and the historical intention demands include the services that the preset user needs to handle and / or consult;

[0067] The analysis module 1302 is used to analyze the word vector features between different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different business types;

[0068] The verification module 1303 is used to generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention. If the verification result is correct, the reply text is stored and uploaded to the recognition model. Otherwise, it is not stored and the reply text is regenerated until the verification result is correct.

[0069] The correction module 1304 is used to analyze the text data features between the reply text with a correct verification result and the reply text with a verification error, and when replying to the intended request of the user according to the business type to be handled by the preset user in the future period, the text data features contained in the reply text with a verification error are automatically removed from the reply text;

[0070] The data identification module 140 responds to the voice text data divided piece by piece by the fusion analysis module 130, and performs judgment and processing based on the acquired user intention data information and the corresponding number of voice text data to verify the intention analysis and recognition results of the number of voice text data.

[0071] A terminal comprises a processor, an input interface, an output interface and a memory, wherein the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute a method as claimed in any one of claims 1 to 7.

[0072] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 7.

[0073] In summary, the present invention utilizes deep learning analysis technology to obtain the historical intention demands of preset users, generate data training for voice data, and generate a recognition model based on this, so as to train and recognize the user's voice data in real time. At the same time, the user's voice text data is further divided, and the user's intention is determined according to the divided text data analysis and recognition, and the user's intention demand is accurately responded to. When a certain divided voice text data fails to correctly identify and judge the user's intention, the voice text data can be further divided for a second time to re-identify and judge the user's intention, thereby continuously improving and optimizing the intention recognition and answer quality, and improving the accuracy of user intention recognition and analysis and recognition efficiency.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying user intention and generating accurate answers, characterized in that: include: Based on big data, the historical intention demands of preset users are obtained, and a training set is generated from the voice data corresponding to the historical intention demands, each sample text in the training set is extracted, a recognition model is generated, and the sample text is input into the recognition model; In the recognition model, the training set is divided into a test set and a validation set, and the acquisition time of each sample text in the training set is marked with the preset user's intention demand, and the correlation between the sample text acquisition time and the preset user's intention demand is analyzed, including: Acquire voice data sent by a preset user to a customer service representative at the beginning of the month, and collect historical intentions and demands of the preset user at the beginning of the month based on the acquired voice data, wherein the historical intentions and demands include the services that the preset user needs to handle and / or consult; Collect word vector features between different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services, and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different service types; Generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention. If the verification result is correct, store the reply text and upload it to the recognition model. Otherwise, do not store it and regenerate the reply text until the verification result is correct. Analyze the text data features between the reply text with a correct verification result and the reply text with a verification error. When replying to the intended request of the user according to the type of business that the user needs to handle in the future period, automatically remove the text data features contained in the reply text with a verification error. Marking the acquired sample texts, inputting the distinguished data information into the recognition model, and updating the recognition model; Acquire preset user voice and text data, divide the voice and text data and analyze them one by one; The intention data information of the preset user is collected according to the divided voice and text data, and judgment and processing are performed based on the acquired user intention data information and the corresponding number of voice and text data.

2. A method for identifying user intention and generating accurate answers as claimed in claim 1, characterized in that: The obtained sample texts are differentiated, including being differentiated into corresponding business processing texts, complaint and suggestion texts, and business consultation texts, wherein the obtained sample texts are marked, including counting the number of inputs of historical voice texts by a preset user in each differentiated sample text data, and sorting each differentiated sample text data according to the number of inputs.

3. A method for identifying user intention and generating accurate answers as claimed in claim 1, characterized in that: Divide the speech text data into θ1, θ2, ..., θ n , where n represents the nth divided speech text data, the context word vectors between different speech text data are analyzed, and the currently divided speech text data are recorded and stored.

4. A method for identifying user intention and generating accurate answers as claimed in claim 3, characterized in that: Analyze the semantic features between each voice text data, and divide the semantic features into a first semantic feature, a second semantic feature and a third semantic feature, obtain the association rules between each voice feature, and input the association rules into the recognition model, wherein the semantic features include image semantic features.

5. A method for identifying user intention and generating accurate answers as claimed in claim 1, characterized in that: A judgment process is performed based on the acquired user intention data information and the corresponding number of voice and text data to verify the intention analysis and recognition results of the corresponding number of voice and text data. When the intention analysis and recognition results of the corresponding number of voice and text data are consistent with the acquired user intention data information, data features between the corresponding number of voice and text data and the user intention data information are further acquired, and the data features are summarized as correctly recognized data features. Conversely, when the intention analysis and recognition results of the corresponding number of voice and text data are inconsistent with the acquired user intention data information, the data features of the inconsistency are also acquired, and the data features are summarized as incorrectly recognized data features.

6. A method for identifying user intention and generating accurate answers as claimed in claim 5, characterized in that: Among the data features summarized as incorrectly identified, the voice text data corresponding to the data features are further divided for a second time, and a new semantic analysis is performed based on the secondarily divided voice text data. At the same time, the data is matched with the user intention data information to re-obtain the analysis result of the user's intention and generate a reply text.

7. A method for identifying user intention and generating accurate answers as claimed in claim 1, characterized in that: In the training set, the sample texts are trained using the stochastic gradient descent method, and correlation features between the preset user intentions and the sample texts are collected to generate a prediction model based on the correlation features.

8. A system for the method of identifying user intention and generating accurate answers as claimed in claim 1, characterized in that: include: A big data information acquisition module is used to obtain the historical intention demands of a preset user, and generate a training set with the voice data corresponding to the historical intention demands to generate a recognition model; A comprehensive data processing module is used to distinguish and mark the acquired sample texts, input the distinguished data information into the recognition model, update the recognition model, divide the training set into a test set and a validation set, and mark the acquisition time of each sample text in the training set with the preset user's intention appeal; A fusion analysis module is used to obtain preset user voice and text data, divide the voice and text data and analyze its semantic features and user intention data information one by one, and the fusion analysis module includes a data statistics module, an analysis module, a verification module and a correction module; The data statistics module is used to collect historical intention demands of the preset user in the period at the beginning of the month based on the voice data sent by the preset user to the customer service in the period at the beginning of the month, and the historical intention demands include the services that the preset user needs to handle and / or consult; The analysis module is used to analyze the word vector features between the different services that the preset user needs to handle and / or consult and the voice data sample text, and classify the different services into Class A services, Class B services and Class C services according to the historical intention demands of the preset user, and match the collected word vector features according to different business types; The verification module is used to generate a reply text according to the business type matched by the word vector feature, and verify the generated reply text with the preset user's intention and demand. If the verification result is correct, the reply text is stored and uploaded to the recognition model. Otherwise, it is not stored and the reply text is regenerated until the verification result is correct; The correction module is used to analyze the text data features between the reply text with a correct verification result and the reply text with a verification error, and when replying to the intended request of the user according to the type of business that the user needs to handle in the future period, the text data features contained in the reply text with a verification error are automatically removed from the reply text; The data identification module responds to the voice and text data divided piece by piece by the fusion analysis module, and performs judgment and processing based on the acquired user intention data information and the corresponding number of voice and text data to verify the intention analysis and recognition results of the number of voice and text data.

9. A terminal, characterized in that: It includes a processor, an input interface, an output interface and a memory, which are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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