Interaction method and device of dialogue model, computer device and storage medium

Abstract

The present disclosure relates to the technical field of computers, and discloses an interaction method and device of a dialogue model, a computer device and a storage medium. The method comprises: obtaining first feature information corresponding to a first target object and a second dialogue model matched with a first dialogue model; extracting key information from the first feature information to generate a question group corresponding to the key information; initiating a dialogue between the second dialogue model for any one target question in the question group; receiving answer information generated by the second dialogue model for the target question; and constructing an interactive dialogue between the first target object and a second target object based on the question and answer matching degree of the answer information and the target question. Through the implementation of the technical solution of the present disclosure, efficient personalized dialogue is realized, the personalized demands of the target object are met, the target object does not need to invest too much energy in communication, and the communication cost of the target object is saved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and specifically to an interaction method, apparatus, computer device, and storage medium for a dialogue model. Background Technology

[0002] Currently, most costs in the inquiry process are sunk costs. For example, a real estate agent needs to invest a lot of energy explaining detailed information about a property to many people. To reduce sunk costs, information recommendations, content reviews, FAQs, and intelligent customer service can be used to provide users with more complete, rich, and accurate information. However, these methods struggle to describe more detailed and personalized information, making it impossible to make judgments and decisions based solely on the described information. Therefore, intelligent question-and-answer methods such as information recommendations, reviews, FAQs, and intelligent customer service still cannot fully meet users' personalized needs, and both parties still need to invest time and effort in communication. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure provide an interaction method, apparatus, computer device, and storage medium for a dialogue model to solve the problem that intelligent question-answering methods cannot meet users' personalized needs.

[0004] In a first aspect, embodiments of this disclosure provide an interaction method for a dialogue model, used in a first dialogue model. The method includes: acquiring first feature information corresponding to a first target object and a second dialogue model matching the first dialogue model; wherein the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of a second target object; extracting key information from the first feature information and generating a question group corresponding to the key information; initiating a dialogue with the second dialogue model for any target question in the question group; receiving answer information generated by the second dialogue model for the target question; and constructing an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question.

[0005] The interaction method of the dialogue model provided in this disclosure obtains a second dialogue model that matches a first dialogue model, combines the feature information of a first target object to determine the corresponding question group, initiates a dialogue with the second dialogue model, receives answer information from the second dialogue model, and generates an interactive dialogue between the first and second target objects based on the question-answer matching degree between the answer information and the target question. This allows the first dialogue model to simulate dialogue between the first target object, and a real dialogue is only constructed when the target question and answer information match. The first and second dialogue models achieve efficient personalized dialogue, meeting the personalized needs of the target object and facilitating corresponding judgments and decisions through the dialogue model. This reduces the need for the first target object to expend excessive effort in communication, saving communication costs.

[0006] Secondly, embodiments of this disclosure provide an interaction method for a dialogue model, used in a second dialogue model. The method includes: obtaining a dialogue request initiated by a first dialogue model, and generating a dialogue between the first dialogue model and the second dialogue model based on the dialogue request; receiving a target question sent by the first dialogue model through the dialogue, and generating answer information for the target question; sending the answer information to the first dialogue model so that the first dialogue model determines the question-answer matching degree between the answer information and the target question, and constructing an interactive dialogue between a first target object and a second target object based on the question-answer matching degree.

[0007] The interaction method of the dialogue model provided in this disclosure involves a second dialogue model obtaining a dialogue request from a first dialogue model to generate a dialogue between the first and second dialogue models, thereby sending the answer information of the target question to the first dialogue model. This allows the second dialogue model to simulate a dialogue between a second target object and the first dialogue model, achieving efficient and personalized dialogue that meets the personalized needs of the second target object. This eliminates the need for the second target object to expend excessive effort in communication, saving communication costs.

[0008] Thirdly, embodiments of this disclosure provide an interactive device for a dialogue model, used for a first dialogue model. The device includes: a first acquisition module, used to acquire first feature information corresponding to a first target object and a second dialogue model matching the first dialogue model; wherein the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of a second target object; a question generation module, used to extract key information from demand information and generate a question group corresponding to the key information; a dialogue initiation module, used to initiate a dialogue with the second dialogue model for any target question in the question group; an answer receiving module, used to receive answer information generated by the second dialogue model for the target question; and an interaction module, used to construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question.

[0009] Fourthly, embodiments of this disclosure provide an interaction device for a dialogue model, used for a second dialogue model. The device includes: a second acquisition module, configured to acquire a dialogue request initiated by a first dialogue model and generate a dialogue between the first and second dialogue models based on the dialogue request; an answer generation module, configured to receive a target question sent by the first dialogue model through the dialogue and generate answer information for the target question; and an answer sending module, configured to send the answer information to the first dialogue model so that the first dialogue model determines the question-answer matching degree between the answer information and the target question, and constructs an interactive dialogue between a first target object and a second target object based on the question-answer matching degree.

[0010] Fifthly, embodiments of this disclosure provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the interaction method of the dialogue model of the first aspect or any corresponding embodiment described above, or to perform the interaction method of the dialogue model of the second aspect or any corresponding embodiment described above.

[0011] In a sixth aspect, embodiments of this disclosure provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the interaction method of the dialogue model of the first aspect or any corresponding embodiment described above, or to execute the interaction method of the dialogue model of the second aspect or any corresponding embodiment described above. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating an interaction method for a dialogue model according to some embodiments of the present disclosure;

[0014] Figure 2 This is an interactive schematic diagram of a dialogue model according to some embodiments of the present disclosure;

[0015] Figure 3 This is an interaction timing diagram of a dialogue model according to some embodiments of this disclosure;

[0016] Figure 4 This is a flowchart illustrating an interaction method for another dialogue model according to some embodiments of the present disclosure;

[0017] Figure 5 This is a flowchart illustrating an interaction method for another dialogue model according to some embodiments of the present disclosure;

[0018] Figure 6 This is a flowchart illustrating an interaction method for another dialogue model according to some embodiments of the present disclosure;

[0019] Figure 7 This is a structural block diagram of an interaction method apparatus for a dialogue model according to an embodiment of the present disclosure;

[0020] Figure 8 This is a structural block diagram of an interaction method apparatus for another dialogue model according to an embodiment of the present disclosure;

[0021] Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present disclosure. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0023] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0024] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0025] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0026] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0027] To reduce sunk costs in communication, information recommendations, content evaluations, FAQs, and intelligent customer service are commonly used to provide users with more complete, richer, and more authentic information. However, these methods struggle to describe more detailed and personalized information, making it impossible to make judgments and decisions based solely on descriptive information. Therefore, even with intelligent Q&A methods such as information recommendations, evaluations, FAQs, and intelligent customer service, it is still difficult to meet users' personalized needs, and both parties still need to invest time and effort in communication.

[0028] Based on this, this technical solution automatically constructs a sustainable learning dialogue model. The dialogue model integrates, extracts, and processes information based on interactive information to achieve the self-learning capability of the dialogue model. This facilitates the dialogue model to automatically initiate information matching, information acquisition, information derivation, and decision-making, thereby improving the self-interaction and decision-making capabilities of the dialogue model and saving human communication costs.

[0029] According to embodiments of this disclosure, an interactive method embodiment for a dialogue model is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides an interaction method for a dialogue model, which can be used in a first dialogue model built in a computer device application. Figure 1 This is a flowchart of an interaction method for a dialogue model according to an embodiment of the present disclosure, such as... Figure 1 As shown, the process includes the following steps:

[0031] Step S101: Obtain the first feature information corresponding to the first target object and the second dialogue model that matches the first dialogue model.

[0032] The first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object.

[0033] The first target object is the object used to construct the first dialogue model, which is a digital twin object model built by the first target object on the application client to achieve intelligent question answering. The first feature information is the relevant information published by the first target object on the application client, such as housing demand information, job demand information, transaction demand information, etc.

[0034] The second target object is an object different from the first target object. The second dialogue model is a digital twin object model built on the application client to realize intelligent question answering. There is a matching relationship between the second feature information of the second dialogue model and the first feature information of the first dialogue model. The first dialogue model combines its acquired first feature information with the second feature information of other dialogue models existing on the application to determine the first and second dialogue models that satisfy the matching relationship.

[0035] For example, if the first feature information corresponding to the first dialogue model is "wanting to buy a car of model A", and the first feature information corresponding to the second dialogue model is "selling a car of model A", then the first dialogue model and the second dialogue model constitute a feature matching relationship.

[0036] Step S102: Extract key information from the first feature information and generate the question group corresponding to the key information.

[0037] The key information refers to the keyword information carried in the first feature information. Specifically, here, high-frequency or meaningful keywords can be extracted from the text of the demand information using natural language processing methods (such as bag-of-words model, TF-IDF, and text summarization). Relationship extraction can also be used to extract relationships between text entities, such as purchasing relationships or employment relationships, and key information can be determined by extracting these entity relationships. Alternatively, text classification and clustering can be used to classify or cluster the text of the demand information according to specific attributes, thereby extracting a set of key information. Of course, regular expressions or template matching can also be used to extract the corresponding key information from the text of the demand information. The specific method for extracting key information is not limited here; those skilled in the art can determine it according to actual needs.

[0038] After obtaining key information, the first dialogue model can use natural language processing technology to transform the extracted key information into a set of open-ended questions; it can also perform reasoning or logical inference based on the extracted key information to generate a set of related questions. For example, if the key information is "Position B, salary system", then the question "What is the salary system for Position B?" can be generated.

[0039] Of course, natural language processing and machine learning techniques can be combined to improve the accuracy and diversity of question generation. No specific limitations are made here, as long as the question group is matched with the key information.

[0040] Step S103: For any target question in the question group, initiate a dialogue with the second dialogue model.

[0041] The question group contains multiple questions. The first dialogue model can sequentially treat each question as a target question and initiate a dialogue with the second dialogue model for each target question to obtain the corresponding response. Specifically, the first dialogue model can engage in multiple rounds of dialogue with the second dialogue model for each target question to obtain the most relevant information.

[0042] For example, if the key information is "Position B, salary system", then the question "What is the salary system for Position B?" can be generated. Combining the answer given by the second dialogue model, other questions related to "Position B, salary system" can be generated to obtain relevant answer information.

[0043] Step S104: Receive the answer information generated by the second dialogue model for the target question.

[0044] The second dialogue model has a corresponding knowledge base (e.g., a general knowledge base, a vertical knowledge base, etc.). When receiving a target question from the first dialogue model, it can retrieve the corresponding knowledge base of the second dialogue model, extract relevant knowledge corresponding to the target question from the knowledge base, and generate corresponding answer information, such as... Figure 2 As shown. Subsequently, the second dialogue model feeds back the generated answer information to the first dialogue model, and correspondingly, the first dialogue model can receive the answer information generated by the second dialogue model for the target question.

[0045] Step S105: Based on the question-answer matching degree between the answer information and the target question, construct an interactive dialogue between the first target object and the second target object.

[0046] Question-answer matching degree is used to characterize the degree of matching between answer information and target question. The first dialogue model has a corresponding knowledge base. The first dialogue model extracts key answer information for the target question from the answer information and combines it with the information in the knowledge base to determine whether the currently received answer information matches the target question.

[0047] If each target question and its corresponding answer have a high question-answer matching degree, it indicates that the first and second dialogue models have achieved a precise match. In this case, the first dialogue model can push the matching information to the first target object, and correspondingly, the second dialogue model can push the matching information to the second target object. Subsequently, the first and second target objects can build an interactive dialogue through the application client to achieve real interaction.

[0048] The interaction method of the dialogue model provided in this embodiment obtains a second dialogue model that matches the first dialogue model, determines the corresponding question group by combining the needs information of the first target object, initiates a dialogue with the second dialogue model, receives the answer information fed back by the second dialogue model, and generates an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question. Thus, the first dialogue model can simulate the dialogue between the first target object. Only when the target question and answer information are matched will a real dialogue between the first and second target objects be constructed. This dialogue model achieves efficient personalized dialogue, meeting the personalized needs of the first target object, thereby saving the first target object from expending excessive effort in communication and reducing communication costs.

[0049] This embodiment provides an interaction method for a dialogue model, which can be used in a first dialogue model built in a computer device application. Figure 2 This is a flowchart of an interaction method for a dialogue model according to an embodiment of the present disclosure, such as... Figure 3 As shown, the process includes the following steps:

[0050] Step S201: Obtain the first feature information corresponding to the first target object and the second dialogue model that matches the first dialogue model.

[0051] The first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object.

[0052] Specifically, step S201 above may include:

[0053] Step S2011: Obtain the first feature information corresponding to the first target object. For a detailed description of obtaining the first feature information, please refer to the relevant description in the above embodiments; it will not be repeated here.

[0054] Step S2012: Obtain the second feature information of other dialogue models.

[0055] The second feature information consists of relevant information published by the target object of other dialogue models on the application client, such as housing rental information or job postings. When the first dialogue model matches with other dialogue models, it can filter the information published on the application client to identify the second feature information published by other dialogue models.

[0056] Step S2013: Based on the feature matching degree between the first feature information and the second feature information, determine at least one second dialogue model that matches the first dialogue model from other dialogue models.

[0057] Feature matching degree is used to characterize the degree of matching between the first feature information and the second feature information. The first dialogue model can perform feature matching between its corresponding first feature information and the second feature information corresponding to other dialogue models to determine the feature matching degree between the first feature information and the second feature information.

[0058] By combining feature matching degree, one or more dialogue models that have a matching relationship with the first dialogue model are identified from other dialogue models that publish supply information, and these one or more dialogue models are identified as the second dialogue model that matches the first dialogue model.

[0059] As an optional implementation, the first feature information includes demand information, and the second feature information includes supply information; a supply-demand matching relationship is established between the first feature information and the second feature information. Accordingly, step S2013 above may include:

[0060] Step a1: Based on the demand information, determine the supply information that matches the demand information from other dialogue models.

[0061] Step a2: Identify at least one other dialogue model with supply information as a second dialogue model that matches the first dialogue model.

[0062] Demand information and supply information constitute a supply-demand matching relationship. The first dialogue model parses the second feature information published by other dialogue models on the application to identify supply information that has a supply-demand matching relationship with the demand information from the second feature information published by other dialogue models. Based on this supply-demand matching relationship, the second dialogue model that matches the first dialogue model is determined from the other dialogue models.

[0063] For example, if the demand information corresponding to the first dialogue model is applying for position A, then the supply information for recruiting for position A can be extracted from other dialogue models. If it is determined that the supply information corresponding to one or more other dialogue models includes recruiting employees who meet the requirements for position A, then those one or more other dialogue models are identified as the second dialogue model that matches the first dialogue model.

[0064] As an optional implementation, the above method may further include: updating a second dialogue model that matches the first dialogue model when demand information and / or supply information changes.

[0065] Since the first and second dialogue models achieve precise matching through demand and supply information, it is necessary to monitor in real time whether the demand information corresponding to the first dialogue model and the supply information corresponding to the second dialogue model have changed. When changes are detected in the demand information corresponding to the first dialogue model and / or the supply information corresponding to the second dialogue model, the matching relationship between the first and second dialogue models needs to be updated to ensure effective dialogue between them.

[0066] As an optional implementation, before obtaining the first feature information corresponding to the first target object, the above method may further include:

[0067] Step b1: In response to the login operation of the first target object, enter the dialogue model creation page based on the login operation.

[0068] Step b2: In response to the dialogue model creation operation generated on the dialogue model creation page, create a first dialogue model based on the dialogue model creation operation.

[0069] The login operation is triggered by the first target object on the application. When the first target object needs the dialogue model service provided by the application, it first needs to log in to the application. When the first target object triggers the login for the application, the application can respond accordingly and enter the dialogue model creation page after successful login.

[0070] The dialog model creation operation is a configuration operation for dialog model creation parameters triggered by the first target object on the dialog model creation page.

[0071] The dialog model creation page has relevant dialog model creation parameters. The first target object can configure each dialog model creation parameter. Correspondingly, the application can respond to the dialog model creation operation triggered by the first target object on the dialog model creation page and create the first dialog model according to the relevant configuration of the dialog model creation operation.

[0072] In the above implementation, by creating a first dialogue model, the question-and-answer interaction between the first target object and other objects or dialogue models is simulated, thus saving the communication time of the first target object.

[0073] Step S202: Extract key information from the first feature information to generate question groups corresponding to the key information. For detailed explanation, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0074] Step S203: For any target question in the question group, initiate a dialogue with the second dialogue model.

[0075] Specifically, step S203 above may include:

[0076] Step S2031: Sort at least one second dialogue model based on feature matching degree, and determine the target second dialogue model according to the sorting results.

[0077] As mentioned above, there is a corresponding feature matching degree between the first dialogue model and each of the second dialogue models. The second dialogue models are sorted from high to low or from low to high according to the feature matching degree to obtain the corresponding ranking results.

[0078] The target second dialogue model is a second dialogue model that constructs a dialogue with the first dialogue model. To select the most suitable dialogue model from multiple second dialogue models, such as... Figure 3 As shown, at this point, each second dialogue model can be assigned to the target second dialogue model and the first dialogue model for question-and-answer dialogue according to the sorting results.

[0079] Step S2032: Initiate dialogues on target questions to each target second dialogue model in sequence according to preset time intervals.

[0080] The preset time interval is a pre-defined time interval for sending dialogue requests. This preset time interval can be 5 minutes, 10 minutes, etc., and is not specifically limited here. Figure 4As shown, the first dialogue model initiates dialogues with the second target dialogue model one by one according to the preset time interval and the sorting results, so that each target second dialogue model can respond to the target question, and the first dialogue model can determine the most matching supply information by combining the response information of each target second dialogue model.

[0081] Step S204: Receive the answer information generated by the second dialogue model for the target question. For detailed explanation, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0082] Step S205: Based on the question-answer matching degree between the answer information and the target question, construct an interactive dialogue between the first target object and the second target object.

[0083] Specifically, step S205 above may include:

[0084] Step S2051: When the answer information successfully matches the target question, an interactive dialogue between the first target object and the second target object is directly generated.

[0085] After the first dialogue model receives the answer information from the second dialogue model, it determines whether the answer information and the target question are successfully matched by combining the question-answer matching degree. When the answer information and the target question are successfully matched, the matching degree between the demand information and the supply information is high. At this time, the first dialogue model can push the second target object corresponding to the second dialogue model to the first target object, and correspondingly, the second dialogue model can push the first target object corresponding to the first dialogue model to the second target object. Then, the first target object and the second target object engage in a real interactive dialogue in the dialogue constructed by the first dialogue model and the second dialogue model.

[0086] Step S2052: When the answer information does not match the target question, the target question is sent to the second target object through the second dialogue model so that the second target object can answer the target question and generate the corresponding target answer information.

[0087] When the answer information fails to match the target question (e.g., no answer or incorrect answer), meaning the answer information does not match the target question, the first dialogue model provides feedback to the second dialogue model indicating a mismatch to further clarify the matching degree between the second and first dialogue models. The second dialogue model can then push the target question to the second target object, enabling the second target object to provide an accurate response to the target question posed by the first dialogue model and obtain the corresponding target answer information.

[0088] Step S2053: Receive the target answer information sent by the second dialogue model.

[0089] The second target object feeds back the target answer information to the second dialogue model, which in turn sends the target answer information back to the first dialogue model through its dialogue channel with the first dialogue model. Figure 2 As shown. Correspondingly, the first dialogue model can receive the target answer information fed back by the second dialogue model.

[0090] Step S2054: When the target answer information successfully matches the target question, construct an interactive dialogue between the first target object and the second target object.

[0091] After the first dialogue model receives the target answer information from the second dialogue model, it determines whether the target answer information and the target question are successfully matched by considering the question-answer matching degree. When the target answer information and the target question are successfully matched, representing a successful match between demand information and supply information, the first dialogue model can push the second target object corresponding to the second dialogue model to the first target object, and correspondingly, the second dialogue model can push the first target object corresponding to the first dialogue model to the second target object. Then, the first target object and the second target object engage in a real interactive dialogue within the dialogue constructed by the first and second dialogue models, such as... Figure 2 As shown.

[0092] As an optional implementation, the above method may further include:

[0093] Step c1: When the target answer information successfully matches the target question, check the login status of the first target object.

[0094] Step c2: When the login status is not logged in, construct an interactive dialogue between the first dialogue model and the second target object.

[0095] The login status indicates whether the first target object is currently online. Since a genuine interactive dialogue with the second target object is only possible when the first target object is online, further detection of the first target object's login status is necessary after confirming a successful match between the target answer and the target question. If the first target object is logged in (i.e., online), an interactive dialogue between the first and second target objects is directly constructed. If the first target object is not logged in (i.e., offline), an interactive dialogue is constructed between the first dialogue model and the second target object. The first dialogue model simulates a question-and-answer session between the first and second target objects to obtain more relevant response information corresponding to the target question, allowing the first target object to filter responses based on this information after logging in.

[0096] In the above implementation, when the target answer information successfully matches the target question but the first target object is not logged in, the first dialogue model simulates a question-and-answer session between the first target object and the second target object, which facilitates continuous learning of the first dialogue model and improves its question-and-answer capabilities.

[0097] As an optional implementation, the above method may further include: when the target answer information does not match the target question, finding the next second dialogue model that matches the first dialogue model.

[0098] like Figure 3 As shown, when the target answer information fails to match the target question, the matching degree between the demand information and the supply information is low. At this time, the first dialogue model can initiate a dialogue with the next matching second dialogue model to determine whether the next second dialogue model can achieve an accurate match with the first dialogue model.

[0099] It should be noted that if a target question fails to match, the first dialogue model can continue to initiate dialogues for other target questions in order to evaluate and determine the question-answer matching degree between the second dialogue model and the first dialogue model by combining the answer information corresponding to each target question in the question group.

[0100] In the above implementation, when the target answer information fails to match the target question, the system continues to search for the next second dialogue model that matches the first dialogue model, so as to achieve a precise match between the first dialogue model and the second dialogue model, and facilitate the finding of matching supply information for the demand information of the first target object.

[0101] As an optional implementation, the above method may further include:

[0102] Step d1: Obtain the interactive dialogue information between the first target object and the second target object.

[0103] Step d2: Extract key dialogue information from the interactive dialogue information and update the first dialogue model using the key dialogue information.

[0104] Interactive dialogue information refers to the dialogue generated between the first target and the second target through real-person interaction. Key dialogue information refers to the crucial information related to the target question that arises during the current interactive dialogue.

[0105] The first dialogue model can generate corresponding question information based on the dialogue needs and preferences of the first target audience. For example... Figure 3As shown, the first dialogue model can extract information from interactive dialogue information to extract relevant key dialogue information. This key dialogue information is then used to update the first dialogue model, avoiding repetitive question-and-answer sessions. This enables the first dialogue model to learn continuously, facilitating the provision of more personalized services and recommendations to the target audience and enhancing its self-learning, self-interaction, and decision-making capabilities.

[0106] The interaction method of the dialogue model provided in this embodiment obtains a second dialogue model that matches the first dialogue model by using the feature matching degree between demand information and supply information. This achieves initial matching between the first and second dialogue models. Multiple second dialogue models are then ranked based on feature matching degree, and dialogue is initiated with each second dialogue model according to the ranking result. This facilitates information integration, extraction, and processing by the dialogue model based on question-and-answer interactions, thereby improving the self-learning ability of the dialogue model. Depending on whether the answer information matches the target question successfully, different methods are used to construct the interactive dialogue between the first and second target objects to achieve accurate matching between them.

[0107] This embodiment provides an interaction method for a dialogue model, which can be used in a second dialogue model built within a computer device application. Figure 5 This is a flowchart of an interaction method for a dialogue model according to an embodiment of the present disclosure, such as... Figure 5 As shown, the process includes the following steps:

[0108] Step S301: Obtain the dialogue request initiated by the first dialogue model, and generate a dialogue between the first dialogue model and the second dialogue model based on the dialogue request.

[0109] A dialogue request is a request generated by the first dialogue model when initiating a dialogue with the second dialogue model. For example... Figure 3 As shown, the first dialogue model sends a dialogue request to the second dialogue model. Correspondingly, the second dialogue model can receive the dialogue request sent by the first dialogue model and construct a dialogue between the first dialogue model and the second dialogue model based on the dialogue request, so as to carry out question-and-answer interaction between the first dialogue model and the second dialogue model through the dialogue.

[0110] Step S302: Receive the target question sent by the first dialogue model through dialogue, and generate the answer information for the target question.

[0111] As mentioned above, the first dialogue model sends the target question to the matching second dialogue model to obtain the second dialogue model's response to the target question. Specifically, after establishing a dialogue channel between the first and second dialogue models, the second dialogue model can receive the target question sent by the first dialogue model through this channel, and then generate the answer information corresponding to the target question based on the knowledge in its corresponding knowledge base.

[0112] Step S303: Send the answer information to the first dialogue model so that the first dialogue model can determine the question-answer matching degree between the answer information and the target question, and construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree.

[0113] The second dialogue model sends the answer information to the first dialogue model through a dialogue channel. The first dialogue model can then continue the current round of dialogue or move on to the next question-and-answer round based on the answer. Thus, the first dialogue model can determine the question-and-answer matching degree based on each target question and the received answer information, and combine this matching degree to determine whether the first and second dialogue models are precisely matched. If there is a precise match between the first and second dialogue models, an interactive dialogue is constructed between the first target object and the second target object.

[0114] The interaction method of the dialogue model provided in this embodiment involves the second dialogue model obtaining a dialogue request from the first dialogue model to generate a dialogue between the first and second dialogue models, thereby sending the answer information of the target question to the first dialogue model. This allows the second dialogue model to simulate a dialogue between a second target object and the first dialogue model, achieving efficient and personalized dialogue that meets the personalized needs of the second target object. This eliminates the need for the second target object to expend excessive effort in communication, saving communication costs.

[0115] This embodiment provides an interaction method for a dialogue model, which can be used in a second dialogue model built within a computer device application. Figure 6 This is a flowchart of an interaction method for a dialogue model according to an embodiment of the present disclosure, such as... Figure 6 As shown, the process includes the following steps:

[0116] Step S401: Obtain the dialogue request initiated by the first dialogue model, and generate a dialogue between the first dialogue model and the second dialogue model based on the dialogue request. For details, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0117] Step S402: Receive the target question sent by the first dialogue model through dialogue, and generate the answer information for the target question.

[0118] Specifically, step S402 above may include:

[0119] Step S4021: Check whether a solution to the target problem can be completed.

[0120] The second dialogue model retrieves knowledge from the knowledge base to determine whether the knowledge it can access can answer the target question, i.e., whether the knowledge in the knowledge base can match the answer to the target question. If it is determined that the second dialogue model can complete the answer to the target question, step S4022 is executed; otherwise, step S4023 is executed.

[0121] Step S4022: When a solution to the target question can be obtained, the answer information for the target question is generated through the second dialogue model.

[0122] When it is determined that the second dialogue model can complete the answer to the target question, it means that the knowledge possessed by the second dialogue model can match the target question. At this time, the second dialogue model can retrieve the knowledge in the knowledge base and generate the answer information corresponding to the target question.

[0123] Step S4023: When a solution cannot be found for the target question, the target question is pushed to the second target object.

[0124] When it is determined that the second dialogue model cannot complete the answer to the target question, it means that the knowledge possessed by the second dialogue model cannot match the target question. In this case, the second dialogue model can push the target question to the second target object corresponding to the second dialogue model so that the second target object can answer the target question.

[0125] Step S4024: In response to the answer generation operation of the second target object for the target question, the target answer information corresponding to the target question is obtained based on the answer generation operation.

[0126] The answer generation operation is the operation by which the second target object responds to the target question, and the target answer information is the answer information given by the second target object to the target question. Specifically, when the second target object receives the target question pushed by the second dialogue model, it can give a corresponding response to the target question and feed the response back to the second dialogue model.

[0127] Accordingly, the second dialogue model can respond to the second target object's answer generation operation for the target question, and determine the target answer information generated by the second target object for the target question based on the answer generation operation.

[0128] As an optional implementation, the above method may further include: extracting key answer information from the target answer information and updating the second dialogue model using the key answer information.

[0129] Key answer information refers to the crucial information contained in the target answer information, which matches the target question. The second dialogue model can perform question-and-answer based on the dialogue needs and preferences of the second target audience.

[0130] Specifically, after obtaining the target answer information generated by the second target object for the target question, the second dialogue model can extract key answer information and record it in the knowledge base corresponding to the second dialogue model to update the second dialogue model, so that the second dialogue model can continuously learn from questions and answers and avoid failing to answer related questions in the future.

[0131] Step S403: The answer information is sent to the first dialogue model so that the first dialogue model can determine the question-answer matching degree between the answer information and the target question, and construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree. For detailed explanation, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0132] As an optional implementation, during the interactive dialogue between the first target object and the second target object, the above method may further include: extracting key dialogue information from the interactive dialogue information between the first target object and the second target object, and updating the second dialogue model using the key dialogue information.

[0133] Interactive dialogue information refers to the dialogue generated between the first target and the second target through real-person interaction. Key dialogue information refers to the crucial information related to the target question that arises during the current interactive dialogue.

[0134] like Figure 3 As shown, the second dialogue model can extract information from interactive dialogues to extract relevant key dialogue information. This key dialogue information is then used to update the second dialogue model, enabling it to achieve continuous learning of question-and-answer interactions. This facilitates the provision of more personalized services and recommendations to the second target audience, thereby enhancing the second dialogue model's self-learning, self-interaction, and decision-making capabilities.

[0135] As an optional implementation, before constructing the dialogue between the first dialogue model and the second dialogue model, the above method may further include:

[0136] Step d1: Obtain the demand information corresponding to the first dialogue model and the supply information corresponding to the second dialogue model.

[0137] Step d2: Based on demand information and target supply information, determine the matching relationship between the first dialogue model and the second dialogue model.

[0138] The supply information refers to the information possessed by the second dialogue model, which is configured by the second target object when creating the second dialogue model. When the second dialogue model matches with the first dialogue model, it can obtain the demand information of the first dialogue model from the application client. Then, based on the obtained demand and supply information, it performs feature matching to determine the supply-demand matching relationship between the two models. Therefore, upon receiving a dialogue request from the first dialogue model, the second dialogue model can construct its dialogue with the first model based on the supply-demand matching relationship, thereby achieving an effective dialogue between the two models.

[0139] The interaction method of the dialogue model provided in this embodiment detects whether the second dialogue model can answer the target question, thereby determining whether the second target object needs to intervene to generate the corresponding answer information, which facilitates the accurate determination of the matching degree between the first target object and the second target object.

[0140] This embodiment also provides an interactive device for a dialogue model, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0141] This embodiment provides an interaction device for a dialogue model, used in a first dialogue model, such as... Figure 7 As shown, the device includes:

[0142] The first acquisition module 501 is used to acquire the first feature information corresponding to the first target object and the second dialogue model that matches the first dialogue model, wherein the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object.

[0143] The problem generation module 502 is used to extract key information from the requirement information and generate problem groups corresponding to the key information.

[0144] The dialogue initiation module 503 is used to initiate a dialogue with the second dialogue model for any target question in the question group.

[0145] The answer receiving module 504 is used to receive the answer information generated by the second dialogue model for the target question.

[0146] Interactive module 505 is used to construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question.

[0147] In some alternative embodiments, the first acquisition module 501 described above may include:

[0148] The information acquisition unit is used to acquire the second feature information of other dialogue models.

[0149] A matching unit is used to determine at least one second dialogue model that matches the first dialogue model from other dialogue models based on the feature matching degree between the first feature information and the second feature information.

[0150] In some alternative embodiments, the first feature information includes demand information, and the second feature information includes supply information. Accordingly, the matching unit may include:

[0151] The supply information determination subunit is used to determine the supply information that matches the demand information from other dialogue models based on the demand information.

[0152] A dialogue model matching subunit is used to identify at least one other dialogue model with supply information as a second dialogue model that matches the first dialogue model.

[0153] As an optional implementation, the first acquisition module 501 described above may further include:

[0154] The update unit is used to update the second dialogue model that matches the first dialogue model when the demand information and / or supply information changes.

[0155] As an optional implementation, the above-described apparatus may further include:

[0156] The login module is used to respond to the login operation of the first target object and enter the dialogue model creation page based on the login operation.

[0157] The dialogue model creation module is used to respond to the dialogue model creation operation generated on the dialogue model creation page and create the first dialogue model based on the dialogue model creation operation.

[0158] In some alternative embodiments, the dialogue initiation module 503 may include:

[0159] The sorting unit is used to sort at least one second dialogue model based on feature matching degree, and to determine the target second dialogue model in turn according to the sorting results.

[0160] The dialogue unit is used to initiate dialogues with each target second dialogue model in turn at preset time intervals, targeting specific questions.

[0161] In some alternative embodiments, the interactive module 505 described above may include:

[0162] The first interactive building unit is used to directly generate an interactive dialogue between the first target object and the second target object when the answer information successfully matches the target question.

[0163] The human-computer dialogue unit is used to send the target question to a second target object through a second dialogue model when the answer information does not match the target question, so that the second target object can answer the target question and generate the corresponding target answer information.

[0164] The information receiving unit is used to receive the target answer information sent by the second dialogue model.

[0165] The second interactive construction unit is used to build an interactive dialogue between the first target object and the second target object when the target answer information successfully matches the target question.

[0166] As an optional implementation, the interactive module 505 may further include:

[0167] The login detection unit is used to detect the login status of the first target object when the target answer information successfully matches the target question.

[0168] The third interactive building unit is used to build an interactive dialogue between the first dialogue model and the second target object when the login status is not logged in.

[0169] As an optional implementation, the above-described apparatus may further include:

[0170] The dialogue model finding module is used to find the next second dialogue model that matches the first dialogue model when the target answer information does not match the target question.

[0171] As an optional implementation, the above-described apparatus may further include:

[0172] The dialogue information acquisition module is used to acquire interactive dialogue information between the first target object and the second target object.

[0173] The first information extraction module is used to extract key dialogue information from interactive dialogue information and update the first dialogue model using the key dialogue information.

[0174] The further functional descriptions of each module and unit are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0175] In this embodiment, the interactive device of the dialogue model is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0176] The interactive device of the dialogue model provided in this embodiment obtains a second dialogue model that matches the first dialogue model, determines the corresponding question group by combining the needs information of the first target object, initiates a dialogue with the second dialogue model, receives the answer information fed back by the second dialogue model, and generates an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question. Thus, the first dialogue model can simulate the dialogue between the first target object, and the actual dialogue between the target object is only constructed when the target question and answer information are matched. This dialogue model achieves efficient personalized dialogue, meets the personalized needs of the first target object, and saves the first target object communication costs by eliminating the need for excessive effort in communication.

[0177] This embodiment provides an interaction device for a dialogue model, used in a second dialogue model, such as... Figure 8 As shown, the device includes:

[0178] The second acquisition module 601 is used to acquire the dialogue request initiated by the first dialogue model and generate a dialogue between the first dialogue model and the second dialogue model based on the dialogue request.

[0179] The answer generation module 602 is used to receive the target question sent by the first dialogue model through dialogue and generate the answer information for the target question.

[0180] The answer sending module 603 is used to send the answer information to the first dialogue model so that the first dialogue model can determine the question-answer matching degree between the answer information and the target question, and construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree.

[0181] As an optional implementation, the answer generation module 602 described above may include:

[0182] The detection unit is used to detect whether a solution to the target problem can be completed.

[0183] The first answer generation unit is used to generate answer information for the target question through the second dialogue model when it is able to complete the answer to the target question.

[0184] The question push unit is used to push the target question to a second target object when a solution cannot be found for the target question.

[0185] The second answer generation unit is used to respond to the answer generation operation of the second target object for the target question, and obtain the target answer information corresponding to the target question based on the answer generation operation.

[0186] As an optional implementation, the above-described apparatus may further include:

[0187] The second information extraction module is used to extract key answer information from the target answer information and to update the second dialogue model using the key answer information.

[0188] As an optional implementation, the above-described apparatus may further include:

[0189] The third information extraction module is used to extract key dialogue information from the interactive dialogue information between the first target object and the second target object, and to update the second dialogue model using the key dialogue information.

[0190] As an optional implementation, the above-described apparatus may further include:

[0191] The information acquisition module is used to acquire the demand information corresponding to the first dialogue model and the target supply information corresponding to the second dialogue model.

[0192] The matching determination module is used to determine the matching relationship between the first dialogue model and the second dialogue model based on demand information and target supply information.

[0193] The further functional descriptions of each module and unit are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0194] In this embodiment, the interactive device of the dialogue model is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0195] The interactive device of the dialogue model provided in this embodiment allows the second dialogue model to generate a dialogue between the first and second dialogue models by obtaining a dialogue request from the first dialogue model, thereby sending the answer information of the target question to the first dialogue model. This enables the second dialogue model to simulate a dialogue between a second target object and the first dialogue model, achieving efficient and personalized dialogue and satisfying the personalized needs of the second target object. This reduces the need for the second target object to expend excessive effort in communication, saving communication costs.

[0196] This disclosure also provides a computer device having the above-described features. Figure 7 or Figure 8 The interactive device of the dialogue model shown.

[0197] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of this disclosure, such as... Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0198] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0199] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0200] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0201] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0202] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0203] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0204] The computer device also includes a communication interface for communicating with other devices or communication networks.

[0205] This disclosure also provides a computer-readable storage medium in which the methods described in this disclosure can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium after being downloaded over a network. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0206] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An interaction method for a dialogue model, characterized in that, For a first dialogue model, the method includes: Obtain first feature information corresponding to the first target object and a second dialogue model that matches the first dialogue model; wherein, the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object; Extract key information from the first feature information and generate a question group corresponding to the key information; For any target question in the question group, initiate a dialogue with the second dialogue model; Receive the answer information generated by the second dialogue model for the target question; Based on the matching degree between the answer information and the target question, an interactive dialogue is constructed between the first target object and the second target object.

2. The method according to claim 1, characterized in that, Obtaining a second dialogue model that matches the first dialogue model includes: Obtain second feature information from other dialogue models; Based on the feature matching degree between the first feature information and the second feature information, at least one second dialogue model that matches the first dialogue model is determined from the other dialogue models.

3. The method according to claim 2, characterized in that, The first feature information includes demand information, and the second feature information includes supply information; the step of determining at least one second dialogue model matching the first dialogue model from the other dialogue models based on the feature matching degree between the first feature information and the second feature information includes: Based on the demand information, supply information that matches the demand information is determined from the other dialogue models; At least one other dialogue model having the supply information is identified as the second dialogue model that matches the first dialogue model.

4. The method according to claim 3, characterized in that, Also includes: When the demand information and / or supply information changes, the second dialogue model that matches the first dialogue model is updated.

5. The method according to claim 3 or 4, characterized in that, The step of initiating a dialogue with the second dialogue model for any target question in the question group includes: Based on the feature matching degree, at least one second dialogue model is sorted, and each second dialogue model is determined as a target second dialogue model according to the sorting result. Dialogues on the target questions are initiated sequentially to each of the target second dialogue models at preset time intervals.

6. The method according to claim 1, characterized in that, The step of constructing an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question includes: When the answer information successfully matches the target question, an interactive dialogue between the first target object and the second target object is directly generated. When the answer information does not match the target question, the target question is sent to the second target object through the second dialogue model, so that the second target object can answer the target question and generate the corresponding target answer information; Receive the target answer information sent by the second dialogue model; When the target answer information successfully matches the target question, an interactive dialogue is established between the first target object and the second target object.

7. The method according to claim 6, characterized in that, Also includes: When the target answer information successfully matches the target question, the login status of the first target object is detected; When the login status is not logged in, an interactive dialogue is constructed between the first dialogue model and the second target object.

8. The method according to claim 6, characterized in that, Also includes: If the target answer information does not match the target question, the next second dialogue model that matches the first dialogue model is searched.

9. The method according to claim 1, characterized in that, Also includes: Obtain interactive dialogue information between the first target object and the second target object; Extract key dialogue information from the interactive dialogue information, and use the key dialogue information to update the first dialogue model.

10. The method according to claim 1, characterized in that, Before obtaining the requirement information corresponding to the first target object, the process also includes: In response to the login operation of the first target object, the dialog model creation page is accessed based on the login operation; In response to a dialogue model creation operation generated on the dialogue model creation page, the first dialogue model is created based on the dialogue model creation operation.

11. An interaction method for a dialogue model, characterized in that, For the second dialogue model, the method includes: Obtain a dialogue request initiated by a first dialogue model, and generate a dialogue between the first dialogue model and a second dialogue model based on the dialogue request; wherein, the first dialogue model is a dialogue model established based on the first feature information of a first target object, and the second dialogue model is a dialogue model established based on the second feature information of a second target object. The system receives the target question sent by the first dialogue model through the dialogue and generates the answer information for the target question. The answer information is sent to the first dialogue model so that the first dialogue model can determine the question-answer matching degree between the answer information and the target question, and construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree.

12. The method according to claim 11, characterized in that, The step of receiving the target question sent by the first dialogue model through the dialogue and generating answer information for the target question includes: The test determines whether a solution to the target problem can be achieved. When a solution to the target question can be found, the second dialogue model generates the answer information for the target question. If a solution cannot be found for the target problem, the target problem will be pushed to the second target object. In response to the second target object's answer generation operation for the target question, target answer information corresponding to the target question is obtained based on the answer generation operation.

13. The method according to claim 12, characterized in that, Also includes: Extract key answer information from the target answer information, and use the key answer information to update the second dialogue model.

14. The method according to claim 11, characterized in that, Also includes: Extract key dialogue information from the interactive dialogue information between the first target object and the second target object, and use the key dialogue information to update the second dialogue model.

15. The method according to claim 11, characterized in that, Also includes: Obtain the demand information corresponding to the first dialogue model and the supply information corresponding to the second dialogue model; Based on the demand information and the supply information, the matching relationship between the first dialogue model and the second dialogue model is determined.

16. An interactive device for a dialogue model, characterized in that, For a first dialogue model, the device includes: The first acquisition module is used to acquire first feature information corresponding to the first target object and a second dialogue model that matches the first dialogue model; wherein, the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object. The question generation module is used to extract key information from the first feature information and generate a question group corresponding to the key information; The dialogue initiation module is used to initiate a dialogue with the second dialogue model for any target question in the question group. The answer receiving module is used to receive answer information generated by the second dialogue model for the target question; The interaction module is used to construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree between the answer information and the target question.

17. An interactive device for a dialogue model, characterized in that, For a second dialogue model, the device includes: The second acquisition module is used to acquire a dialogue request initiated by the first dialogue model and generate a dialogue between the first dialogue model and the second dialogue model based on the dialogue request; wherein, the first dialogue model is a dialogue model established based on the first feature information of the first target object, and the second dialogue model is a dialogue model established based on the second feature information of the second target object. The answer generation module is used to receive the target question sent by the first dialogue model through the dialogue and generate the answer information for the target question; The answer sending module is used to send the answer information to the first dialogue model so that the first dialogue model can determine the question-answer matching degree between the answer information and the target question, and construct an interactive dialogue between the first target object and the second target object based on the question-answer matching degree.

18. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the interaction method of the dialogue model according to any one of claims 1 to 10, or to perform the interaction method of the dialogue model according to any one of claims 11 to 15.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the interaction method of the dialogue model according to any one of claims 1 to 10, or to perform the interaction method of the dialogue model according to any one of claims 11 to 15.

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