Dialogue generation method and device based on application tool, electronic equipment and medium

Abstract

The embodiment of the invention provides a dialogue generation method and device based on an application tool, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for the field of financial science and technology. The method comprises the steps of obtaining initial dialogue data of a target object; performing intention recognition on the initial dialogue data to obtain initial dialogue intention data; performing task detection on the initial dialogue data based on the initial dialogue intention data, and determining a target task label; obtaining tool attribute data of the candidate application tools, and selecting the application tools based on the initial dialogue intention data, the target task tag and the tool attribute data to determine a target application tool; obtaining a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used for instructing the target object to execute a preset execution process; and performing dialogue generation based on the initial dialogue intention data and a preset process execution instruction. According to the embodiment of the invention, the dialogue generation accuracy related to the task matched with the application tool can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, is applicable to the field of financial technology, and in particular to a method and device for generating a dialogue based on an application tool, an electronic device, and a medium. Background Art

[0002] Application tools are designs used in intelligent systems to help users complete tasks or provide services. They can be independent applications or plug-ins integrated into other software. For example, in the claims system of financial technology, application tools can be claims information collection tools, claims information management tools, claims resource settlement tools, etc., and the users can be professionals such as insurance agents and brokers. In this way, users can operate different application tools in the claims system to perform different tasks, such as claims information collection, claims resource settlement, etc., thereby improving the efficiency of task completion.

[0003] At present, due to the fact that users are not proficient in the use of application tools, and the application tools included in the intelligent system are updated frequently, it is difficult for users to quickly and accurately master the specific use steps of the application tools, and thus they are unable to complete tasks, such as claim information collection, claim resource settlement, etc. At the same time, although the relevant technology can provide answers to questions for users in the intelligent dialogue scenario of the intelligent system, it does not associate dialogue generation with application tools, which easily reduces the accuracy of dialogue generation related to the tasks matched by the application tools. Therefore, how to improve the accuracy of dialogue generation related to the tasks matched by the application tools and avoid users being unable to complete tasks efficiently due to their lack of proficiency in the use of application tools has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device, electronic device, and medium for generating a conversation based on an application tool, aiming to improve the accuracy of conversation generation related to the task matched by the application tool, and to avoid the user being unable to complete the task efficiently due to lack of familiarity with the use of the application tool.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for generating a dialogue based on an application tool, the method comprising:

[0006] Acquire initial conversation data of a target object in a target application, wherein the target application is provided with candidate application tools, each of the candidate application tools has a candidate function, and the candidate functions of any two candidate application tools are different;

[0007] Performing intent recognition on the initial conversation data to obtain initial conversation intention data;

[0008] Performing task detection on the initial dialogue data based on the initial dialogue intention data to determine a target task tag of the target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process;

[0009] Acquire tool attribute data of the candidate application tool, and select an application tool for the candidate application tool based on the initial conversation intention data, the target task label and the tool attribute data, and determine a target application tool, wherein the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process;

[0010] Obtaining a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used to instruct the target object to execute the preset execution process;

[0011] A dialogue is generated based on the initial dialogue intention data and the preset process execution instruction.

[0012] In some embodiments, the generating of the dialogue based on the initial dialogue intention data and the preset process execution instruction includes:

[0013] Determining candidate process nodes of the preset execution process;

[0014] Extracting node data of the preset process execution instruction based on the candidate process node to obtain candidate node data of the candidate process node, wherein the candidate node data includes node processing intent and node input data, and different candidate process nodes have different node processing intents;

[0015] Selecting a node for the candidate process node based on the initial conversation intention data and the node processing intention, determining a target process node, and using the node input data of the target process node as target input data, wherein the intention indicated by the initial conversation intention data is the same as the node processing intention;

[0016] Performing data type detection on the target input data to obtain input data type;

[0017] A dialogue generation is performed on the initial dialogue intention data, the target input data and the input data type based on a preset dialogue generation model.

[0018] In some embodiments, the input data type includes a data upload type and a data selection type, the data upload type is used to indicate uploading data of a preset data type pre-configured at the target process node, and the data selection type is used to indicate data selection in preset task data configured at the target process node;

[0019] The performing dialogue generation on the initial dialogue intention data, the target input data and the input data type based on the preset dialogue generation model includes:

[0020] If the input data type is the data upload type, the node configuration parameters of the target process node are obtained, and a dialogue is generated based on the preset dialogue generation model for the initial dialogue intention data, the target input data and the node configuration parameters to obtain a first generated dialogue, wherein the first generated dialogue is used to guide the target object to upload data of the preset data type, and the node configuration parameters are conditions that must be met for data uploaded at the target process node;

[0021] If the input data type is the data selection type, the preset task data of the target process node is obtained, and a dialogue is generated for the initial dialogue intention data, the target input data and the preset task data based on the preset dialogue generation model to obtain a second generated dialogue, and the second generated dialogue is used to guide the target object to perform data selection on the preset task data.

[0022] In some embodiments, the method further comprises:

[0023] Acquire the reply data fed back by the target object, wherein the reply data is a reply file uploaded by the target object or a reply text input by the target object;

[0024] Sending the reply data to the calling interface of the target application tool, and obtaining data execution information of the target application tool on the reply data from the calling interface;

[0025] Determine the process execution state of the target task based on the data execution information;

[0026] Based on the process execution status, the preset process execution instruction is updated to obtain the current process execution instruction;

[0027] A dialogue is generated based on the current process execution instruction, the initial dialogue intention data and the reply data.

[0028] In some embodiments, selecting the candidate application tool based on the initial conversation intention data, the target task label, and the tool attribute data to determine the target application tool includes:

[0029] Performing multi-level intent recognition on the initial dialogue intention data and the target task label based on a preset multi-level attention hybrid model to obtain multi-level dialogue intention data;

[0030] Performing data encoding on the multi-level dialogue intention data to obtain a multi-level dialogue intention vector;

[0031] Performing data encoding on the tool attribute data to obtain a tool attribute vector;

[0032] Calculating similarity between the multi-level dialogue intention vector and the tool attribute vector to obtain vector similarity of the candidate application tool;

[0033] An application tool selection is performed on the candidate application tools based on the vector similarity to determine the target application tool.

[0034] In some embodiments, the performing multi-level intent recognition on the initial dialogue intention data and the target task label based on a preset multi-level attention hybrid model to obtain the multi-level dialogue intention data includes:

[0035] Extracting data features from the initial conversation intention data to obtain initial conversation intention features, wherein the initial conversation intention features include initial conversation intention sub-features, and the initial conversation intention sub-features are provided with initial interaction items, and the initial interaction items are used to indicate items associated with a conversation interaction;

[0036] Extracting label features of the target task labels to obtain target task features;

[0037] Combining the sub-features of the initial conversation intention based on a preset number of tail items to obtain a combined conversation intention feature;

[0038] Performing attention calculation based on the target task feature and the combined dialogue intention feature to obtain a combined attention feature;

[0039] Perform a linear transformation on the combined attention feature to obtain the multi-level dialogue intention data.

[0040] In some embodiments, performing task detection on the initial dialogue data based on the initial dialogue intention data to determine the target task label of the target object includes:

[0041] Extracting intention keywords from the initial conversation intention data to obtain conversation intention keywords;

[0042] Acquire candidate task keywords associated with preset candidate task tags from a preset tag library;

[0043] Calculating similarity between the conversation intention keyword and the candidate task keyword to obtain keyword similarity;

[0044] The target task tag of the target object is determined from the candidate task tags based on the keyword similarity.

[0045] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a dialog generation device based on an application tool, the device comprising:

[0046] A first acquisition module is used to acquire initial conversation data of a target object in a target application, wherein the target application is provided with candidate application tools, each of the candidate application tools has a candidate function, and the candidate functions of any two candidate application tools are different;

[0047] An identification module, used to perform intent identification on the initial conversation data to obtain initial conversation intent data;

[0048] A detection module, configured to perform task detection on the initial dialogue data based on the initial dialogue intention data, and determine a target task tag of the target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process;

[0049] a tool selection module, configured to obtain tool attribute data of the candidate application tool, and select an application tool for the candidate application tool based on the initial dialogue intention data, the target task label and the tool attribute data, and determine a target application tool, wherein the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process;

[0050] A second acquisition module is used to acquire a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used to instruct the target object to execute the preset execution process;

[0051] A dialogue generation module is used to generate a dialogue based on the initial dialogue intention data and the preset process execution instruction.

[0052] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the application tool-based dialogue generation method described in the first aspect when executing the computer program.

[0053] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the application tool-based dialogue generation method described in the first aspect above.

[0054] The present application proposes a method and device for generating a dialogue based on an application tool, an electronic device, and a medium, which obtains initial dialogue data of a target object in a target application, wherein the target application is provided with candidate application tools, each candidate application tool has a candidate function, and the candidate functions of any two candidate application tools are different; further, the initial dialogue data is subjected to intent recognition to obtain initial dialogue intent data; and task detection is performed on the initial dialogue data based on the initial dialogue intent data to determine a target task tag of the target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process; further, tool attribute data of the candidate application tool is obtained, and application tool selection is performed on the candidate application tool based on the initial dialogue intent data, the target task tag, and the tool attribute data to determine a target application tool, wherein the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process; and a preset process execution instruction of the target application tool is obtained; wherein the preset process execution instruction is used to instruct the target object to execute the preset execution process; further, dialogue generation is performed based on the initial dialogue intent data and the preset process execution instruction. Compared with the related art that requires the user to learn to use the application tool to complete the corresponding task, this application can associate the dialogue generation with the application tool, that is, the target object can use the dialogue method to call the corresponding application tool, and rely on the preset process execution instructions of the application tool to continuously generate dialogues, avoiding the tedious tool screening and manual operation process, saving time and reducing the risk of human error. Therefore, this application can effectively improve the accuracy of dialogue generation related to the task matched by the application tool, and avoid the user's lack of proficiency in the use of the application tool and the inability to complete the task efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of a method for generating a dialogue based on an application tool provided in an embodiment of the present application;

[0056] Figure 2 yes Figure 1 A flowchart of step S130 in FIG.

[0057] Figure 3 yes Figure 1 A flowchart of step S140 in FIG.

[0058] Figure 4 yes Figure 3 A flowchart of step S310 in FIG.

[0059] Figure 5 yes Figure 1 A flowchart of step S160 in FIG.

[0060] Figure 6 is another flow chart of the method for generating a dialogue based on an application tool provided in an embodiment of the present application;

[0061] Figure 7 is a specific flow chart of the method for generating a dialogue based on an application tool provided in an embodiment of the present application;

[0062] Figure 8 is a structural diagram of a conversation generation device based on an application tool provided in an embodiment of the present application;

[0063] Fig. 9 It is a hardware structure diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0067] First, some nouns involved in this application are analyzed:

[0068] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0069] Natural language processing (NLP): NLP uses computers to process, understand and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is often used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0070] Large Language Models (LLMs): A language model based on deep learning and natural language processing technology. Applying a pre-trained large language model to a specific task or dataset, and adjusting the model parameters through supervised learning, can adapt to the requirements of a specific task. The core of LLMs is the use of deep learning methods, and is usually based on the Transformer architecture, which can capture the complexity and diversity of language.

[0071] Application tools are designs used in intelligent systems to help users complete tasks or provide services. They can be independent applications or plug-ins integrated into other software. For example, in the claims system of financial technology, application tools can be claims information collection tools, claims information management tools, claims resource settlement tools, etc., and the users can be professionals such as insurance agents and brokers. In this way, users can operate different application tools in the claims system to perform different tasks, such as claims information collection, claims resource settlement, etc., thereby improving the efficiency of task completion.

[0072] At present, understanding and using application tools requires learning costs and operational thresholds. Although developers believe that they have designed products from the user's perspective as much as possible, due to the lack of familiarity with the use of application tools by the users and the frequent updates of the application tools included in the intelligent system, it is difficult for the users to quickly and accurately master the specific steps of using the application tools, and thus they cannot complete tasks such as claim information collection and claim resource settlement. At the same time, although the relevant technology can provide answers to questions for users in the intelligent dialogue scenario of the intelligent system, it does not associate dialogue generation with application tools, which can easily reduce the accuracy of dialogue generation related to the tasks matched by the application tools. Therefore, how to improve the accuracy of dialogue generation related to the tasks matched by the application tools and avoid the inability of users to efficiently complete tasks due to their lack of familiarity with the use of application tools has become a technical problem that needs to be solved urgently.

[0073] Based on this, the embodiments of the present application provide a method and device for generating a conversation based on an application tool, an electronic device, and a medium, which aim to improve the accuracy of conversation generation related to the tasks matched by the application tool, and avoid the situation where the user is not familiar with the use of the application tool and is unable to complete the task efficiently.

[0074] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0075] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0076] The dialogue generation method based on application tools provided in the embodiment of the present application relates to the field of artificial intelligence technology. The dialogue generation method based on application tools provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms; the software can be an application that implements the dialogue generation method based on application tools, etc., but is not limited to the above forms.

[0077] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on the object's historical data, object conversation data, object uploaded data and other data related to the object's identity or characteristics, the object's permission or consent will be obtained first, and the collection, use and processing of these data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain the sensitive personal information of the object, the object's separate permission or separate consent will be obtained through a pop-up window or jump to a confirmation page. After clearly obtaining the object's separate permission or separate consent, the necessary object-related data used to enable the normal operation of the embodiment of the present application will be obtained.

[0079] See also Figure 1 , Figure 1is an optional flow chart of the method for generating a dialogue based on an application tool provided in an embodiment of the present application. In some embodiments, Figure 1 The method may include but is not limited to steps S110 to S160:

[0080] Step S110, obtaining the initial conversation data of the target object in the target application;

[0081] Step S120, performing intent recognition on the initial conversation data to obtain initial conversation intent data;

[0082] Step S130, performing task detection on the initial dialogue data based on the initial dialogue intention data to determine the target task label of the target object;

[0083] Step S140, obtaining tool attribute data of the candidate application tools, and selecting application tools for the candidate application tools based on the initial dialogue intention data, the target task label and the tool attribute data, to determine the target application tool;

[0084] Step S150, obtaining a preset process execution instruction of the target application tool;

[0085] Step S160, generating a dialogue based on the initial dialogue intention data and the preset process execution instructions.

[0086] In steps S110 to S160 of some embodiments, the embodiments of the present application integrate other complex application tools into the background of the system (such as the claims system) by retaining the simple intelligent dialogue part. In this way, the background call processing can be quickly performed according to the intention requirements of the operation object, so that the target product required by the operation object can be directly output. In this way, the cumbersome tool screening and manual operation process can be avoided, saving time and reducing the risk of human error. In addition, the intelligent interaction method of the present application can effectively improve the accuracy of dialogue generation related to the task matched by the application tool, and avoid the user's lack of proficiency in the use of the application tool and the inability to complete the task efficiently.

[0087] In step S110 of some embodiments, the target application refers to an application that can conduct intelligent dialogue, such as the application corresponding to the insurance company's claims management system, the application corresponding to the digital medical system, etc. The target application is provided with candidate application tools, each candidate application tool has a candidate function, and the candidate functions of any two candidate application tools are different. For example, in the application corresponding to the claims management system of financial technology, the application tool can be a tool corresponding to different functions in the claims management system, such as a claims information collection tool, a claims information management tool, a claims resource settlement tool, etc. The target object can be an insurance agent, a broker and other professionals. For another example, the candidate function corresponding to the claims information collection tool can be a policy opening function, and the candidate function corresponding to the claims information management tool can be a customer claims information query. At this time, when the insurance agent wants to open a policy for his or her customer, the control corresponding to the claims information collection tool can be triggered in the application corresponding to the claims management system, and the step-by-step operation based on the policy opening process pre-set in this tool can be started.

[0088] It should be noted that the candidate functions included in the target application may also include marketing center, business opportunity filling, material submission, video interview, electronic signing, contract inquiry, order management, residual value assessment, etc., which can cover various business nodes in the entire business management process.

[0089] It should be noted that the initial dialogue data refers to the data of the target object's dialogue interaction with the intelligent robot in the dialogue window of the target application. In other words, the initial dialogue data is the entire interaction process between the target object and the target application, not just a single input, but the context of the entire dialogue, so that the input of the target object can be better understood to provide an appropriate response. In actual applications, the target object can open a dialogue window in the target application and input data such as text or images into the system through dialogue, so as to realize the function of the target object and the target application providing task goals or requirements through natural language interaction.

[0090] In step S120 of some embodiments, in order to understand the task goal of the target object, such as electronic signing, contract query, etc., the present application can further perform intent recognition on the initial dialogue data to obtain initial dialogue intent data. The initial dialogue intent data is used to characterize the goal or intention of the target object in the initial dialogue data, and the initial dialogue intent data can be a label or a more detailed intent description, which provides a basis for subsequent processing and response.

[0091] It should be noted that this application can use natural language processing technology, such as text classification and named entity recognition based on linguistic rules, machine learning models, etc., to analyze and understand the initial dialogue data input by the target object to identify the intention behind it, so as to determine the task the target object wants to perform or the information it wants to obtain. For example, the target object inputs the initial dialogue data as "How do I know the company's A realization rate?" At this time, the target application can start the dialogue generation method based on the application tool to guide the target object to enter more information, so as to accurately identify whether the target object has a simple search intention or a comparative analysis intention, and then call the corresponding application tool to implement the function.

[0092] In step S130 of some embodiments, after obtaining the initial conversation intention data, the present application may perform task detection on the initial conversation data based on the initial conversation intention data to determine the specific task of the target object, that is, to determine the target task tag. Among them, each task is associated with a task tag, which represents the task function, and each task tag is configured with a preset execution process, that is, the steps required to complete the task. The preset execution process is a series of standardized steps designed to complete a task to ensure that the task can be completed in the expected manner. For example, the target object is a simple search intention, and the task function corresponding to the target task tag at this time can be "contract query". At this time, the preset execution process corresponding to the task function may include: entering query information and query conditions, and then entering further screening conditions based on the returned data.

[0093] In some embodiments, see Figure 2 , Figure 2 This is an optional flowchart of step S130 provided in an embodiment of the present application. Wherein, step S130 may specifically include but is not limited to steps S210 to S240:

[0094] Step S210, extracting intention keywords from the initial conversation intention data to obtain conversation intention keywords;

[0095] Step S220, obtaining candidate task keywords associated with preset candidate task tags from a preset tag library;

[0096] Step S230, calculating the similarity between the conversation intention keywords and the candidate task keywords to obtain the keyword similarity;

[0097] Step S240 , determining a target task tag of the target object from the candidate task tags based on the keyword similarity.

[0098] In step S210 of some embodiments, when determining the target task label, the present application may first perform intent keyword extraction on the initial conversation intent data to extract conversation intent keywords that are more representative of the main needs or purposes of the target object.

[0099] In step S220 of some embodiments, the present application may further access a preset tag library, which contains candidate task keywords associated with each candidate task tag. These candidate task keywords are predefined and used to describe and distinguish different tasks.

[0100] In step S230 of some embodiments, further, similarity calculation may be performed between the conversation intention keyword and the candidate task keyword of the candidate task tag. The similarity calculation method may include cosine similarity, Jaccard similarity, etc., to quantify the similarity between the conversation intention keyword and the candidate task keyword, so as to more accurately detect the target task tag of the target object.

[0101] In step S240 of some embodiments, further, the keyword similarities of multiple candidate task keywords can be numerically compared, and the candidate task tag corresponding to the largest keyword similarity can be used as the target task tag. Alternatively, the present application can arrange the keyword similarities of multiple candidate task keywords in descending order, and filter out the most relevant target task tags based on a preset similarity threshold. In other words, when performing tag matching based on a preset similarity threshold, if the matching result is empty, intent recognition can be re-performed or the preset tag library can be updated to improve the accuracy of task detection.

[0102] In the above embodiment, in order to more accurately match the intent of the target object with the tasks that the target application can perform, the present application can be implemented through keyword similarity calculation, thereby improving the target application's understanding of the target object's intent and providing more accurate services or responses.

[0103] In step S140 of some embodiments, the tool attribute data is used to characterize the characteristics and capabilities of the corresponding candidate application tool. The tool attribute data may include the function description, performance indicators, user evaluation, compatibility information, etc. of the candidate application tool, without specific limitation. The target application tool is the tool that best matches the target task label selected from the candidate application tools based on the initial dialogue intention data, the target task label and the tool attribute data. The candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process.

[0104] It should be noted that the present application can select the most suitable one from multiple candidate application tools to perform the task according to the task requirements of the target object, and this tool can complete the task in the expected manner. In addition, the process of determining the target application tool in the present application is equivalent to conversation-based function recommendation, that is, the conversation-based function recommendation module can be started according to the intent recognition result (i.e., the initial conversation intent data). This module evaluates the current conversation stage and context information, and selects a suitable tool from a series of available tools based on a multi-level reasoning strategy to locate a solution that better meets the needs of the end user.

[0105] In some embodiments, see Figure 3 , Figure 3 This is an optional flow chart of step S140 provided in the embodiment of the present application. Wherein, step S140 may specifically include but is not limited to steps S310 to S350:

[0106] Step S310, performing multi-level intent recognition on the initial dialogue intention data and the target task label based on a preset multi-level attention hybrid model to obtain multi-level dialogue intention data;

[0107] Step S320, encoding the multi-level dialogue intention data to obtain a multi-level dialogue intention vector;

[0108] Step S330, encoding the tool attribute data to obtain a tool attribute vector;

[0109] Step S340, calculating the similarity between the multi-level dialogue intention vector and the tool attribute vector to obtain the vector similarity of the candidate application tool;

[0110] Step S350 , selecting an application tool for the candidate application tools based on the vector similarity, and determining a target application tool.

[0111] In step S310 of some embodiments, the present application may adopt a multi-level reasoning strategy (i.e., a multi-level attention hybrid model) to help prune a large amount of search space, avoid local optimal solutions, and converge to a more satisfactory solution by considering the overall behavior trend of the user (i.e., considering the initial dialogue intent data and the target task label). Specifically, the present application may use a preset multi-level attention hybrid model to process the initial dialogue intent data and the target task label. This model can recognize and understand multiple levels of intent in a dialogue, such as surface intent, deep intent, and implicit intent. This model can extract richer and more detailed intent information to form multi-level dialogue intent data.

[0112] It should be noted that the multi-level of this application refers to understanding the user's intention at different levels of abstraction. Surface intent refers to the needs or questions directly expressed by the target object. Deep intent refers to the intention that may not be directly expressed by the target object, but can be inferred through the context. Implicit intent refers to the potential needs that may not be explicitly expressed by the target object, but are inferred by the system through information such as the target object's behavior pattern and historical interactions.

[0113] It should be noted that in order to obtain these high-level concepts, two SBR-related inductive biases, namely local invariance and inherent priority, are used to reduce the search space. It can be understood that the multi-level attention hybrid model used in this application can be a model built based on the Atten-Mixer model, a large model, etc., without limitation.

[0114] In some embodiments, see Figure 4 , Figure 4 This is an optional flowchart of step S310 provided in the embodiment of the present application. Wherein, step S310 may specifically include but is not limited to steps S410 to S45:

[0115] Step S410, extracting data features from the initial conversation intention data to obtain initial conversation intention features;

[0116] Step S420, extracting label features of the target task label to obtain target task features;

[0117] Step S430, combining the initial conversation intention sub-features based on a preset number of tail items to obtain a combined conversation intention feature;

[0118] Step S440, performing attention calculation based on the target task feature and the combined dialogue intention feature to obtain a combined attention feature;

[0119] Step S450, perform linear transformation on the combined attention features to obtain multi-level dialogue intention data.

[0120] In step S410 of some embodiments, the present application may extract key features from the initial conversation intention data, which are referred to as initial conversation intention features. These features are further decomposed into smaller units, namely, initial conversation intention sub-features. Each sub-feature is associated with a specific initial interaction item, which indicates a specific interaction point or topic in the conversation, that is, the initial interaction item is used to indicate the item associated with a conversation interaction.

[0121] In step S420 of some embodiments, the present application also performs feature extraction on the target task label, and these features can describe the attributes or characteristics of the task corresponding to the target task label.

[0122] In step S430 of some embodiments, further, the present application may combine the initial conversation intention sub-features into a larger feature set according to preset rules, such as a preset number of tail items, called a combined conversation intention feature, which helps to obtain more complex patterns and relationships in the conversation. In other words, the multi-level attention hybrid model can form groups by different numbers of preset tail items. These groups are used to construct related high-level concepts, thereby generating group representations.

[0123] In steps S440 and S450 of some embodiments, the present application may further use the target task features and the combined conversation intention features to calculate the attention weights to determine which parts of the conversation are more important for understanding the user's intentions and completing the task. Finally, the combined attention features are linearly transformed to generate multi-level conversation intention data. This transformation may involve weight adjustment, feature scaling, or other mathematical operations to convert the features into a form suitable for subsequent processing, such as input into a classifier or prediction model.

[0124] In the above embodiments, the present application converts the original dialogue intention data into structured, information-rich multi-level dialogue intention data through feature extraction, feature combination and attention calculation. These data can be used in more complex dialogue systems and machine learning tasks, helping to improve the system's understanding of user intentions and optimize the efficiency of dialogue management and task execution.

[0125] In step S320 of some embodiments, after identifying the multi-level dialogue intention data, the present application may encode the multi-level dialogue intention data into a vector form, i.e., a multi-level dialogue intention vector. This encoding typically involves converting text data into a numerical representation so that the machine learning model can process it. This process may include techniques such as word embedding (such as Word2Vec or BERT) to convert text into a fixed-length vector.

[0126] In step S330 of some embodiments, the present application may also encode the tool attribute data into a vector form, namely a tool attribute vector. This step involves converting the tool attributes (such as function description, performance index, etc.) into numerical representations for subsequent similarity calculations.

[0127] In step S340 and step S350 of some embodiments, further, the present application may calculate the similarity between the multi-level dialogue intention vector and the tool attribute vector to quantify the degree of match between the dialogue intention and the tool attribute. And select the most suitable candidate application tool based on the calculated vector similarity. This selection process may involve selecting the tool with the highest similarity, or determining the most relevant tool based on a certain threshold. The target application tool finally determined is the tool that best matches the user's intention.

[0128] In the above embodiments, the present application achieves accurate selection of candidate application tools through multi-level intent recognition and vector similarity calculation to ensure that the system can provide the most relevant services or responses to users, thereby improving the accuracy of the system's understanding and response to user intentions.

[0129] In step S150 of some embodiments, after determining the target application tool, the present application can directly call the selected tool and guide the target object to input the data required by the tool, that is, according to the needs of the tool, guide the target object to input relevant information in the dialogue interface. Specifically, the preset process execution instruction of the target application tool can be obtained first, and the preset process execution instruction is used to instruct the target object to execute the preset execution process.

[0130] In step S160 of some embodiments, further, the present application may use the target object's initial intention data and preset process execution instructions to generate a dialogue to guide the target object to complete the required task or provide further guidance.

[0131] It should be noted that the dialogue interface of the target application of this application can support multi-modal input, that is, the input data can be pictures, files, text, etc., without limitation. After the target object enters a message, the instructions can be executed according to the update preset process to guide the target object to enter new data until the task function is completed.

[0132] In some embodiments, see Figure 5 , Figure 5 This is an optional flowchart of step S160 provided in an embodiment of the present application. Wherein, step S160 may specifically include but is not limited to steps S510 to S550:

[0133] Step S510, determining a candidate process node of a preset execution process;

[0134] Step S520, extracting node data of the preset process execution instruction based on the candidate process node to obtain candidate node data of the candidate process node;

[0135] Step S530, selecting a node for a candidate process node based on the initial dialog intention data and the node processing intention, determining a target process node, and using the node input data of the target process node as the target input data;

[0136] Step S540, performing data type detection on the target input data to obtain the input data type;

[0137] Step S550, performing dialogue generation on the initial dialogue intention data, target input data and input data type based on a preset dialogue generation model.

[0138] In step S510 of some embodiments, the present application may first identify all candidate process nodes contained in the preset execution process, and these candidate process nodes represent various stages or steps in the preset execution process. Each candidate process node corresponds to a specific processing task or decision point. The preset process execution instruction is the guidance information that instructs the target object to execute the preset execution process.

[0139] In step S520 of some embodiments, the candidate node data includes node processing intentions and node input data, and different candidate process nodes have different node processing intentions. The present application can extract data related to each candidate process node from the preset process execution instructions, which include node processing intentions (i.e., the tasks or goals that the node needs to complete) and node input data (i.e., the content type, information format, etc. of the input information required to execute the node task). Different candidate process nodes may have different processing intentions, so their node input data will also be different.

[0140] In step S530 of some embodiments, the intention indicated by the initial conversation intention data is the same as the node processing intention. The present application can match the initial conversation intention data with the processing intention of the candidate process node to determine which target process node is most suitable for processing the current conversation intention, and the selection of this target process node must also comply with the node execution order of the preset execution process. Once a matching node is found, the node is determined as the target process node, and its corresponding node input data is selected as the target input data for subsequent processing.

[0141] In step S540 of some embodiments, the present application may analyze the determined target input data to identify the required input data type. The input data type may include different types of data such as text, numbers, dates, pictures, files, etc.

[0142] In step S550 of some embodiments, the present application may further use a preset dialogue generation model to generate a dialogue in response to the target object by combining the initial dialogue intention data, the target input data, and the identified input data type. This dialogue generation model may be based on machine learning or natural language processing technology, and can generate natural and appropriate responses based on the input data and context.

[0143] In some embodiments, the input data type includes a data upload type and a data selection type. The data upload type is used to indicate uploading data of a preset data type pre-configured in the target process node, and the data selection type is used to indicate selecting data from the preset task data configured in the target process node. At this time, the process of generating a dialogue based on the preset dialogue generation model for the initial dialogue intention data, the target input data, and the input data type may specifically include:

[0144] If the input data type is a data upload type, the node configuration parameters of the target process node are obtained, and a dialogue is generated based on the initial dialogue intention data, the target input data and the node configuration parameters based on the preset dialogue generation model to obtain a first generated dialogue, where the first generated dialogue is used to guide the target object to upload data of the preset data type, and the node configuration parameters are conditions that must be met for the data uploaded at the target process node;

[0145] If the input data type is a data selection type, the preset task data of the target process node is obtained, and a dialogue is generated for the initial dialogue intention data, the target input data and the preset task data based on the preset dialogue generation model to obtain a second generated dialogue. The second generated dialogue is used to guide the target object to perform data selection on the preset task data.

[0146] It should be noted that the data upload type is used to indicate that the target object needs to upload non-text data such as pictures and files in the dialog box of the dialog window, and the preset data type refers to at least one type of pictures, files, etc. The data selection type is used to indicate that the target object needs to select the output data from the preset task data configured by the target process node output according to the historical dialogue in the dialog box of the dialog window. In other words, if the input data type is the data upload type, the initial dialogue intention data, the target input data and the node configuration parameters are generated based on the preset dialogue generation model to obtain the first generated dialogue. The first generated dialogue is used to guide the target object to upload data of the preset data type, and the node configuration parameters are the conditions that the data uploaded at the target process node must meet. At this time, the target object can send pictures or files based on the preset data type to the dialog box, and the target application can directly upload these contents to the target application tool in the background, and automatically script a series of processing of the tool.

[0147] If the input data type is a data selection type, a dialogue is generated based on the preset dialogue generation model for the initial dialogue intention data, the target input data, and the preset task data to obtain a second generated dialogue, which is used to guide the target object to select data for the preset task data, and the second generated dialogue contains the preset task data. At this time, the target object can reply to the selected data in the dialog box, and the whole process does not involve the target object jumping to the target application tool to perform operations.

[0148] In some embodiments, see Figure 6 , Figure 6 It is another optional flow chart of the method for generating a conversation based on an application tool provided in an embodiment of the present application. After step S160, the method for generating a conversation based on an application tool provided in an embodiment of the present application may further include steps S610 to S650:

[0149] Step S610, obtaining the response data fed back by the target object;

[0150] Step S620, sending the reply data to the calling interface of the target application tool, and obtaining data execution information of the target application tool on the reply data from the calling interface;

[0151] Step S630, determining the process execution state of the target task based on the data execution information;

[0152] Step S640, updating the preset process execution instruction based on the process execution status to obtain the current process execution instruction;

[0153] Step S650, generating a dialogue based on the current process execution instruction, the initial dialogue intention data and the response data.

[0154] In step S610 and step S620 of some embodiments, when generating a dialogue, the present application can continuously update the generated dialogue according to the requirements of the task function in the target application tool. Specifically, the present application can obtain the reply data currently fed back by the target object in real time. The reply data is a reply file uploaded by the target object (such as PDF, Word document, etc.) or a reply text directly entered in the dialog box. Further, these reply data are sent to the calling interface of the target application tool. This interface can be an application programming interface (API), which allows the system to communicate and exchange data with external application tools. Through this interface, the system requests the target application tool to perform specific processing or analysis on the reply data.

[0155] In step S630 of some embodiments, after sending the reply data to the target application tool, the system receives the result information of the target application tool performing the operation on the reply data from the calling interface. This information may include processing status, analysis results or other feedback, and this data execution information is crucial for determining subsequent steps. Furthermore, the process execution status of the target task can be determined based on the data execution information received from the target application tool. This status may indicate whether the task has been completed, whether further operation is required, or whether there are errors or abnormal conditions, etc.

[0156] In step S640 of some embodiments, the present application may then update the preset process execution instructions according to the determined process execution status. These instructions guide the system on how to continue to execute subsequent steps or adjust the process path according to the current execution status.

[0157] In step S650 of some embodiments, the current process execution instruction, the target object's initial dialogue intention data, and the user's reply data may be used to generate a dialogue. This dialogue may be a confirmation of the user's reply, a further inquiry, or the provision of information required to complete the task, ensuring the continuity and relevance of the dialogue while maintaining user engagement.

[0158] In some specific embodiments, see Figure 7 , Figure 7 It is a specific flow chart of the dialogue generation method based on the application tool provided in the embodiment of the present application. In practical applications, the present application can first start a dialogue in the target application, and the target application can perform intent recognition on the initial dialogue data input by the target object to obtain initial dialogue intent data. Furthermore, the target application can perform multi-level intent recognition on the initial dialogue intent data and the target task label based on a multi-level attention hybrid model to determine the target application tool. Furthermore, the generated dialogue can call the target application tool to guide the target object to input the data required by the tool. When the auxiliary process of the dialogue system ends, the output generated by the target application tool can be passed into the dialog box via the original environment.

[0159] The embodiment of the present application provides a method for generating a dialogue based on an application tool, which describes the task goal or requirement by having a dialogue with the system, and the target object does not need to directly operate complex tools to complete the task. This intelligent interaction method provides a more convenient, fast and personalized user experience. Secondly, the present application can use natural language processing technology to parse and understand user input, and select the corresponding tool according to the intention. This process avoids the cumbersome tool screening and manual operation process, saves time and reduces the risk of human error. In addition, the function recommendation module based on the session of the present application adopts a multi-level reasoning strategy as a decision-making mechanism, and considers contextual information to improve the quality of decision, which helps to provide a more comprehensive and accurate matching demand. Among them, the present application can adopt a multi-level attention hybrid model based on the Atten-Mixer model structure, which not only processes a single user input, but analyzes it in the context of the entire session. The model can understand the changes in user intentions over time and take into account historical interactions when generating responses. Combined with dynamic context fusion technology, the technology can update the dialogue context in real time and integrate it into the current user intention recognition process. For example, customized word embedding layers are developed for professional terms in the insurance field. These embedding layers are pre-trained on large-scale insurance corpora to enhance the model's understanding of professional terms. And the weight distribution strategy in the attention mechanism is adjusted to focus more on key insurance information in user input to improve the accuracy of intent recognition. In addition, the present application can support multimodal input (such as text, pictures, files, etc.) and introduce local invariance reasoning in the model, which can maintain attention on key information during user intent recognition and maintain focus even in the face of complex dialogue processes. Furthermore, the present application can implement a strategy based on priority reasoning, which can dynamically adjust the order of recommendation tools according to the importance and urgency of user intent. Therefore, compared to the related art that requires the user to learn to use application tools to complete the corresponding task, the present application can associate dialogue generation with application tools, that is, the target object can use dialogue to call the corresponding application tool, and rely on the preset process execution instructions of the application tool to continuously generate dialogues, avoiding tedious tool screening and manual operation processes, saving time and reducing the risk of human error, thereby effectively improving the accuracy of dialogue generation related to the task matched by the application tool, and avoiding the situation where the user is not proficient in the use of the application tool and cannot complete the task efficiently.

[0160] See also Figure 8 The embodiment of the present application further provides a device for generating a dialogue based on an application tool, which can implement the above-mentioned method for generating a dialogue based on an application tool, and the device includes:

[0161] A first acquisition module 810 is used to acquire the initial conversation data of the target object in the target application, where the target application is provided with candidate application tools, each candidate application tool has a candidate function, and the candidate functions of any two candidate application tools are different;

[0162] The recognition module 820 is used to perform intent recognition on the initial dialogue data to obtain initial dialogue intent data;

[0163] A detection module 830 is used to perform task detection on the initial dialogue data based on the initial dialogue intention data to determine a target task tag of a target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process;

[0164] The tool selection module 840 is used to obtain tool attribute data of the candidate application tools, and select the candidate application tools based on the initial dialogue intention data, the target task label and the tool attribute data, and determine the target application tool, the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process;

[0165] The second acquisition module 850 is used to acquire a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used to instruct the target object to execute a preset execution process;

[0166] The dialogue generation module 860 is used to generate a dialogue based on the initial dialogue intention data and the preset process execution instructions.

[0167] The specific implementation of the application tool-based conversation generation device is basically the same as the specific implementation of the application tool-based conversation generation method described above, and will not be repeated here.

[0168] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned application tool-based dialogue generation method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0169] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0170] The processor 910 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0171] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the dialog generation method based on the application tool in the embodiment of this application;

[0172] Input / output interface 930, used to implement information input and output;

[0173] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0174] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0175] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0176] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned application tool-based dialogue generation method is implemented.

[0177] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0178] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0179] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0180] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0182] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0183] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0185] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0188] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for generating a dialogue based on an application tool, characterized in that: The method comprises: Acquire initial conversation data of a target object in a target application, wherein the target application is provided with candidate application tools, each of the candidate application tools has a candidate function, and the candidate functions of any two of the candidate application tools are different; Performing intent recognition on the initial conversation data to obtain initial conversation intention data; Performing task detection on the initial dialogue data based on the initial dialogue intention data to determine a target task tag of the target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process; Acquire tool attribute data of the candidate application tool, and select an application tool for the candidate application tool based on the initial dialogue intention data, the target task label and the tool attribute data, and determine a target application tool, wherein the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process; Obtaining a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used to instruct the target object to execute the preset execution process; A dialogue is generated based on the initial dialogue intention data and the preset process execution instruction.

2. The method according to claim 1, characterized in that The generating of the dialogue based on the initial dialogue intention data and the preset process execution instruction includes: Determining candidate process nodes of the preset execution process; Extracting node data of the preset process execution instruction based on the candidate process node to obtain candidate node data of the candidate process node, wherein the candidate node data includes node processing intent and node input data, and different candidate process nodes have different node processing intents; Selecting a node for the candidate process node based on the initial conversation intention data and the node processing intention, determining a target process node, and using the node input data of the target process node as target input data, wherein the intention indicated by the initial conversation intention data is the same as the node processing intention; Performing data type detection on the target input data to obtain input data type; A dialogue generation is performed on the initial dialogue intention data, the target input data and the input data type based on a preset dialogue generation model.

3. The method according to claim 2, characterized in that The input data type includes a data upload type and a data selection type, wherein the data upload type is used to indicate uploading data of a preset data type pre-configured at the target process node, and the data selection type is used to indicate selecting data from preset task data configured at the target process node; The performing dialogue generation on the initial dialogue intention data, the target input data and the input data type based on the preset dialogue generation model includes: If the input data type is the data upload type, the node configuration parameters of the target process node are obtained, and a dialogue is generated based on the preset dialogue generation model for the initial dialogue intention data, the target input data and the node configuration parameters to obtain a first generated dialogue, wherein the first generated dialogue is used to guide the target object to upload data of the preset data type, and the node configuration parameters are conditions that must be met for data uploaded at the target process node; If the input data type is the data selection type, the preset task data of the target process node is obtained, and a dialogue is generated for the initial dialogue intention data, the target input data and the preset task data based on the preset dialogue generation model to obtain a second generated dialogue, and the second generated dialogue is used to guide the target object to perform data selection on the preset task data.

4. The method according to claim 1, characterized in that: The method further comprises: Acquire the reply data fed back by the target object, wherein the reply data is a reply file uploaded by the target object or a reply text input by the target object; Sending the reply data to the calling interface of the target application tool, and obtaining data execution information of the target application tool on the reply data from the calling interface; Determine the process execution state of the target task based on the data execution information; Based on the process execution status, the preset process execution instruction is updated to obtain the current process execution instruction; A dialogue is generated based on the current process execution instruction, the initial dialogue intention data and the reply data.

5. The method according to claim 1, characterized in that The step of selecting an application tool for the candidate application tool based on the initial conversation intention data, the target task label, and the tool attribute data to determine a target application tool includes: Performing multi-level intent recognition on the initial dialogue intention data and the target task label based on a preset multi-level attention hybrid model to obtain multi-level dialogue intention data; Performing data encoding on the multi-level dialogue intention data to obtain a multi-level dialogue intention vector; Performing data encoding on the tool attribute data to obtain a tool attribute vector; Calculating similarity between the multi-level dialogue intention vector and the tool attribute vector to obtain vector similarity of the candidate application tool; An application tool selection is performed on the candidate application tools based on the vector similarity to determine the target application tool.

6. The method according to claim 5, characterized in that The method of performing multi-level intent recognition on the initial dialogue intention data and the target task label based on a preset multi-level attention hybrid model to obtain multi-level dialogue intention data includes: Extracting data features from the initial conversation intention data to obtain initial conversation intention features, wherein the initial conversation intention features include initial conversation intention sub-features, and the initial conversation intention sub-features are provided with initial interaction items, and the initial interaction items are used to indicate items associated with a conversation interaction; Extracting label features of the target task labels to obtain target task features; Combining the sub-features of the initial conversation intention based on a preset number of tail items to obtain a combined conversation intention feature; Performing attention calculation based on the target task feature and the combined dialogue intention feature to obtain a combined attention feature; Perform a linear transformation on the combined attention feature to obtain the multi-level dialogue intention data.

7. The method according to any one of claims 1 to 6, characterized in that: The performing task detection on the initial dialogue data based on the initial dialogue intention data to determine the target task label of the target object includes: Extracting intention keywords from the initial conversation intention data to obtain conversation intention keywords; Acquire candidate task keywords associated with preset candidate task tags from a preset tag library; Calculating similarity between the conversation intention keyword and the candidate task keyword to obtain keyword similarity; The target task tag of the target object is determined from the candidate task tags based on the keyword similarity.

8. A conversation generation device based on application tools, characterized in that: The device comprises: A first acquisition module is used to acquire initial conversation data of a target object in a target application, wherein the target application is provided with candidate application tools, each of the candidate application tools has a candidate function, and the candidate functions of any two candidate application tools are different; An identification module, used to perform intent identification on the initial conversation data to obtain initial conversation intent data; A detection module, configured to perform task detection on the initial dialogue data based on the initial dialogue intention data, and determine a target task tag of the target object; wherein each target task tag is used to indicate a task function, and the task function is configured with a preset execution process; a tool selection module, configured to obtain tool attribute data of the candidate application tool, and select an application tool for the candidate application tool based on the initial dialogue intention data, the target task label and the tool attribute data, and determine a target application tool, wherein the candidate function of the target application tool is the same as the task function, the candidate function is configured with a candidate execution process, and the candidate execution process of the target application tool is the same as the preset execution process; A second acquisition module is used to acquire a preset process execution instruction of the target application tool; wherein the preset process execution instruction is used to instruct the target object to execute the preset execution process; A dialogue generation module is used to generate a dialogue based on the initial dialogue intention data and the preset process execution instruction.

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

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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