Interface interaction method and device, equipment and storage medium

Abstract

The invention discloses an interface interaction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the interface interaction method comprises the steps: receiving a business problem related to a target business scene, and determining a target prompt word matched with the business problem from a prompt word set; determining a user business intention according to the target prompt word and the business problem, and determining target business interface data matched with the user business intention from an enterprise business system; and performing service processing on the target service interface data, and feeding back a service processing result to a user corresponding to the service problem. According to the method and the device, the intelligent service application can directly access the service resource data in the enterprise service system, the intelligent service application can actively identify the context of the user problem and quickly match the cue word, the interaction initiative is improved, seamless direct interaction with the service interface data is realized, the manual input link is eliminated, and the user experience is improved. And closed-loop operation of data processing can be completed, and real-time feedback of a business processing result is ensured.

Description

Technical Field

[0001] This disclosure generally relates to the field of artificial intelligence technology, and more particularly to a method, apparatus, device, and storage medium for interface interaction. Background Technology

[0002] Currently, to improve business processing efficiency, an Artificial Intelligence (AI) interface can be pre-developed, possessing independent processing capabilities. In subsequent practical applications, users input the business interface data to be processed paragraph by paragraph into the AI ​​interface via copy and paste. The AI ​​interface then processes the data and provides the results back to the user. However, this AI interface passively responds to user input throughout the process, lacking the ability to directly interact with the business interface data. Furthermore, it can only operate independently without relying on other external systems, leading to data silos between systems and the drawbacks of redundant development. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide an interface interaction method, device, equipment and storage medium that enables intelligent business applications to directly access business resource data in enterprise business systems. The intelligent business applications can proactively identify the context of user questions and quickly match prompt words, thereby improving the initiative of interaction, realizing seamless direct interaction with business interface data, eliminating the manual input step, and completing the closed-loop operation of data processing to ensure real-time feedback of business processing results.

[0004] Firstly, this application provides a user interface interaction method applied to an intelligent business application, wherein the intelligent business application is integrated with an enterprise business system and includes a set of prompt words associated with a target business scenario. The method includes: Receive business questions related to the target business scenario, and determine target prompt words that match the business question from the prompt word set; The user's business intent is determined based on the target prompt words and the business question, and target business interface data matching the user's business intent is determined from the enterprise business system. The target business interface data is processed, and the processing results are fed back to the user corresponding to the business problem.

[0005] In conjunction with the first aspect, in one possible implementation, the intelligent business application further includes a first mapping relationship between different business intentions and business processing methods, and the business processing of the target business interface data includes: The target business processing method that matches the user's business intent is found from the first mapping relationship, and the target business interface data is processed according to the target business processing method.

[0006] In conjunction with the first aspect, in one possible implementation, determining the user's business intent based on the target prompt and the business question includes: A complete prompt word is generated based on the business parameter variables corresponding to the target prompt word and the business question. The complete prompt word is then input into the intent reasoning model to perform intent reasoning on the complete prompt word, thereby obtaining the user's business intent output by the intent reasoning model.

[0007] In conjunction with the first aspect, in one possible implementation, determining the target business interface data matching the user's business intent from the enterprise business system includes: When the enterprise business system includes different types of business interface data, the business interface data corresponding to the user's business intent is retrieved from the enterprise business system. The found business interface data is preprocessed to obtain the target business interface data.

[0008] In conjunction with the first aspect, in one possible implementation, the intelligent business application further includes a second mapping relationship between different types of business instructions and interface data preprocessing methods. The preprocessing of the found business interface data to obtain the target business interface data includes: Perform semantic parsing on the business problem to determine the corresponding business instruction category; Find the interface data preprocessing method corresponding to the business instruction category from the second mapping relationship, and preprocess the found business interface data according to the found interface data preprocessing method to obtain the target business interface data.

[0009] In conjunction with the first aspect, in one possible implementation, the intelligent business application is a runtime interface obtained by rendering a visual configuration interface based on the interface development code corresponding to the intelligent business application; the visual configuration interface is the initial interface engine of the intelligent business application.

[0010] In conjunction with the first aspect, in one possible implementation, the interface development code corresponding to the intelligent business application is executable code generated based on the configuration resource parameters of each AI component dragged to the visual configuration interface, the enterprise business system, and the set of prompt words associated with the target business scenario, assuming the visual configuration interface includes an AI component library.

[0011] Secondly, this application also provides a user interface interaction device for use in intelligent business applications, wherein the intelligent business applications are integrated with enterprise business systems and include a set of prompt words associated with target business scenarios. The device includes: The problem acquisition unit is used to receive business problems related to the target business scenario and determine target prompt words that match the business problem from the prompt word set; The interface determination unit is used to determine the user's business intent based on the target prompt words and the business question, and to determine the target business interface data that matches the user's business intent from the enterprise business system. The interface interaction unit is used to process the target business interface data and provide feedback on the processing results to the user corresponding to the business problem.

[0012] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the interface interaction method described in the first aspect.

[0013] Fourthly, this application also provides a computer-readable storage medium. This computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the interface interaction method described in the first aspect.

[0014] This application provides a user interface interaction method, apparatus, device, and storage medium. The user interface interaction method integrates an intelligent business application with an enterprise business system and includes a set of prompt words associated with the target business scenario. This allows the intelligent business application to directly access business resource data within the enterprise business system, avoiding the data silo problem caused by reliance on user copy-and-paste in traditional methods. Simultaneously, the prompt word set is pre-set for specific business scenarios, reducing redundant development needs. By receiving business questions related to the target business scenario and determining target prompt words matching the business question from the prompt word set, the intelligent business application can proactively identify the user's question context and quickly match prompt words, improving the initiative of interaction. By determining the user's business intent based on the target prompt words and business question, and determining the target business interface data matching the user's business intent from the enterprise business system, seamless direct interaction with the business interface data is achieved, eliminating the manual input step. By processing the target business interface data and providing feedback on the processing results to the user corresponding to the business question, a closed-loop operation of data processing is completed, ensuring real-time feedback and avoiding inefficiencies caused by system fragmentation. The entire process constructs a proactive, integrated, and efficient interaction flow, fundamentally overcoming the shortcomings of traditional AI interfaces. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is one of the flowcharts illustrating the interface interaction method in one embodiment; Figure 2 This is a second flowchart illustrating the interface interaction method in one embodiment; Figure 3 This is the third flowchart illustrating the interface interaction method in one embodiment; Figure 4 This is the fourth flowchart illustrating the interface interaction method in one embodiment; Figure 5 This is a schematic diagram of the interface during the design phase of one embodiment; Figure 6 This is one of the schematic diagrams of the runtime interface in one embodiment; Figure 7 This is a second schematic diagram of the runtime interface in one embodiment; Figure 8 This is a structural block diagram of the interface interaction device in one embodiment; Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments. Furthermore, the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The terms "first" and "second," etc., in the specification and claims of the embodiments of this application are used to distinguish different objects, not to describe a specific order of objects.

[0018] Currently, to improve business processing efficiency, an AI interface can be pre-developed, possessing independent processing capabilities. In subsequent practical applications, users input the business interface data to be processed paragraph by paragraph into the AI ​​interface via copy and paste. The AI ​​interface then processes this data and provides the results back to the user. However, this AI interface passively responds to user input throughout the entire process, lacking the ability to directly interact with the business interface data. Furthermore, it can only operate independently without relying on other external systems, leading to data silos between systems and the drawbacks of redundant development.

[0019] Furthermore, existing methods for developing AI interfaces are difficult and inefficient. Specifically, developing AI interfaces typically requires professionals to train, fine-tune, and deploy models. This not only results in long development cycles (ranging from months to years) and an inability to quickly respond to business needs, but also makes it difficult for companies to iterate their AI interfaces rapidly, thus missing market opportunities.

[0020] To address the aforementioned technical issues, this application provides an interface interaction method, apparatus, device, and storage medium that enables intelligent business applications to directly access business resource data within an enterprise business system. The intelligent business applications can proactively identify the context of user questions and quickly match prompts, enhancing interactive initiative and achieving seamless direct interaction with business interface data. This eliminates the need for manual input and completes closed-loop data processing, ensuring real-time feedback of business processing results.

[0021] The following is combined Figures 1 to 9 This application describes an interface interaction method, apparatus, device, and storage medium. The execution subject of the interface interaction method can be a customer service robot within an intelligent business application, and this customer service robot can be directly embedded in the intelligent business application. The intelligent business application can be integrated with an enterprise business system and includes a set of prompt words associated with the target business scenario. Furthermore, the intelligent business application can be located in a computer device, which can be a personal computer (PC), portable device, laptop computer, smartphone, tablet computer, portable wearable device, or other electronic devices. This application does not specifically limit the specific application of the computer device. Further, the interface interaction method can also be applied to an interface interaction device within an intelligent business application located in a computer device. This interface interaction device can be implemented through software, hardware, or a combination of both. The following description uses a customer service robot within an intelligent business application as an example to illustrate the interface interaction method.

[0022] To facilitate understanding of the interface interaction method provided in the embodiments of this application, the interface interaction method provided in this application will be described in detail below through several exemplary embodiments. It is understood that the following exemplary embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0023] Reference Figure 1 This is a flowchart illustrating the interface interaction method provided in the embodiments of this application, as shown below. Figure 1 As shown, the interface interaction method includes the following steps 101 to 103.

[0024] Step 101: Receive business questions related to the target business scenario and determine the target prompt words that match the business questions from the prompt word set.

[0025] In practical applications, intelligent business applications can be understood as a software-based functional module that, through deep integration with enterprise business systems, can directly access business data resources within those systems, thereby avoiding the data silo problem caused by relying on manual copying and pasting in traditional methods.

[0026] Furthermore, a cue word set can be understood as a predefined set of keywords and / or phrases associated with a specific target business scenario. This set can be implemented through manual configuration, automated generation, or a combination of both. For example, in a financial business scenario, the cue word set might include keywords such as loan approval and account inquiry; in a medical business scenario, it might include keywords such as medical record inquiry and drug inventory management. In other words, the combination of cue words primarily aims to provide a scenario-based matching foundation to quickly locate the relevant context of the user's question.

[0027] Specifically, there can be multiple intelligent business applications, each applicable to a specific business scenario. Each intelligent business application corresponds to an application URL and can be displayed to the user in a graphical interface. Thus, when a user determines that they need to ask a question about a certain business scenario (i.e., the target business scenario), they can enter the application URL corresponding to that target business scenario into their browser. Once the browser completes the interface loading, it will open the interface of the corresponding target intelligent business application, laying the foundation for subsequent operations.

[0028] For intelligent business scenarios where the interface has already been loaded, the process of receiving business questions related to the target business scenario can be achieved through user input text, speech recognition, or image parsing. For example, users can input a problem description via keyboard, ask business questions in natural language via voice assistant, or ask business questions by parsing the dialogue content entered by the user in the intelligent business application's dialog box. Furthermore, the process of determining the target prompt word matching the business question from the prompt word set can be achieved using string matching, semantic similarity calculation, or rule engines. For example, based on preset matching rules, the user-input business question is compared one by one with predefined keywords and / or phrases and other forms of prompt words in the prompt word set, and at least one prompt word with the highest similarity is selected as the target prompt word.

[0029] Step 102: Determine the user's business intent based on the target prompts and business questions, and determine the target business interface data that matches the user's business intent from the enterprise business system.

[0030] Specifically, the process by which a customer service robot in an intelligent business application determines a user's business intent based on target prompts and business questions can be achieved through logical reasoning, rule-based judgment, or external knowledge base queries. For example, based on the combination of target prompts and business questions, a predefined business rule base can be invoked to deduce the user's potential intent, which is then identified as the user's business intent. Furthermore, the process of determining the target business interface data matching the user's business intent from the enterprise business system can be achieved through data retrieval, API calls, or database queries. For example, the target business interface data related to the user's intent can be obtained by calling the enterprise business system's Application Programming Interface (API).

[0031] Step 103: Perform business processing on the target business interface data and provide feedback on the business processing results to the user corresponding to the business issue.

[0032] Specifically, customer service robots in intelligent business applications possess proactive analytical capabilities. They can autonomously analyze target business interface data that matches the user's business intent. For example, when the target business interface data is document business data, they can detect document anomalies and / or predict business trends and other business processing operations to obtain the business processing result with the highest degree of matching to the user's input business question, and then feed that result back to the user.

[0033] For example, when a user enters a business question into the intelligent business application interface as "the distribution of backlogged bugs", the customer service robot in the intelligent business application infers the target business intent and determines the account query form data related to the target business intent. At this time, by performing backlogged bug distribution detection on the account query form data, the distribution result of backlogged bugs is obtained.

[0034] As can be understood from steps 101 to 103 above, the working principle of this embodiment is as follows: the intelligent business application is integrated with the enterprise business system and includes a set of prompt words associated with the target business scenario. This set of prompt words consists of pre-set relevant prompt words for a specific business scenario, thus avoiding the data silo problem caused by manual user input in traditional methods. When a business question related to the target business scenario is received, the system matches the target prompt word corresponding to the business question from the prompt word set, achieving rapid identification and proactive response to the user's question context, changing the traditional AI interface's passive waiting-for-input mode. Furthermore, the system infers the user's business intent by combining the target prompt word and the business question, and directly determines the target business interface data matching the user's business intent from the enterprise business system, thereby achieving seamless interaction with the business interface data and eliminating the manual copy-paste step. Subsequently, the target business interface data is processed, and the processing results are fed back to the user who raised the business question, completing the closed-loop operation of data processing. In this way, by providing a foundation for contextualized matching through a set of prompt words, accurately locating business interface data in enterprise business systems based on the matching results through intent inference, and ensuring the efficiency of automated execution through processing and feedback, a proactive, integrated, and efficient interactive process is constructed. This fundamentally solves the problems of passive response, lack of direct interaction capabilities, data silos between systems, and redundant development in traditional AI interfaces.

[0035] The interface interaction method provided in this application deeply integrates intelligent business applications with enterprise business systems and utilizes a set of prompt words to automatically parse user questions and identify intents. It directly obtains relevant interface data from the enterprise business system for end-to-end processing, thereby changing the traditional AI interface's passive response to user input. Specifically, the integration of the intelligent business application with the enterprise business system and the inclusion of a set of prompt words associated with the target business scenario allows the intelligent business application to directly access business resource data within the enterprise business system, avoiding the data silo problem caused by reliance on user copy-pasting in traditional methods. Simultaneously, the prompt word set is pre-set for specific business scenarios, reducing the need for redundant development. The feature of receiving business questions related to the target business scenario and determining target prompt words matching the business question from the prompt word set enables the intelligent business application to proactively identify the context of the user question and quickly match prompt words, improving the initiative of the interaction. The feature of determining user business intent based on target prompts and business questions, and identifying target business interface data matching the user's business intent from the enterprise business system, automatically infers user intent by combining prompts and user questions, and directly extracts corresponding business interface data from the enterprise business system. This achieves seamless and direct interaction with business interface data, eliminating manual input. The feature of processing the target business interface data and providing feedback on the processing results to the user corresponding to the business question completes the closed-loop operation of data processing, ensuring real-time feedback and avoiding inefficiencies caused by system fragmentation. These features work together to construct a proactive, integrated, and efficient interaction process, fundamentally overcoming the shortcomings of traditional AI interfaces.

[0036] Based on the above Figure 1 In one example embodiment of the method shown, to enable subsequent interaction of interface data by the intelligent business application, corresponding business processing method calls can be pre-generated for all business interface data covered by a specific business scenario and registered in the intelligent business application, so as to select the appropriate business processing method when inferring the user's business intent. Based on this, when the intelligent business application also includes a first mapping relationship between different business intents and business processing methods, the specific determination process for performing business processing on the target business interface data in step 103 can be implemented through the following steps in this embodiment.

[0037] Find the target business processing method that matches the user's business intent from the first mapping relationship, and perform business processing on the target business interface data according to the target business processing method.

[0038] The first mapping relationship refers to a mechanism that establishes a predefined association between different types of business interface data and their corresponding business processing methods. This can be implemented through configuration files, database tables, or rule engines. The purpose of introducing the first mapping relationship is to avoid hard-coding processing logic for each type of business interface data, thereby improving the system's reusability and maintainability. The target business processing method can be understood as a standardized operating procedure designed for a specific business interface data type, which can execute actual business processing tasks through dynamic invocation.

[0039] Specifically, by constructing a first mapping relationship, a dynamic association between business intent types and processing methods is achieved, effectively solving the problem of repetitive development caused by the lack of a standardized matching mechanism. In implementation, based on the characteristic information of the user's business intent, the first mapping relationship quickly locates the target business processing method that matches it. Subsequently, the intelligent business application automatically calls this target business processing method to process the target business interface data. This process not only ensures accurate adaptation of processing logic but also significantly reduces the need for manual intervention and minimizes the risks caused by manual configuration errors. Furthermore, when a new business intent type is added, only the first mapping relationship needs to be extended to complete system adaptation without modifying the core code, thus significantly reducing development and maintenance costs. The above mechanism, combined with the overall architecture of the intelligent business application, further promotes seamless integration between enterprise business systems and intelligent business applications, fundamentally alleviating the data silo problem and improving business processing efficiency.

[0040] Based on the above Figure 1 In one example embodiment of the method shown, to improve the accuracy of intent recognition, an intent recognition model can be generated by pre-training a large model, so that the user's business intent can be quickly and accurately inferred in subsequent applications. Based on this, the user's business intent is determined in step 102 according to the target prompt words and business questions. The specific determination process in this embodiment can be implemented through the following steps.

[0041] A complete prompt word is generated based on the business parameter variables and business question corresponding to the target prompt word. The complete prompt word is then input into the intent reasoning model to perform intent reasoning and obtain the user's business intent output by the intent reasoning model.

[0042] The intent reasoning model can be understood as a deep learning-based computational framework. It can be implemented using a Transformer architecture, recurrent neural networks, or a knowledge graph-based reasoning engine. It can also be implemented by training a large model using sample datasets, where each sample contains a corresponding complete prompt word sample and the sample's business intent. The purpose of introducing this intent reasoning model is to improve the accuracy of semantic parsing through structured context processing.

[0043] A complete prompt can be understood as a comprehensive information carrier that integrates business parameter variables and user business issues. It can be generated through template filling, dynamic splicing, or feature vector fusion, aiming to enhance the information completeness and scenario relevance of the prompt.

[0044] It's important to note that when intelligent business applications are presented to users in the form of a user interface, they can be understood as runtime interfaces, generated through configuration of the design-time interface or interface engine. Furthermore, as a runtime interface, the customer service robot within the intelligent business application converts the prompts configured during interface design into actual data that the runtime interface can recognize and process. This conversion is based on a pre-configured list of business parameter variables that can be recognized and processed by the intelligent business application. Each set of business parameter variables in this list has a substitutable relationship with the text format of a specific prompt in the corresponding prompt set. In this way, the intelligent business application parses the text format of the target prompt and matches it with the list of business parameter variables to obtain the business parameter variables that have a substitutable relationship with the parsed text format. Then, by combining this with the user's business question, the complete prompt is obtained.

[0045] At this point, the customer service robot in the intelligent business application can directly input the complete prompt word into the intent reasoning model. This intent reasoning model performs intent reasoning based on the input complete prompt word and obtains the user's business intent output by the intent reasoning model.

[0046] The interface interaction method provided in this application, by inputting the business parameter variables combined with the target prompt word and the complete prompt word of the business question into the intent reasoning model, fully utilizes the business scenario orientation of the predefined prompt words and the dynamic characteristics of real-time questions to provide the model with a structured context, avoiding semantic gaps caused by single question input, significantly enhancing the scenario adaptability of the prompt words, and thus eliminating intent ambiguity caused by missing parameters; in addition, by using the intent recognition model to perform intent reasoning on the complete prompt word, complex business semantic patterns can be efficiently parsed, improving the robustness of the reasoning process; the final output of the user's business intent provides a highly reliable basis for subsequent business interface data matching, ensuring that the model's response content is highly consistent with the user's actual needs, thereby further optimizing the parsing efficiency of user query needs, effectively solving the problems of semantic ambiguity and noise interference, and achieving accurate intent recognition and efficient business processing capabilities.

[0047] Based on the above Figure 1In one example embodiment of the method shown, considering that each business interface data stored in the enterprise business system corresponds to a complete interface, and that not all data in a complete interface is necessarily related to the user's actual business needs, the initial business interface data can be found and preprocessed first to obtain interface business data related to the user's actual business needs. Based on this, in step 102, the target business interface data matching the user's business intent is determined from the enterprise business system. The specific determination process in this embodiment can be achieved through... Figure 2 Steps 201 and 202 shown are implemented.

[0048] Step 201: If the enterprise business system includes different types of business interface data, search for the business interface data corresponding to the user's business intent from the enterprise business system.

[0049] Step 202: Preprocess the found business interface data to obtain the target business interface data.

[0050] Specifically, an enterprise business system can be understood as a core data processing platform integrated into intelligent business applications. It can contain various heterogeneous data sources, such as relational databases, non-relational databases, or file storage systems. Different types of business interface data can be understood as collections of business interface data with different formats, structures, or protocols, such as JSON-formatted business interface data, XML-formatted business interface data, or tabular business interface data. By limiting operations to the enterprise business system, which includes multiple types of business interface data, it is possible to effectively focus on data adaptation issues in complex scenarios and avoid redundant processing in a single data environment.

[0051] Among them, preprocessing the found business interface data can be understood as cleaning and other preprocessing of the found initial business interface data to obtain target business interface data that is adapted to the subsequent business processing flow, thereby providing a reliable foundation for subsequent processing. The original data is converted due to differences in format or inconsistency in structure.

[0052] Preprocessing methods can include data cleaning, format conversion, field mapping, or encoding standardization, among other operations. The purpose is to eliminate redundant data that is irrelevant to the user's actual intent, thereby supporting the seamless integration of intelligent business applications with enterprise business systems.

[0053] In practical applications, customer service robots in intelligent business applications locate relevant business interface data within the enterprise's business system by searching for data corresponding to the user's business intent. This can be understood as the process of using semantic analysis or keyword matching technology to locate relevant business interface data based on the user's input business question and its matching target prompts. This process can be achieved by building an index structure, using a full-text search engine, or a rule-based filtering mechanism. The goal is to improve the accuracy and efficiency of data retrieval, ensuring that the retrieved business interface data is highly relevant to the user's actual intent.

[0054] Specifically, when multiple types of business interface data exist in an enterprise's business system, direct searching may not accurately match the user's intent, and the retrieved business interface data may not be directly usable for subsequent processing due to differences in format or type. Therefore, the first step is to locate business interface data related to the user's business intent from the enterprise's business system through semantic analysis or rule matching, ensuring the relevance and accuracy of the data. Subsequently, preprocessing operations are performed on the retrieved business interface data, such as converting unstructured data into structured data, unifying multiple formats into a standard format, and / or performing data cleaning operations, thereby generating the target business interface data. This process not only significantly improves the efficiency of data retrieval but also reduces the need for manual intervention through automated preprocessing, effectively alleviating the problem of data silos between systems.

[0055] The interface interaction method provided in this application, by combining data lookup and preprocessing mechanisms, solves the technical challenge of heterogeneous data adaptation, ensuring the accuracy and efficiency of interface interaction. Especially in complex business environments, it can specifically address data incompatibility issues while avoiding redundant development of adaptation logic, thereby significantly improving the overall efficiency of business processing.

[0056] Based on the above Figure 2 In one example embodiment of the method shown, considering that the interface data related to the user's true intent differs depending on the user's actual intent, the preprocessing operations performed on the relevant initial business interface data also differ accordingly. Thus, the corresponding preprocessing operation can be determined based on the business instruction category of the user's business question. Based on this, in cases where the intelligent business application also includes a second mapping relationship between different types of business instructions and interface data preprocessing methods, step 202 preprocesses the found business interface data to obtain the target business interface data. The specific determination process in this embodiment can be achieved through… Figure 3 Steps 301 and 302 shown are implemented.

[0057] Step 301: Perform semantic parsing on the business problem to determine the corresponding business instruction category.

[0058] Step 302: Find the interface data preprocessing method corresponding to the business instruction category from the second mapping relationship, and preprocess the found business interface data according to the found interface data preprocessing method to obtain the target business interface data.

[0059] The second mapping relationship can be understood as a pre-established set of rules used to associate different types of business instructions with corresponding interface data preprocessing methods. It can be implemented in the form of key-value pair storage structure, database table or knowledge graph, etc., with the aim of providing adapted preprocessing strategies for different business instructions.

[0060] Business instruction categories can be understood as operation type identifiers extracted after semantic parsing of business questions. They can be implemented through classification models, rule matching, or template parsing in natural language processing technology, aiming to transform user-input business questions into recognizable structured instructions.

[0061] Business instructions can be categorized into data query / analysis instructions and action execution instructions. For example, "distribution of backlogged bugs" is a regular dialogue command, while "help me commit to my bugs" is an action instruction.

[0062] Specifically, a dynamically adaptable preprocessing mechanism is constructed by introducing a second mapping relationship. First, semantic parsing of the business question transforms the user's natural language business question input into a clearly defined business instruction category. This process ensures that the intelligent business application can accurately understand the user's intent. Then, based on the parsed business instruction category, a matching interface data preprocessing method is searched from the second mapping relationship. This step achieves a high degree of consistency between the preprocessing logic and the user's instructions. Finally, the business interface data is preprocessed according to the found preprocessing method, ensuring that the preprocessing results closely align with the specific business scenario requirements. This design not only overcomes the shortcomings of fixed preprocessing patterns in responding to diverse instructions but also provides high-quality data input for subsequent business processing, thereby significantly improving the accuracy and smoothness of the overall interaction.

[0063] The interface interaction method provided in this application solves the problem of the lack of dynamic adaptation capability for different business instruction categories in the preprocessing method by combining semantic parsing and mapping relationships. This ensures that the preprocessing process can accurately match the user's intent and avoids business processing deviations or inefficiencies caused by insufficient adaptation. This mechanism provides an extensible rule base for the conversion of business interface data, effectively improving the system's flexibility and adaptability.

[0064] In one example embodiment, considering that the intelligent business application is a runtime interface, the initialization of the intelligent business application can be completed by rendering the pre-configured and generated interface development code, so as to receive user input of business questions. Based on this, the interface interaction method provided in this application embodiment may further include an application initialization phase, which in this embodiment can be configured through the following steps.

[0065] Intelligent business applications are runtime interfaces obtained by rendering the visual configuration interface based on the interface development code corresponding to the intelligent business application; the visual configuration interface is the initial interface engine of the intelligent business application.

[0066] Interface development code can be understood as executable code that describes the interface structure and component logic of intelligent business applications. It can be implemented through methods such as template generation, script writing, or dynamic assembly.

[0067] A visual configuration interface can be understood as a basic environment that supports interface customization through graphical operations, or a WYSIWYG editable canvas. Specifically, it can be implemented using a web-based drag-and-drop editor, allowing developers to intuitively manipulate interface elements.

[0068] Specifically, the visual configuration interface, serving as the initial interface engine for intelligent business applications, also embeds a customer service robot. This robot can initiate the acquisition process of the intelligent business application by receiving interface execution commands, specifically by first acquiring the interface development code corresponding to the intelligent business application, such as from a low-code platform. The low-code platform is used to store the interface development code for each different intelligent business application. Subsequently, the visual configuration interface transforms the interface development code into a specific application interface, i.e., into the runtime interface of the corresponding intelligent business application. This process not only simplifies the development process but also allows business personnel to intuitively participate in interface customization. The resulting intelligent business application can be directly integrated with enterprise business systems, avoiding data silos and supporting rapid iteration and scenario-based adjustments. This fundamentally overcomes the limitations of traditional static development models, effectively improving the flexibility of business scenario adaptation and system integration efficiency.

[0069] In one example embodiment, the interface development code corresponding to the intelligent business application is executable code generated based on the configuration resource parameters of each AI component dragged to the visual configuration interface, the enterprise business system, and the set of prompt words associated with the target business scenario, when the visual configuration interface includes an AI component library.

[0070] The visual configuration interface can be understood as a graphical development environment that allows for the design of complex functions through intuitive user interaction. In practical applications, the visual configuration interface can be implemented using a web-based graphical editor or a desktop integrated development environment (IDE), with the aim of lowering the development threshold and improving development efficiency.

[0071] AI component libraries can be understood as a set of predefined artificial intelligence functional modules. These modules are usually provided in a reusable form and may include natural language processing modules, image recognition modules, etc., with the aim of supporting diverse intelligent needs.

[0072] Configuration resource parameters can be understood as a set of variables or settings used to define the behavior and attributes of the corresponding AI component. They can be stored in key-value pairs, such as defining the input and output format of the AI ​​component, runtime dependencies, etc., with the aim of ensuring that the AI ​​component can run as expected.

[0073] Specifically, by introducing an AI component library and a drag-and-drop mechanism, intelligent generation of UI development code is achieved. That is, developers can select the required AI components in the visual configuration interface and add them to the design area through simple drag-and-drop operations. During this process, the visual configuration interface automatically captures the configuration resource parameters of each dragged AI component. These configuration resource parameters serve as key input sources, providing a reliable basis for the subsequent generation of UI development code. Furthermore, the visual configuration interface can dynamically integrate all captured configuration resource parameters, enterprise business systems, and a set of prompts associated with the target business scenario, thereby generating UI development code that can adapt to business needs. The unique aspect of this process is that it not only avoids the complexity of traditional manual coding but also tightly connects the enterprise business system with intelligent prompting capabilities by integrating the enterprise business system data source and the business scenario-based prompt set into the UI code generation process, effectively solving the problems of low development efficiency and insufficient system integration.

[0074] The interface development code generation process provided in this application embodiment firstly, through a drag-and-drop operation mechanism, allows developers to design complex functions without requiring extensive programming backgrounds, significantly lowering the development threshold. Secondly, the ability to dynamically integrate enterprise business systems and contextualized prompts enables the generated code to directly adapt to specific business needs without additional adaptation work, thereby eliminating data silos and avoiding redundant development. Finally, by closely integrating with the construction process of intelligent business applications, the automated generation of interface development code improves development efficiency and ensures a high degree of system integration.

[0075] For example, refer to Figure 4 The flowchart of the interface interaction method shown is as follows: Figure 4 As shown, the entire process includes an initialization phase and an interaction phase. For the initialization phase, the first step is opening the interface: the user enters the application URL in the browser, and the browser loads the interface, laying the foundation for subsequent operations. Next is the initialization of XiaoTe AI (i.e., the customer service robot): during design time, the interface (i.e., the interface engine) initializes XiaoTe AI according to the system configuration. This means that after the interface loads, the corresponding interface development code is called for rendering, ensuring it is ready to handle the business questions entered by the user. Finally, there is the interface interaction API registration: to enable subsequent interaction between XiaoTe AI and the interface's business data, the interface generates corresponding method calls during design time and registers them with XiaoTe AI, so that the appropriate method can be selected and the corresponding business instructions executed during intent recognition.

[0076] Continue, for Figure 4 In the interactive phase shown, the first step is command input: the user inputs dialogue content in the Xiaote AI dialog box within the runtime interface of the intelligent business application. This can be categorized into two types of commands: data query and analysis, and action execution. For example, "distribution of backlogged bugs" is a common dialogue command, while "help me identify my bugs" is an action instruction. The second step is collecting interface data: before Xiaote AI interacts with the intent reasoning model, the intelligent business application can perform preliminary semantic analysis on the user's input business question to determine the corresponding business instruction category. For data analysis instructions, the corresponding business interface data is cleaned to provide a reliable foundation for subsequent processing. Next is constructing prompt words: the text format of the prompt words configured during interface design is replaced with business parameter variables from the runtime interface, combined with the user's question, to generate complete prompt words for model interaction. Finally, intent recognition: the intent reasoning model infers based on the input complete prompt words and returns the corresponding inferred user business intent to Xiaote AI. At this point, XiaoTe AI outputs the user's business intent through dialogue, actions, and execution, completing natural language interaction with the user. It then searches the enterprise business system for business interface data corresponding to the user's business intent. Based on the interface data preprocessing method corresponding to the business instruction category of the business problem, it preprocesses the found business interface data to obtain the target business interface data. Finally, it performs business processing on the target business interface data according to the target business processing method matching the user's business intent, completing the execution of the business instruction. For interface diagrams of the design-time interface and runtime interface, please refer to... Figures 5 to 7 The specific processes involved can be referred to in the aforementioned embodiments, and will not be repeated here.

[0077] The interface interaction method provided in this application not only lowers the development threshold through drag-and-drop interface and pre-built AI components, enabling non-technical personnel to quickly build a customer service robot as an intelligent AI assistant, but also allows the intelligent AI assistant to be directly embedded in the business interface, analyze page data (such as documents and forms) in real time, provide analysis, suggestions and help documents, and achieve deep business integration. In addition, the intelligent AI assistant has proactive analysis capabilities, such as detecting document anomalies and predicting business trends, and interacts with interface functions through natural language actions, achieving proactive and scenario-based support.

[0078] It should be noted that although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart can be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0079] In one embodiment, this application also provides a user interface interaction device applied to an intelligent business application, wherein the intelligent business application is integrated with an enterprise business system and includes a set of prompt words associated with a target business scenario; such as Figure 8 As shown, the interface interaction device includes: a problem acquisition unit 301, an interface determination unit 302, and an interface interaction unit 303.

[0080] Problem acquisition unit 301 is used to receive business problems related to the target business scenario and determine the target prompt words that match the business problem from the prompt word set.

[0081] The interface determination unit 302 is used to determine the user's business intent based on the target prompt words and business questions, and to determine the target business interface data that matches the user's business intent from the enterprise business system.

[0082] The interface interaction unit 303 is used to process the target business interface data and provide feedback on the business processing results to the user corresponding to the business problem.

[0083] Optionally, in one embodiment, the interface interaction unit 303 is specifically used to find the target business processing method that matches the user's business intent from the first mapping relationship between different business intents and business processing methods, and to perform business processing on the target business interface data according to the target business processing method.

[0084] Optionally, in one embodiment, the interface determination unit 302 is specifically used to generate a complete prompt word based on the business parameter variables and business questions corresponding to the target prompt word, input the complete prompt word into the intent reasoning model, perform intent reasoning on the complete prompt word, and obtain the user business intent output by the intent reasoning model.

[0085] Optionally, in one embodiment, the interface determination unit 302 is specifically used to search for business interface data corresponding to the user's business intent from the enterprise business system when the enterprise business system includes different types of business interface data; and to preprocess the searched business interface data to obtain target business interface data.

[0086] Optionally, in one embodiment, the interface determination unit 302 is specifically used to perform semantic parsing on the business problem, determine the business instruction category corresponding to the business problem; search for the interface data preprocessing method corresponding to the business instruction category from the second mapping relationship between different types of business instructions and interface data preprocessing methods, and preprocess the found business interface data according to the found interface data preprocessing method to obtain the target business interface data.

[0087] Optionally, in one embodiment, the intelligent business application in the interface interaction detection device is a runtime interface obtained by rendering the visual configuration interface based on the interface development code corresponding to the intelligent business application; the visual configuration interface is the initial interface engine of the intelligent business application.

[0088] Optionally, in one embodiment, the interface development code corresponding to the intelligent business application in the interface interaction detection device is executable code generated based on the configuration resource parameters of each AI component dragged to the visual configuration interface, the enterprise business system, and the set of prompt words associated with the target business scenario, when the AI ​​component library is included in the visual configuration interface.

[0089] It should be understood that the units described in the interface interaction testing device and the reference Figure 1 The steps in the described method correspond to each other. Therefore, the operations and features described above for the method also apply to the user interface device and the units contained therein, and will not be repeated here. The user interface device can be pre-implemented in the browser or other security applications of a computer device, or it can be loaded into the browser or its security applications of a computer device through download or other means. The corresponding units in the user interface device can cooperate with the units in the computer device to implement the solutions of the embodiments of this application.

[0090] The following is for reference. Figure 9 It shows a schematic diagram of the structure of a computer system 400 suitable for implementing computer devices or servers in the embodiments of this application.

[0091] like Figure 9 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the system 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0092] The following components are connected to I / O interface 405: an input section 706 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0093] Specifically, according to embodiments of this application, the above references Figure 1 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing instructions for performing... Figure 1 The program code for the method. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable media 411.

[0094] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0097] On the other hand, this application also provides a computer-readable storage medium, which may be included in the computer device described in the above embodiments, or may exist independently and not assembled into the computer device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the methods described in this application. For example, it may execute... Figure 1 The steps of the method shown are as follows.

[0098] This application provides a computer program product including instructions that, when executed, cause the method described in this application to be performed. For example, it can execute... Figure 1 The steps of the method shown are as follows.

[0099] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0100] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A user interface interaction method, characterized in that, The method is applied to intelligent business applications, which are integrated with enterprise business systems and include a set of prompt words associated with target business scenarios; the method includes: Receive business questions related to the target business scenario, and determine target prompt words that match the business question from the prompt word set; The user's business intent is determined based on the target prompt words and the business question, and target business interface data matching the user's business intent is determined from the enterprise business system. The target business interface data is processed, and the processing results are fed back to the user corresponding to the business problem.

2. The method according to claim 1, characterized in that, The intelligent business application also includes a first mapping relationship between different business intentions and business processing methods, and the business processing of the target business interface data includes: The target business processing method that matches the user's business intent is found from the first mapping relationship, and the target business interface data is processed according to the target business processing method.

3. The method according to claim 1, characterized in that, Determining the user's business intent based on the target prompt and the business question includes: A complete prompt word is generated based on the business parameter variables corresponding to the target prompt word and the business question. The complete prompt word is then input into the intent reasoning model to perform intent reasoning on the complete prompt word, thereby obtaining the user's business intent output by the intent reasoning model.

4. The method according to claim 1, characterized in that, The step of determining the target business interface data that matches the user's business intent from the enterprise business system includes: When the enterprise business system includes different types of business interface data, the business interface data corresponding to the user's business intent is retrieved from the enterprise business system. The found business interface data is preprocessed to obtain the target business interface data.

5. The method according to claim 4, characterized in that, The intelligent business application also includes a second mapping relationship between different types of business instructions and interface data preprocessing methods. The preprocessing of the found business interface data to obtain the target business interface data includes: Perform semantic parsing on the business problem to determine the corresponding business instruction category; Find the interface data preprocessing method corresponding to the business instruction category from the second mapping relationship, and preprocess the found business interface data according to the found interface data preprocessing method to obtain the target business interface data.

6. The method according to any one of claims 1 to 5, characterized in that, The intelligent business application is a runtime interface obtained by rendering the visual configuration interface based on the interface development code corresponding to the intelligent business application; the visual configuration interface is the initial interface engine of the intelligent business application.

7. The method according to claim 6, characterized in that, The interface development code corresponding to the intelligent business application is executable code generated based on the configuration resource parameters of each AI component dragged to the visual configuration interface, the enterprise business system, and the set of prompt words associated with the target business scenario, when the AI ​​component library is included in the visual configuration interface.

8. A user interface interaction device, characterized in that, The device is applied to intelligent business applications, which are integrated with enterprise business systems and include a set of prompt words associated with target business scenarios; the device includes: The problem acquisition unit is used to receive business problems related to the target business scenario and determine target prompt words that match the business problem from the prompt word set; The interface determination unit is used to determine the user's business intent based on the target prompt words and the business question, and to determine the target business interface data that matches the user's business intent from the enterprise business system. The interface interaction unit is used to process the target business interface data and provide feedback on the processing results to the user corresponding to the business problem.

9. A computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the interface interaction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the interface interaction method according to any one of claims 1 to 7.

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