Front-end instruction interaction system based on AI drive

Abstract

The invention relates to the technical field of front-end pages, in particular to a front-end instruction interaction system based on AI drive, and the system comprises an artificial intelligence assistant which comprises a front-end instruction sending assembly; the application program comprises a front-end instruction receiving component; the artificial intelligence assistant identifies the voice input by the user to obtain a front-end control instruction corresponding to the application program; and the front-end instruction sending component forwards the front-end control instruction to the front-end instruction receiving component, and the application program executes the front-end control instruction. Aiming at the problem that an artificial intelligence assistant in the prior art needs to additionally design a function page to adapt to a relatively complex function, a corresponding front-end instruction sending component and a front-end instruction receiving component are introduced into the artificial intelligence assistant and an application program; when the artificial intelligence assistant recognizes a part of relatively complex operation associated with a certain application program, the instruction can be forwarded to the application program through the front-end instruction sending component, and a function module in the application program is scheduled to return a corresponding result, so that the development efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of front-end page technology, and in particular to an AI-driven front-end instruction interaction system. Background Art

[0002] AI assistant is an application based on artificial intelligence technology. It is usually configured in various computer devices to receive spoken content input by users, usually voice, and perform corresponding operations after recognition, thereby reducing user input time and improving operation efficiency. Natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that can enable effective communication between humans and computers using natural language.

[0003] In the prior art, there are already many such technical solutions. For example, Chinese patent CN202011147783.8 discloses a method for voice assistant calling and a voice assistant. The method includes: step 201, the voice assistant determines that the calling user needs to control the first call; step 202, the voice assistant determines whether the calling user is controlling the second call; if so, execute step 203; step 203, the voice assistant outputs a first prompt message to the calling user, receives the call content instruction given by the calling user in response to the first prompt message while controlling the second call, and the voice assistant continues the first call with the first called user according to the call content instruction. Through the above method and voice assistant, the calling user can control the calls of multiple terminals or voice assistants at the same time, thereby improving the user experience.

[0004] However, in the actual implementation process, the inventor found that this type of voice assistant usually interacts with the user based on natural language processing technology in the dialogue interface of the voice assistant after the voice assistant is awakened during operation, and dispatches the corresponding capabilities or interfaces for processing after the recognition is completed and returns the results to the operation interface. The user can perform subsequent interactions according to the return results of the operation interface. Among them, the capabilities and interfaces are generally provided in the form of back-end services, and the returned results need to follow the interface and interaction specifications of the AI ​​assistant. In the above case, there are usually two implementation methods. One is to configure the page in the AI ​​assistant. After the AI ​​assistant processes the request submitted by the page, it will call back the back-end service corresponding to the application system. This method relies on the low-code configuration capability of the AI ​​assistant's page, and generally only supports relatively simple page configurations. For complex pages, custom development is generally still required; the second is to embed the result operation interface of the AI ​​assistant into the page developed by the application system. This method generally requires page customization and development while connecting to the user system and single sign-on. It is difficult to reuse the existing functions of the application system, and the workload is relatively large. Summary of the invention

[0005] In view of the above problems existing in the prior art, a front-end command interaction system based on AI drive is now provided.

[0006] The specific technical solutions are as follows:

[0007] An AI-driven front-end command interaction system, including an AI assistant and an application;

[0008] The artificial intelligence assistant includes a front-end instruction sending component;

[0009] The application program includes a front-end instruction receiving component;

[0010] The artificial intelligence assistant recognizes and obtains the front-end control instructions corresponding to the application program from the user input voice;

[0011] The front-end instruction sending component forwards the front-end control instruction to the front-end instruction receiving component, and the application executes the front-end control instruction.

[0012] On the other hand, the artificial intelligence assistant also includes:

[0013] A voice front-end module, the voice front-end module receives the input voice input by the user; a dialogue processing module, the dialogue processing module is connected to the voice front-end module;

[0014] The dialogue processing module recognizes the input speech and generates feedback content and returns it to the speech front-end module;

[0015] An instruction matching module, the instruction matching module is connected to the dialogue processing module and the front-end instruction sending component respectively;

[0016] The instruction matching module performs matching during the process of the dialogue processing module recognizing the input voice, and sends the front-end control instruction to the front-end instruction sending component when the front-end control instruction appears.

[0017] In another aspect, the application comprises:

[0018] A plurality of functional modules, each of which is used to provide a corresponding application function;

[0019] An instruction dispatching framework, the instruction dispatching framework is connected to the functional module and the front-end instruction receiving component respectively;

[0020] The instruction scheduling framework receives and sends the front-end control instruction to the corresponding functional module.

[0021] On the other hand, the instruction scheduling framework includes:

[0022] A function instruction configuration module, wherein the function instruction configuration module is pre-configured with function instruction matching rules for the function module;

[0023] A function routing scheduling module, wherein the function routing scheduling module is connected to the function instruction configuration module;

[0024] The function routing scheduling module receives the front-end control instruction;

[0025] The function routing scheduling module forwards the front-end control instruction to the corresponding function module according to the function instruction matching rule.

[0026] On the other hand, the artificial intelligence assistant also includes:

[0027] A backend service module, the backend service module is connected to the voice frontend module;

[0028] The backend service module is also connected to the external service interface;

[0029] The voice front-end page forwards the feedback content to the back-end service module to call the external service interface.

[0030] On the other hand, the artificial intelligence assistant also includes:

[0031] A result response module, the result response module is connected to the dialogue processing module;

[0032] The result response module generates a response result according to the recognition result of the dialogue processing module;

[0033] A result operation module, the result operation module connects the result response module and the dialogue processing module;

[0034] The result operation module generates a processing result according to the response result and returns it to the dialogue processing module.

[0035] On the other hand, the result response module includes:

[0036] A recognition result classification module, wherein the recognition result classification module classifies the recognition result to generate a classification mark;

[0037] The classification mark includes rich text, option list and form;

[0038] A response result editing module is connected to the recognition result classification module, and processes the recognition result based on the classification identifier to form the response result.

[0039] On the other hand, the backend service module is also connected to the result operation module;

[0040] The backend service module returns the processing result of the external service interface to the processing module via the result operation module.

[0041] The above technical solution has the following advantages or beneficial effects:

[0042] In view of the problem that the artificial intelligence assistants in the prior art need to additionally design function pages to adapt to more complex functions when returning results, which causes inconvenience in development, in this solution, corresponding front-end command sending components and front-end command receiving components are introduced in the artificial intelligence assistants and applications. When the artificial intelligence assistant recognizes some relatively complex operations related to a certain application, the front-end command sending component can forward the instructions to the application, and schedule the functional modules in the application to return the corresponding results, thereby improving development efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The embodiments of the present invention will be described more fully with reference to the attached drawings, which are provided for illustration and description only and are not intended to limit the scope of the present invention.

[0044] Figure 1 It is an overall schematic diagram of an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of a dialogue processing module in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a scheduling framework in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of a function routing scheduling module in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of a backend service module in an embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram of a result response module in an embodiment of the present invention;

[0050] Figure 7 Schematic diagram of a response result editing module in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0053] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0054] The present invention comprises:

[0055] An AI-driven front-end command interaction system, such as Figure 1 As shown, it includes an artificial intelligence assistant 1 and an application 2;

[0056] The artificial intelligence assistant 1 includes a front-end instruction sending component 11;

[0057] The application 2 includes a front-end instruction receiving component 21;

[0058] The artificial intelligence assistant 1 recognizes the front-end control instructions corresponding to the application program from the user input voice;

[0059] The front-end instruction sending component 11 forwards the front-end control instruction to the front-end instruction receiving component 21, and the application 2 executes the front-end control instruction.

[0060] Specifically, with respect to the artificial intelligence assistants in the prior art, when returning results, additional function pages need to be designed to adapt to more complex functions, which causes inconvenience in development. In this solution, corresponding front-end instruction sending components 11 and front-end instruction receiving components 21 are introduced in the artificial intelligence assistant 1 and the application 2.

[0061] When the artificial intelligence assistant 1 recognizes some relatively complex operations related to a certain application 2, it can forward the instructions to the application 2 via the front-end instruction sending component 11, and schedule the functional modules in the application 2 to return the corresponding results, thereby improving development efficiency.

[0062] In one embodiment, Figure 2 As shown, the artificial intelligence assistant 1 also includes:

[0063] The voice front-end module 12 receives the input voice input by the user;

[0064] A dialogue processing module 13, the dialogue processing module 13 is connected to the voice front-end module 12;

[0065] The dialogue processing module 13 recognizes the input speech and generates feedback content and returns it to the speech front-end module 12;

[0066] The instruction matching module 14 is connected to the dialogue processing module 13 and the front-end instruction sending component 11 respectively;

[0067] The instruction matching module 14 performs matching during the recognition process of the input speech by the dialogue processing module 13 , and sends the front-end control instruction to the front-end instruction sending component 11 when a front-end control instruction appears.

[0068] Specifically, in order to implement the control process, in this embodiment, a command matching module 14 is set in the artificial intelligence assistant 1 to connect the dialogue processing module 13 and the front-end command sending component 11 for processing.

[0069] Specifically, when a conventional artificial intelligence assistant 1 performs speech recognition, it receives the input speech input by the user through the speech front-end module 12, then transmits it to the dialogue processing module 13 to recognize the input speech, and generates feedback content and returns it to the speech front-end module 12.

[0070] During the recognition process, the dialogue processing module 13 will recognize the input voice through a voice recognition model, etc., generate a recognition result, and then give feedback content through the processing algorithm of the artificial intelligence assistant 1 or by calling the back-end interface, and then feed the feedback content back to the voice front-end module 12 to interact with the user.

[0071] In the above process, the instruction matching module 14 matches the recognition process of the input voice by the dialogue processing module 13 to determine whether the recognized results involve pre-built control instructions. Such control instructions are usually specific operations that need to be executed through a certain type of application, such as switching and overlaying of layers in GIS processing software.

[0072] After matching this type of instruction, the instruction matching module 14 sends the front-end control instruction to the front-end instruction sending component 11 , and the front-end instruction sending component 11 forwards the front-end control instruction to the front-end instruction receiving component 21 .

[0073] Application 2 executes the front-end control instructions, thereby directly forwarding the application operations at the front-end level, reducing the steps of building pages in the artificial intelligence assistant or processing through the back-end and calling additional interfaces for invocation, thereby improving processing efficiency.

[0074] In one embodiment, Figure 3 As shown, application 2 includes:

[0075] A plurality of function modules 22, each of which is used to provide a corresponding application function;

[0076] An instruction dispatching framework 23, the instruction dispatching framework 23 is connected to the functional module 22 and the front-end instruction receiving component 21 respectively;

[0077] The instruction dispatching framework 23 receives and sends the front-end control instruction to the corresponding functional module 22 .

[0078] Specifically, in order to achieve a better control process, in this embodiment, an instruction scheduling framework 23 is set up on the basis of the original application program 2, and the functional modules 22 included in the application program 2 are analyzed.

[0079] Among them, the functional modules 22 are respectively used to provide corresponding application functions, and the instruction scheduling framework 23 collects these functions. After the front-end instruction receiving component 21 receives the forwarded front-end control instruction, it sends the front-end control instruction to the corresponding functional module 22 according to the pre-collected information to wake up the corresponding functional module 22 to perform the action expected by the user.

[0080] In one embodiment, Figure 4 As shown, the instruction scheduling framework 23 includes:

[0081] A function instruction configuration module 231, the function instruction configuration module 231 is pre-configured with a function instruction matching rule for the function module;

[0082] Function routing scheduling module 232, the function routing scheduling module 232 is connected to the function instruction configuration module 231;

[0083] The function routing scheduling module 232 receives the front-end control instruction;

[0084] The function routing scheduling module 232 forwards the front-end control instruction to the corresponding function module 22 according to the function instruction matching rule.

[0085] Specifically, in order to implement the forwarding process of related instructions, in this embodiment, a functional instruction configuration module 231 is set in the instruction scheduling framework 23.

[0086] The function instruction configuration module 231 collects the relevant interfaces and executed functions of the function module 22 in advance, and then configures the function instruction matching rules according to this information, including the front-end control instructions and routing rules corresponding to each function module 22.

[0087] When a front-end control instruction appears, the function routing scheduling module 232 receives the front-end control instruction forwarded by the front-end instruction receiving component 21, and then matches the function instruction matching rule to find the corresponding function instruction matching rule.

[0088] The front-end control instruction is forwarded to the corresponding functional module 22 according to the routing rule in the functional instruction matching rule.

[0089] In one embodiment, Figure 5 As shown, the artificial intelligence assistant 1 also includes:

[0090] A backend service module 15, the backend service module 15 is connected to the voice frontend module 12;

[0091] The backend service module 15 is also connected to the external service interface 15A;

[0092] The voice front-end module 12 forwards the feedback content to the back-end service module 15 to call the external service interface 15A.

[0093] Specifically, in order to realize the relevant functions of the artificial intelligence assistant 1, in this embodiment, a backend service module 15 is provided to connect to the external service interface 15A for some relatively complex applications.

[0094] The external service interface 15A is used to schedule remote AI models or external services to meet the relevant needs of users.

[0095] When the voice front-end module 12 forwards the feedback content or the voice content input by the user to the back-end service module 15 to call the external service interface 15A, the back-end service module 15 obtains the relevant feedback content returned by the external service interface 15A.

[0096] In one embodiment, Figure 6 As shown, the AI ​​assistant also includes:

[0097] A result response module 16, the result response module 16 is connected to the dialogue processing module;

[0098] The result response module 16 generates a response result according to the recognition result of the dialogue processing module 13;

[0099] Result operation module 17, the result operation module 17 connects the result response module and the dialogue processing module;

[0100] The result operation module 17 generates a processing result according to the response result and returns it to the dialogue processing module.

[0101] Specifically, in order to realize the processing of the recognition results of the conversation content, in this embodiment, the result response module 16 first generates a response result according to the recognition result of the conversation processing module 13, including rich text, option lists and forms, etc., and then the result operation module 17 generates a processing result based on the response result and returns it to the conversation processing module.

[0102] Among them, option lists and forms can be clicked to enter the result operation interface for result processing.

[0103] Specifically, after clicking on an option in the option list, it will usually serve as a context to enter the next round of dialogue, or a pop-up window will be displayed to display an interface that requires further interaction for the user to operate.

[0104] Clicking on the form will usually pop up a window with the form content embedded for the user to enter information. After submission, the AI ​​assistant will prompt the result or use it as context to enter the next round of conversation.

[0105] In one embodiment, the backend service module 15 is also connected to the result operation module 17;

[0106] The backend service module 15 returns the processing result of the external service interface 17A to the dialogue processing module 13 via the result operation module 17 .

[0107] Specifically, in order to realize the interactive process based on external services, such as remote AI programs and services, in this embodiment, after the back-end service module 15 obtains feedback content by calling the external service interface 17A, the back-end service module 15 returns the processing result of the external service interface 17A to the dialogue processing module 13 via the result operation module 17, thereby realizing a better interactive process.

[0108] In one embodiment, Figure 7 As shown, the result response module 16 includes:

[0109] A recognition result classification module 161, which classifies the recognition result to generate a classification mark;

[0110] Category logos include rich text, option lists, and forms;

[0111] The response result editing module 162 is connected to the recognition result classification module 161 . The response result editing module 162 processes the recognition result based on the classification identifier to form a response result.

[0112] Furthermore, in order to process different categories of interactive content, in this embodiment, the recognition result is first classified by the recognition result classification module 161 to generate a classification identifier, and it is determined whether the recognition result is an interactive option list and form. Then, the response result editing module 162 processes the recognition result based on the classification identifier to form a response result output.

[0113] In one embodiment, the above system can be applied to a lightweight base map application. A lightweight base map is a GIS application that supports comprehensive geographic spatial display of different scenes by overlaying base map layers, overlay layers, and scene layers.

[0114] The core function is that users can first select a base map, and then select one or more overlay layers and a scene layer as needed to perform a comprehensive overlay display of the scene.

[0115] It also supports separate previews of basemaps, overlay layers, and scene layers. Due to the large number of overlay layers and scene layers, it is necessary to support users to quickly combine and switch scenes through natural language dialogue such as voice or text.

[0116] The lightweight base map AI intelligent application is structurally composed of the lightweight base map H5 front-end application and the lightweight base map AI assistant.

[0117] The lightweight base map H5 application consists of components such as layer overlay rendering and layer preview function modules, command scheduling framework, AI assistant SDK components and front-end command receiving components.

[0118] The lightweight basemap AI assistant consists of H5 front-end application and back-end service.

[0119] Before starting the process, you must first configure the command scheduling framework, including the command and function routing configuration corresponding to the composite layer settings and layer preview.

[0120] At the same time, the artificial intelligence model on the AI ​​assistant side needs to learn layer-related knowledge and model training to realize the recognition of intentions such as natural language-based layer query and layer switching.

[0121] The actual processing flow includes:

[0122] S1. Access the system: Users access the lightweight base map H5 application through a browser, enter the main interface of the application function and initialize the AI ​​assistant SDK. Normal function operations can still be performed without using the AI ​​assistant;

[0123] S2. Invoke AI Assistant: The lightweight base map H5 application can invoke AI Assistant by calling AI Assistant SDK by default after the main function interface is loaded. It can also be invoked by clicking the AI ​​Assistant icon. When invoked for the first time, AI Assistant will be initialized.

[0124] S3. AI assistant interaction: After invoking the AI ​​assistant, the user will enter the IM (instant messaging) mode dialogue interface. Users can conduct natural language dialogues with the AI ​​assistant through voice or text as needed, including the following forms of interaction:

[0125] S3.1. The user asks the AI ​​assistant what layers or types of layers are available through natural language. The AI ​​assistant calls the backend service to perform intent recognition and capability scheduling, and returns the layer list required by the user for the user to choose.

[0126] S3.2. The user asks the AI ​​assistant to switch layers or overlay scenes through natural language. The AI ​​assistant backend service performs intent recognition and capability scheduling, and generates a result configuration list of base map layers, overlay layers, and scene layer overlays for the user to choose.

[0127] S4. Front-end instruction scheduling: Front-end instruction scheduling will only be triggered when the result processing returned by the AI ​​assistant is configured as a front-end instruction. The triggering method is as follows according to the number of candidate lists:

[0128] S4.1. The AI ​​assistant returns multiple candidate lists, which need to be manually selected before triggering;

[0129] S4.2. When the AI ​​assistant returns only one candidate list, it can be set to be automatically triggered;

[0130] S5, command sending; after receiving the front-end command sending request, the front-end command sending component sends the command to the front-end command receiving component. In this scenario, the Javascript Message API technology of the H5 front-end is used as the communication protocol for command sending;

[0131] S6. Calling instructions: After receiving the front-end instruction message through the Javascript Message API, the front-end instruction receiving component first decodes it, and then packages it into JSON format parameters to call the instruction dispatching interface of the front-end instruction dispatching framework component;

[0132] S7, function scheduling: After receiving the instruction scheduling request, the front-end instruction scheduling framework component first searches for a matching function route in the function instruction configuration according to the call parameters, and then performs function route scheduling. The possible situations of function scheduling are as follows:

[0133] S4.1. When the function route is not matched, the user is prompted that "the function for instruction scheduling does not exist";

[0134] S4.2. When matching the layer preview command configuration, call the function to layer preview and specify the corresponding layer parameters. The H5 application displays the layer preview interface according to the specified layer and parameters;

[0135] S4.3. When the composite layer setting instruction configuration is matched, the function is called to the layer setting and the corresponding layer parameters are specified. The H5 application renders the layers in sequence according to the specified layers and parameters.

[0136] Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0137] Those skilled in the art will appreciate that various aspects of the present invention, or possible implementations of various aspects, may be specifically implemented as systems, methods, or computer program products. Therefore, various aspects of the present invention, or possible implementations of various aspects, may take the form of complete hardware embodiments, complete software embodiments (including firmware, resident software, etc.), or embodiments of combined software and hardware aspects, all collectively referred to herein as "circuits," "modules," or "systems." In addition, various aspects of the present invention, or possible implementations of various aspects, may take the form of computer program products, which refer to computer instructions stored in a memory.

[0138] The memory may be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing, such as random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable read-only memory (CD-ROM).

[0139] The processor in the computer reads the computer instructions stored in the memory, so that the processor can execute the functional actions specified in each step or the combination of steps in the flowchart; and generate a device for implementing the functional actions specified in each block or the combination of blocks in the block diagram.

[0140] It should be understood that the processor in the computer can be understood as one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components implemented to execute the aforementioned computer instructions.

[0141] Computer instructions can be executed completely on the user's local computer, partially on the user's local computer, as a separate software package, partially on the user's local computer and partially on a remote computer, or completely on a remote computer or server. It should also be noted that in some alternative embodiments, the functions noted in each step in the flow chart or each block in the block diagram may not occur in the order noted in the figure. For example, depending on the functions involved, two steps or two blocks shown in succession may actually be executed roughly simultaneously, or these blocks may sometimes be executed in reverse order.

[0142] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A front-end instruction interaction system based on AI drive, characterized in that: including AI assistants and apps; The artificial intelligence assistant includes a front-end instruction sending component; The application program includes a front-end instruction receiving component; The artificial intelligence assistant recognizes and obtains the front-end control instructions corresponding to the application program from the user input voice; The front-end instruction sending component forwards the front-end control instruction to the front-end instruction receiving component, and the application executes the front-end control instruction.

2. The front-end instruction interaction system according to claim 1, characterized in that: The artificial intelligence assistant also includes: A voice front-end module, the voice front-end module receives the input voice input by the user; A dialogue processing module, the dialogue processing module is connected to the voice front-end module; The dialogue processing module recognizes the input speech and generates feedback content and returns it to the speech front-end module; An instruction matching module, the instruction matching module is connected to the dialogue processing module and the front-end instruction sending component respectively; The instruction matching module performs matching during the process of the dialogue processing module recognizing the input voice, and sends the front-end control instruction to the front-end instruction sending component when the front-end control instruction appears.

3. The front-end instruction interaction system according to claim 1, characterized in that: The applications include: A plurality of functional modules, each of which is used to provide a corresponding application function; An instruction dispatching framework, the instruction dispatching framework is connected to the functional module and the front-end instruction receiving component respectively; The instruction scheduling framework receives and sends the front-end control instruction to the corresponding functional module.

4. The front-end instruction interaction system according to claim 3, characterized in that: The instruction scheduling framework includes: A function instruction configuration module, wherein the function instruction configuration module is pre-configured with function instruction matching rules for the function module; A function routing scheduling module, wherein the function routing scheduling module is connected to the function instruction configuration module; The function routing scheduling module receives the front-end control instruction; The function routing scheduling module forwards the front-end control instruction to the corresponding function module according to the function instruction matching rule.

5. The front-end instruction interaction system according to claim 2, characterized in that: The artificial intelligence assistant also includes: A backend service module, the backend service module is connected to the voice frontend module; The backend service module is also connected to the external service interface; The voice front-end page forwards the feedback content to the back-end service module to call the external service interface.

6. The front-end instruction interaction system according to claim 5, characterized in that: The artificial intelligence assistant also includes: A result response module, the result response module is connected to the dialogue processing module; The result response module generates a response result according to the recognition result of the dialogue processing module; A result operation module, the result operation module connects the result response module and the dialogue processing module; The result operation module generates a processing result according to the response result and returns it to the dialogue processing module.

7. The front-end instruction interaction system according to claim 6, characterized in that: The result response module includes: A recognition result classification module, wherein the recognition result classification module classifies the recognition result to generate a classification mark; The classification mark includes rich text, option list and form; A response result editing module is connected to the recognition result classification module, and processes the recognition result based on the classification identifier to form the response result.

8. The front-end instruction interaction system according to claim 6, characterized in that: The backend service module is also connected to the result operation module; The backend service module returns the processing result of the external service interface to the dialogue processing module via the result operation module.

Images