Method and device for smart home after-sales service, and smart after-sales system

Abstract

The invention relates to the technical field of after-sales systems, and discloses a method for an intelligent home after-sales service, and the method comprises the steps: converting the received voice input of a user into a text; generalizing the text to serve as the input of the intellectual family brain module, and obtaining corpora output by the intellectual family brain module; wherein the corpus comprises question corpus; obtaining an answer matched with a corpus according to the question corpus output by the smart home brain module; and displaying videos and texts corresponding to the answers to the user according to the answers. According to the method, an automatic process from voice input to intelligent diagnosis to solution display is realized, the after-sales service efficiency and convenience are greatly improved, manual intervention and user waiting time are reduced, and the satisfaction degree and experience of the user are improved. The invention further discloses a device for the smart home after-sales service and a smart after-sales system.

Description

Technical Field

[0001] This application relates to the field of after-sales system technology, such as a method and apparatus for after-sales service of smart homes, and an intelligent after-sales system. Background Technology

[0002] Currently, with the rapid development of artificial intelligence, the Internet of Things, and big data technologies, smart home appliances are becoming increasingly popular. People have higher and higher demands for the intelligence of home appliances, not only pursuing a convenient user experience but also hoping to obtain efficient and timely after-sales support. However, the current after-sales service model for home appliances such as televisions is relatively traditional, mainly relying on manual telephone consultation or on-site repair. This results in a cumbersome and time-consuming process for resolving after-sales issues. Users need to describe the problem themselves and wait for after-sales personnel to come and diagnose it, which not only increases after-sales costs but also affects the user's service experience.

[0003] To efficiently resolve after-sales issues, a remote consultation system for smart home repair has been disclosed, comprising: a device terminal, which internally includes a user information module, an after-sales service module, a video module, a voice module, a split-screen module, a camera module, an points module, a service item module, a scanning and modeling module, and an evaluation module. Professional repair personnel provide remote service by scanning and modeling the damaged structure using the scanning and modeling module, and then conducting a professional assessment using the evaluation module to provide objective suggestions.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] Although the relevant technologies can provide remote services through the device terminal, they still require guidance from professional maintenance personnel. The scanning and modeling process also makes the after-sales process more complicated and reduces the user experience.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a method and apparatus for after-sales service in smart homes, as well as an intelligent after-sales system, making it more efficient for users to resolve after-sales issues.

[0009] In some embodiments, the method for after-sales service of smart homes includes: converting received user voice input into text; generalizing the text and using it as input to a smart home brain module, and obtaining corpus output by the smart home brain module; wherein the corpus includes question corpus; obtaining answers matching the corpus based on the question corpus output by the smart home brain module; and displaying the corresponding video and text to the user based on the answers.

[0010] Optionally, the text is generalized and used as input to the Smart Home Brain module, and the output corpus of the Smart Home Brain module is obtained, including: generalizing the text through a large AI vertical model; outputting corpus corresponding to the generalized text; wherein the corpus also includes a catch-all corpus.

[0011] Optionally, the output corpus corresponding to the generalized text includes: comparing the similarity between the generalized text and the question corpus in the Smart Home Brain module to determine whether the generalized text is after-sales corpus; if the generalized text is determined to be after-sales corpus, outputting the question corpus corresponding to the generalized text in the Smart Home Brain module; if the generalized text is determined to be non-after-sales corpus, outputting the catch-all corpus in the Smart Home Brain module.

[0012] Optionally, the method for after-sales service of smart homes further includes: displaying the fallback corpus to the user when the smart home brain module outputs the fallback corpus.

[0013] Optionally, based on the answer, the user is shown a video and text corresponding to the answer, including: performing data parsing on the answer to obtain the parsed video and the text corresponding to the video; and showing the user the video and the text corresponding to the video.

[0014] Optionally, after converting the received user's voice input into text, the method further includes: displaying the converted text to the user.

[0015] In some embodiments, the device for smart home after-sales service includes: a voice module configured to convert received user voice input into text; a smart home brain module configured to generalize the text and use it as input to the smart home brain module, and obtain corpus output by the smart home brain module; wherein the corpus includes question corpus; and based on the question corpus output by the smart home brain module, obtain answers matching the corpus; and a user interaction module configured to display videos and text corresponding to the answers to the user.

[0016] Optionally, the device for smart home after-sales service further includes: an online service intelligent agent module configured to generalize text through an AI vertical category large model.

[0017] In some embodiments, the apparatus for smart home after-sales service includes a processor and a memory storing program instructions, the processor being configured to perform the method for smart home after-sales service as described above when the program instructions are executed.

[0018] In some embodiments, the intelligent after-sales system includes: an intelligent after-sales system body; and an apparatus for smart home after-sales service as described above, installed on the intelligent after-sales system body.

[0019] The method, apparatus, and intelligent after-sales system for smart home after-sales service provided in this disclosure can achieve the following technical effects:

[0020] In this embodiment, the text is generalized to cover a wider range of question types and expressions, thereby improving the accuracy and adaptability of question matching. The generalized text serves as input to the Smart Home Brain module, which analyzes and processes the input corpus and outputs question-related corpus. Based on the question corpus output by the Smart Home Brain module, the system can quickly obtain matching answers and, based on the answer content, display corresponding videos and text to the user, intuitively guiding the user to solve the problem. This automates the process from voice input to intelligent diagnosis and solution display, significantly improving the efficiency and convenience of after-sales service, reducing manual intervention and user waiting time, and enhancing user satisfaction and experience.

[0021] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0022] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0023] Figure 1 This is a schematic diagram illustrating the implementation environment of the method for after-sales service of smart homes provided in this embodiment of the disclosure;

[0024] Figure 2 This is a schematic diagram of a method for after-sales service of a smart home provided in an embodiment of this disclosure;

[0025] Figure 3 This is a schematic diagram of another method for after-sales service of smart homes provided in an embodiment of this disclosure;

[0026] Figure 4 This is a schematic diagram of another method for after-sales service of smart homes provided in an embodiment of this disclosure;

[0027] Figure 5 This is a schematic diagram of an apparatus for after-sales service of a smart home provided in an embodiment of this disclosure;

[0028] Figure 6 This is a schematic diagram of another device for after-sales service of smart homes provided in an embodiment of this disclosure. Detailed Implementation

[0029] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0030] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0031] Unless otherwise stated, the term "multiple" means two or more.

[0032] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0033] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0034] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0035] Currently, with the rapid development of artificial intelligence, the Internet of Things, and big data technologies, smart home appliances are becoming increasingly popular. People have higher and higher demands for the intelligence of home appliances, not only pursuing a convenient user experience but also hoping to obtain efficient and timely after-sales support. However, the current after-sales service model for home appliances such as televisions is relatively traditional, mainly relying on manual telephone consultation or on-site repair. Existing televisions lack software that can troubleshoot and report problems, making the after-sales process cumbersome and time-consuming. Users need to describe the problem themselves and wait for after-sales personnel to come and diagnose it, which not only increases after-sales costs but also affects the user's service experience. Under the wave of intelligentization, after-sales problems in home appliances are becoming increasingly prominent, becoming a pain point that urgently needs to be addressed in the industry. Developing efficient after-sales solutions is therefore particularly important.

[0036] Figure 1 This is a schematic diagram illustrating the implementation environment of the method for after-sales service of smart homes provided in this embodiment of the disclosure. Figure 1 As shown, the implementation environment may include user 100 and smart TV 200.

[0037] The smart TV 200 is equipped with a smart after-sales system 201. User 100 can interact with the smart TV 200 and ask questions about after-sales issues to the smart after-sales system 201. The smart after-sales system 201 can output the most appropriate response based on the user 100's question, including relevant video and text solutions.

[0038] Combination Figure 2 As shown in the embodiments of this disclosure, a method for after-sales service of smart homes is provided, including:

[0039] S201, the processor converts the received user's voice input into text.

[0040] S202, the processor generalizes the text and uses it as input to the Smart Home Brain module, and obtains the corpus output by the Smart Home Brain module; the corpus includes question corpus.

[0041] S203, the processor obtains the answer that matches the question corpus output by the Smart Home Brain module.

[0042] S204, the processor displays the corresponding video and text to the user based on the answer.

[0043] The method for after-sales service of smart homes provided in this disclosure first converts received user voice input into text, utilizing advanced speech recognition technology to ensure accurate conversion of voice information into processable text. Then, the text is generalized to cover a wider range of question types and expressions, thereby improving the accuracy and adaptability of question matching. The generalized text serves as input to the Smart Home Brain module, which uses its powerful knowledge base and intelligent algorithms to analyze and process the input corpus and output question-related corpus. Based on the question corpus output by the Smart Home Brain module, the system can quickly obtain matching answers, which may include detailed solutions, operation guides, etc. Finally, based on the answer content, the system displays corresponding videos and text to the user, intuitively guiding them to solve the problem.

[0044] This automates the entire process from voice input to intelligent diagnosis and solution demonstration, significantly improving the efficiency and convenience of after-sales service, reducing manual intervention and user waiting time, and enhancing user satisfaction and experience. Simultaneously, the integrated presentation of voice and visual information better meets users' after-sales needs in a smart home environment, promoting the improvement and development of the smart home ecosystem.

[0045] Optionally, the text is generalized and used as input to the Smart Home Brain module, and the output corpus of the Smart Home Brain module is obtained, including: generalizing the text through a large AI vertical model; outputting corpus corresponding to the generalized text; wherein the corpus also includes a catch-all corpus.

[0046] In this embodiment of the disclosure, the user's voice input is converted into text, and then the text is generalized using an AI vertical-category large model. The generalization process includes semantic expansion and semantic transformation of the text, enabling the text to cover a wider range of question types and expressions. For example, "washing machine drainage failure" is generalized into different expressions such as "washing machine drainage failure," "washing machine poor drainage," and "washing machine slow drainage."

[0047] The generalized text is input into the Smart Home Brain module, which analyzes and processes it, outputting corresponding corpus data. Through generalization, the system can more accurately understand the user's intent, improving the accuracy and adaptability of question matching. With the help of the Smart Home Brain module, the system can quickly obtain answers related to the question, displaying corresponding videos and text to the user, intuitively guiding them to solve the problem. This not only improves the efficiency and convenience of after-sales service but also reduces manual intervention and user waiting time, enhancing user satisfaction and experience.

[0048] Optionally, the output corpus corresponding to the generalized text includes: comparing the similarity between the generalized text and the question corpus in the Smart Home Brain module to determine whether the generalized text is after-sales corpus; if the generalized text is determined to be after-sales corpus, outputting the question corpus corresponding to the generalized text in the Smart Home Brain module; if the generalized text is determined to be non-after-sales corpus, outputting the catch-all corpus in the Smart Home Brain module.

[0049] In this embodiment of the disclosure, after the user's voice command is converted into text and generalized by an AI vertical category model, the system compares the similarity of the generalized text with the question corpus in the Smart Home Brain module. The Smart Home Brain module stores a massive amount of carefully labeled and categorized home appliance question corpus, covering various possible fault scenarios and user consultation types.

[0050] Similarity comparison algorithms, such as cosine similarity or edit distance, calculate the semantic similarity between the generalized text and each question corpus. If the similarity reaches a preset threshold, the generalized text is determined to be after-sales corpus. At this point, the system extracts the most matching question corpus from the Smart Home Brain module and outputs it. These question corpora often contain a specific description of the problem, possible causes, and relevant solution information, laying the foundation for providing more accurate guidance to users in the future. For example, the problem corresponding to "washing machine drainage failure" might be "drain pipe blockage" or "drain motor failure," along with solution corpus, such as "how to clean the drain pipe" or "how to replace the drain motor."

[0051] Conversely, if the similarity does not reach the threshold, it indicates that the generalized text may not describe after-sales issues covered by the system, or that the user's expression is too specific to match the existing problem corpus. The system will then output a fallback corpus from the Smart Home Brain module. This fallback corpus is designed to handle various complex or atypical user input scenarios. When a user's question exceeds the system's knowledge scope, or when the user's expression is ambiguous, the fallback corpus can guide the user to further clarify the problem, or provide some general suggestions and solutions. For example, "Please describe more details of the fault" or "It is recommended to contact a professional repair person."

[0052] The embodiments disclosed herein achieve comprehensive coverage of user input, ensuring that the system can still provide reasonable responses in the face of various complex situations, thereby improving the efficiency and reliability of after-sales service, reducing user waiting time, and also enhancing user satisfaction and experience.

[0053] Optionally, the method for after-sales service of smart homes further includes: displaying the fallback corpus to the user when the smart home brain module outputs the fallback corpus.

[0054] In this embodiment, the user interaction display module presents the information to the user in text and voice formats, guiding the user to further clarify the problem, provide more information, or try some basic self-troubleshooting steps. The catch-all corpus effectively covers user input scenarios outside the system's knowledge base, ensuring that the user always receives a response from the system during the consultation process. Secondly, by guiding the user to provide more details about the fault, the system can collect more contextual information about the user's problem, which helps to subsequently optimize the problem corpus of the Smart Home Brain module and improve the system's ability to identify and process similar problems.

[0055] Optionally, based on the answer, the user is shown a video and text corresponding to the answer, including: performing data parsing on the answer to obtain the parsed video and the text corresponding to the video; and showing the user the video and the text corresponding to the video.

[0056] Combination Figure 3 As shown in the embodiments of this disclosure, another method for after-sales service of smart homes is provided, including:

[0057] S301, the processor converts the received user's voice input into text.

[0058] S302, the processor generalizes the text and uses it as input to the Smart Home Brain module, and obtains the corpus output by the Smart Home Brain module; the corpus includes question corpus.

[0059] S303: The processor obtains the answer that matches the question corpus output by the Smart Home Brain module.

[0060] S304, the processor parses the answer data to obtain the parsed video and the corresponding text.

[0061] S305, the processor displays a video and corresponding text to the user.

[0062] In this embodiment, after the Smart Home Brain module outputs the corresponding corpus, the answer is parsed to accurately extract the corresponding video and text content. This involves data format conversion and adaptation to ensure that the video and text can be effectively recognized and presented by the user's device. For example, the system will associate and bind the video link or storage path with the text description, and at the same time perform semantic optimization on the text to present it to the user in a more understandable way.

[0063] This disclosure leverages the intuitiveness of video and the conciseness of text to complement each other, catering to the learning and acceptance habits of different users and enabling them to understand solutions more efficiently. Secondly, it improves service accuracy by ensuring that the information received by the user is highly relevant to the problem through precise parsing and matching, avoiding information redundancy or insufficiency and optimizing the user experience. Furthermore, it achieves service continuity and interactivity. If users have further questions while watching videos or reading text, they can consult again based on existing information. The system then invokes the relevant processes again based on the feedback, forming a closed-loop service until the problem is completely resolved.

[0064] Optionally, after converting the received user's voice input into text, the method further includes: displaying the converted text to the user.

[0065] Combination Figure 4 As shown in the embodiments of this disclosure, a method for after-sales service of smart homes is provided, including:

[0066] S401, the processor converts the received user's voice input into text.

[0067] S402, the processor displays the converted text to the user.

[0068] S403, the processor generalizes the text and uses it as input to the Smart Home Brain module, and obtains the corpus output by the Smart Home Brain module; the corpus includes question corpus.

[0069] S404: The processor obtains the answer that matches the question corpus output by the Smart Home Brain module.

[0070] S405: Based on the answer, the processor displays the corresponding video and text to the user.

[0071] In this embodiment of the disclosure, displaying the converted text to the user not only allows the user to clearly see that their question has been accurately received by the system, but also provides a visual basis for subsequent corpus processing and problem diagnosis.

[0072] By displaying the text, an immediate feedback mechanism is provided to users, ensuring they can confirm whether the system has correctly understood their voice input. This effectively avoids subsequent processing deviations caused by speech recognition errors, improving the accuracy of the entire after-sales service process. Furthermore, this display method also provides assurance for subsequent corpus processing. If users discover errors in the converted text, they can be corrected promptly at this stage, ensuring that subsequent corpus generalization, matching, and other steps are based on correct input, thereby improving the efficiency and accuracy of problem diagnosis. Finally, this process also provides the system with a foundation for recording and analyzing user behavior data. By analyzing user feedback and correction behavior on the displayed text, the system can continuously optimize the speech recognition model and corpus processing algorithm, improve its ability to understand user intent, and further optimize the quality of smart home after-sales service and user experience.

[0073] Combination Figure 5 As shown, this disclosure provides an apparatus 500 for smart home after-sales service, including a voice module 501, a smart home brain module 502, and a user interaction module 503. The voice module 501 is configured to convert received user voice input into text; the smart home brain module 502 is configured to generalize the text and use it as input to the smart home brain module, and obtain the corpus output by the smart home brain module; wherein, the corpus includes question corpus; based on the question corpus output by the smart home brain module, answers matching the corpus are obtained; the user interaction module 503 is configured to display a video and text corresponding to the answer to the user.

[0074] The device 500 for smart home after-sales service provided in this disclosure converts received user voice input into text. Advanced speech recognition technology ensures that the voice information is accurately converted into a processable text format. The text is then generalized to cover a wider range of question types and expressions, thereby improving the accuracy and adaptability of question matching. The generalized text serves as input to the Smart Home Brain module, which uses its powerful knowledge base and intelligent algorithms to analyze and process the input corpus and output question-related corpus. Based on the question corpus output by the Smart Home Brain module, the system can quickly obtain matching answers, which may include detailed solutions, operation guides, etc. Finally, based on the answer content, the system displays corresponding videos and text to the user, intuitively guiding them to solve the problem.

[0075] Combination Figure 5 As shown, optionally, the device 500 for smart home after-sales service also includes an online service agent module 504. The online service agent module 504 is configured to generalize text using a large AI vertical model.

[0076] In this embodiment of the disclosure, the user's voice input is converted into text, and then the web service intelligent agent module 504 generalizes the text using an AI vertical category large model. The generalization process includes semantic expansion and semantic transformation of the text, enabling the text to cover a wider range of question types and expressions, thereby improving the accuracy and adaptability of question matching.

[0077] Combination Figure 6 As shown, this disclosure provides an apparatus 60 for smart home after-sales service, including a processor 600 and a memory 601. Optionally, the apparatus 60 may further include a communication interface 602 and a bus 603. The processor 600, communication interface 602, and memory 601 can communicate with each other via the bus 603. The communication interface 602 can be used for information transmission. The processor 600 can call logical instructions in the memory 601 to execute the method for smart home after-sales service described in the above embodiments.

[0078] Furthermore, the logic instructions in the aforementioned memory 601 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0079] The memory 601, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 600 executes functional applications and data processing by running the program instructions / modules stored in the memory 601, thereby implementing the method for smart home after-sales service described in the above embodiments.

[0080] The memory 601 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 601 may include high-speed random access memory and may also include non-volatile memory.

[0081] This disclosure provides an intelligent after-sales service system, including: an intelligent after-sales service system body, and the aforementioned device for smart home after-sales service. The device for smart home after-sales service is installed in the intelligent after-sales service system body. The installation relationship described herein is not limited to placement inside the intelligent after-sales service system body, but also includes installation and connection with other components of the intelligent after-sales service system, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the device for smart home after-sales service can be adapted to feasible intelligent after-sales service system bodies, thereby realizing other feasible embodiments.

[0082] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0083] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0084] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0086] 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 embodiments of this disclosure. 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. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for after-sales service of smart homes, characterized in that, include: Convert the received user voice input into text; The text is generalized and used as input to the Smart Home Brain module, and the output corpus of the Smart Home Brain module is obtained; the corpus includes question corpus. Based on the question corpus output by the Smart Home Brain module, obtain the answers that match the corpus; Based on the answer, the system displays the corresponding video and text to the user.

2. The method according to claim 1, characterized in that, The generalized text is used as input to the Smart Home Brain module, and the output corpus of the Smart Home Brain module is obtained, including: Generalize text using AI-powered vertical category models; Output the corpus corresponding to the generalized text; the corpus also includes a catch-all corpus.

3. The method according to claim 2, characterized in that, Output the corpus corresponding to the generalized text, including: The generalized text is compared with the question corpus in the Smart Home Brain module to determine whether the generalized text is after-sales corpus. If the generalized text is determined to be after-sales corpus, output the corresponding question corpus in the Smart Home Brain module; If the generalized text is determined to be non-after-sales corpus, the fallback corpus from the Smart Home Brain module is output.

4. The method according to claim 3, characterized in that, Also includes: When the Smart Home Brain module outputs a fallback corpus, the fallback corpus is displayed to the user.

5. The method according to any one of claims 1 to 4, characterized in that, Based on the answer, the system displays a video and text corresponding to the answer to the user, including: The answer is analyzed to obtain the analyzed video and the corresponding text. Display videos and corresponding text to users.

6. The method according to any one of claims 1 to 4, characterized in that, After converting the received user voice input into text, it also includes: Show the user the converted text.

7. A device for after-sales service of smart homes, characterized in that, include: The voice module is configured to convert received user voice input into text; The Smart Home Brain module is configured to generalize text and use it as input to obtain the corpus output by the Smart Home Brain module; the corpus includes question corpus; and based on the question corpus output by the Smart Home Brain module, the answer matching the corpus is obtained. The user interaction module is configured to display videos and text corresponding to the answers given.

8. The apparatus according to claim 7, characterized in that, Also includes: The online service intelligent agent module is configured to generalize text using a large AI vertical model.

9. An apparatus for after-sales service of smart homes, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to perform the method for after-sales service of a smart home as described in any one of claims 1 to 6 when executing the program instructions.

10. An intelligent after-sales system, characterized in that, include: The core of the intelligent after-sales system; The device for smart home after-sales service as described in any one of claims 7 to 9 is installed on the main body of the smart after-sales system.

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