Toy interaction method combining RFID with large language model

Abstract

The embodiment of the invention relates to a toy interaction method combining RFID (Radio Frequency Identification Device) with a large language model, which comprises a main toy unit and a plurality of auxiliary toy units, a controller is arranged in the main toy unit and is connected with an RFID reader-writer, a loudspeaker, a microphone and an RFID tag, and each auxiliary toy unit is provided with the RFID tag; when the main toy unit is started, a user can connect and set the mode of the main toy unit through a mobile phone, the microphone collects voice information and sends a signal to the large language model, and when the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends a signal to the large language model; after analyzing the signal, the model generates model output and returns the model output to the main toy unit, and the main toy unit completes interactive response immediately; and the main toy unit dynamically adjusts the interaction mode and the output content according to the user setting mode, the voice input, the connection state of each toy unit and the RFID tag information. According to the interaction method provided by the embodiment of the invention, personalized interaction experience can be realized, and the education and entertainment values of toys are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a toy interaction method combining RFID with a large language model. Background Art

[0002] Currently, users have an increasing demand for the interactivity and educational value of toys. Companies in the toy industry on the market also provide many different types of toys to meet the different needs of users.

[0003] Among them, toys with strong interactivity usually rely on preset programs and simple sensors, which limits the adaptability and educational potential of toys. For example, some toys may respond to user operations through simple touch or sound sensors, but these responses are usually fixed and cannot be adjusted according to different user behaviors or environmental changes. In addition, these toys usually lack the ability to communicate deeply with users and cannot provide a richer and more personalized interactive experience, thus failing to fully realize their potential in promoting children's cognitive development and social skills. Summary of the invention

[0004] The embodiment of the present invention provides a toy interaction method combining RFID with a large language model. The toy interaction method provided by the embodiment of the present invention can improve the problem that current toys lack deep communication capabilities and cannot be personalized and interactive, and further improve the educational and entertainment value of the toys.

[0005] In a first aspect, an embodiment of the present invention provides a toy interaction method combining RFID with a large language model, comprising:

[0006] The main toy unit and multiple auxiliary toy units, the main toy unit is equipped with a controller, connected to the RFID reader, speaker, microphone and RFID tag, each auxiliary toy unit is equipped with an RFID tag; when the main toy unit is started, the user can connect and set the main toy unit mode through the mobile phone, the microphone collects voice information and sends a signal to the large language model, when the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends a signal to the large language model; after the model parses the signal, it generates a model output and returns it to the main toy unit, and the main toy unit immediately completes the interactive response; the main toy unit dynamically adjusts the interactive mode and output content according to the user setting mode, voice input, connection status of each toy unit and RFID tag information.

[0007] In a second aspect, an embodiment of the present invention provides a real-time personalized interaction method for a large language model toy, comprising:

[0008] The controller of the main toy unit has the ability to generate interactive responses in real time, and the interactive responses are model outputs generated based on the large language model; the connection between the main toy unit and the cloud server is wireless, and the wireless connection uses the Wi-Fi communication protocol;

[0009] The main toy unit has learning capabilities, can record the interaction history and user preferences with each auxiliary toy unit, and optimize the interaction mode and output content in real time; the main toy unit can record the user's interaction data, and with the user's consent, use the data for further training and optimization of the model;

[0010] The main toy unit has multi-language processing capabilities and can provide corresponding language interactions based on the user's geographical location and language preference; the text information collected by the microphone also includes voiceprint recognition, which can identify the voice characteristics of a specific user to provide a personalized interactive response; with the method, the user can wirelessly connect to the main toy unit through a mobile phone and set the interaction mode of the main toy unit to achieve a personalized interactive experience.

[0011] In a third aspect, an embodiment of the present invention provides an RFID technology and connection management, including:

[0012] The RFID reader / writer has the ability to read multiple RFID tags and can distinguish the RFID tag information of different auxiliary toy units; the connection status identification mechanism between the main toy unit and the auxiliary toy unit includes proximity, contact and separation, and each connection status corresponds to a different interaction mode; the controller of the main toy unit has the function of judging the response logic according to the connection sequence, and can determine the connection sequence according to the RFID tag information and connection status of the auxiliary toy unit, and return the corresponding model response accordingly.

[0013] In a fourth aspect, an embodiment of the present invention provides an interactive method of a toy's voice and a multiplayer dialogue, including:

[0014] The speaker and microphone of the main toy unit support voice recognition and voice synthesis functions, so that interactive responses can be made through voice; the main toy unit can conduct multiple rounds of dialogue interaction based on the voice recognition results and RFID tag information combined with the model output generated by the large language model.

[0015] The toy interaction method of RFID combined with a large language model provided in this embodiment: the method includes a main toy unit and multiple auxiliary toy units, wherein the main toy unit is provided with a controller, connected to an RFID reader, a speaker, a microphone and an RFID tag, and each auxiliary toy unit is equipped with an RFID tag; when the main toy unit is started, the user can connect and set the main toy unit mode through a mobile phone, the microphone collects voice information and sends a signal to the large language model, and when the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends a signal to the large language model; after the model parses the signal, the model output is generated and returned to the main toy unit, and the main toy unit immediately completes the interactive response; the main toy unit dynamically adjusts the interactive mode and output content according to the voice input, the connection status of each toy unit and the RFID tag information. The interactive method provided in this embodiment can realize a personalized interactive experience and improve the educational and entertainment value of toys. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a toy interaction method combining RFID with a large language model provided by an embodiment of the present invention;

[0018] Figure 2 for Figure 1 A flowchart of a specific implementation method of step 101 in the illustrated embodiment;

[0019] Figure 3 for Figure 1 A flowchart of a specific implementation method of step 102 in the illustrated embodiment;

[0020] Figure 4 for Figure 1 A flowchart of a specific implementation method of step 103 in the illustrated embodiment;

[0021] Figure 5 A schematic diagram of a process of a user setting a main toy unit mode provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all 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.

[0023] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0024] The embodiment of the present invention provides a toy interaction method combining RFID with a large language model: the method includes a main toy unit and multiple auxiliary toy units, the main toy unit is provided with a controller, connected to an RFID reader, a speaker, a microphone and an RFID tag, and each auxiliary toy unit is equipped with an RFID tag; when the main toy unit is started, the user can connect and set the main toy unit mode through a mobile phone, the microphone collects voice information and sends a signal to the large language model, and when the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends a signal to the large language model; after the model parses the signal, the model output is generated and returned to the main toy unit, and the main toy unit immediately completes the interactive response; the main toy unit dynamically adjusts the interactive mode and output content according to the user setting mode, voice input, the connection status of each toy unit and the RFID tag information. The interactive method provided by the embodiment of the present invention can realize a personalized interactive experience and improve the educational and entertainment value of toys.

[0025] refer to Figure 1 , Figure 1 : is a flow chart of a toy interaction method combining RFID with a large language model provided by an embodiment of the present invention, the interaction method comprising:

[0026] Step 101: When the main toy unit is started, the user can connect and set the main toy unit mode through the mobile phone. The microphone collects voice information and sends signals to the large language model. When the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends signals to the large language model.

[0027] For example, reference Figure 5In the mobile phone interactive interface shown, the user can enter "age", "toy type", "knowledge type", and "voiceprint" as the basic settings of the main toy unit. After that, the microphone starts to collect the user's voice information. For example, if the user says "I want to build a castle", this voice information will be recorded and sent to the server via MQTT Broker; at the same time, when the user connects the main toy unit and the auxiliary toy unit, the RFID reader of the main toy unit reads the RFID tag information of the auxiliary toy unit and sends it to the server via MQTT Broker. The server converts the voice information into text information through the automatic speech recognition (ASR) service, and then sends the text information and RFID information to the large language model. Figure 2 As shown, the specific implementation of step 101 includes:

[0028] Step 1011: The user inputs user information, including age, toy type, knowledge type, and voiceprint. After input, the toy is started. The startup method is voice wake-up and button. After startup, the user begins to interact with the toy by voice and assembly.

[0029] Exemplarily, the user enters personal information through the mobile phone interactive interface: the specific content is age, toy type, knowledge type and voiceprint sample. For example, the user selects "8 years old" as his age in the application, selects "castle" as the toy type, selects "historical story" as the knowledge type, and records a voiceprint sample. The user can start the toy by voice activation or physical button activation. When voice activation, the user starts the toy by saying a preset wake-up word, such as "Hey, toy buddy, I'm ready." When the physical button is activated, the user starts the interaction by pressing the physical button on the main toy unit. This action is recognized by the controller of the main toy unit and triggers the start sequence. After that, the microphone starts to collect the user's voice information. For example, if the user says "I want to build a castle", this voice information will be recorded and converted into digital signals. These signals are transmitted to the server through MQTT Broker; at the same time, the RFID reader of the main toy unit reads the RFID tag information of the auxiliary toy unit when the user brings the main toy unit and the auxiliary toy unit close to each other. After reading, this RFID information will also be transmitted to the server through MQTT Broker.

[0030] Step 1012: The main toy unit obtains voice information through the microphone and the RFID tag information through the RFID reader, and sends the information to the server through the MQTT Broker.

[0031] For example, when a user is building a castle model, he places an auxiliary toy unit representing a castle tower on the castle base as the main toy unit, and the RFID reader in the main toy unit then reads the RFID tag information attached to the auxiliary toy unit. The RFID tag contains characteristic information about the castle tower. At the same time, the user can ask specific questions through the microphone, such as "How high should the tower be?" to seek information or suggestions related to the castle tower. After the microphone captures this voice command, the main toy unit sends the voice information and the RFID tag information to the server through the MQTT Broker.

[0032] Step 1013: The server schedules the ASR service to process the voice information into text information, retrieve the knowledge base to obtain user information, and send the processed text information, user information, and RFID tag information to the large language model.

[0033] Exemplarily, after receiving this voice information through the MQTT Broker, the server first converts the voice command into text format through the automatic speech recognition (ASR) service. The converted text, i.e., "How high should the tower be", provides the server with the user's current interaction request. The server will query its internal user information knowledge base, which stores the user's personal information, preference settings, and historical interaction records. If the user has shown interest in the characteristics of the castle building in the previous interaction, the server will retrieve this preference information from the knowledge base. At the same time, the server will also combine the RFID tag information of the auxiliary toy unit, which is read by the RFID reader when the user brings the auxiliary toy unit close to the main toy unit. If the user has just brought an auxiliary toy unit representing a castle tower close to the main toy unit, the server will recognize this action and use it as context information for the current interaction. Combining the user's voice request, personal preferences, and RFID tag information, the server constructs a comprehensive data packet. This data packet contains the user's request to ask about the height of the tower, the user's interest preference in the characteristics of the castle building, and the RFID tag information that the user is building the castle tower. Finally, the server sends this data packet to the large language model.

[0034] Step 102: After the model analyzes the signal, it generates a model output and returns it to the main toy unit, and the main toy unit then completes the interactive response.

[0035] For example, the large language model analyzes and generates corresponding text information based on the received data packet, which contains the user's request for the height of the tower, the user's interest preference in the characteristics of the castle building, and the RFID tag information of the castle tower that the user is building. For example, if the user asks "how high should the tower be", the large language model will generate detailed text information about the historical height of the castle tower, and may also include educational content such as the changes of the tower in different historical periods, the architectural characteristics and its role in the castle defense system.

[0036] The text information generated by the large language model is then converted into voice information by the server through the TTS service. This conversion process involves converting the text data into a natural voice stream so that the user can hear it through the speaker of the main toy unit. The converted voice information is stored on the server, and a notification of successful storage is sent to the main toy unit through the MQTT Broker. After receiving the notification, the main toy unit downloads the stored voice information through the network and plays it to the user using its built-in speaker. The user hears a detailed explanation of the height of the castle tower and its historical background, thus completing an interactive response. Figure 3 As shown, the specific implementation of step 102 includes:

[0037] Step 1021: The large language model outputs corresponding text information based on the received text information, user information, and RFID tag information.

[0038] For example, during the construction of the castle model, the user asks questions through the microphone of the main toy unit, such as "How high should the tower be?" The question is captured by the microphone and converted into a digital signal. These signals are transmitted to the server through the MQTT Broker, and the server uses the automatic speech recognition (ASR) service to convert the voice signal into text information, that is, "How high should the tower be?" After receiving the text information, the server combines it with the user's personal information, historical interaction records, and RFID tag information for in-depth analysis by the large language model. Based on this comprehensive data, the large language model generates detailed text information about the historical height of the castle tower. This process includes an in-depth understanding of user preferences, historical interaction records, and the current construction scene to ensure that the generated text information is both accurate and educational for users.

[0039] Step 1022: The large model sends the output text information to the server, and the server calls the TTS service to convert the text information into voice information and store it.

[0040] Exemplarily, once the large language model generates text information containing historical heights and architectural features based on the user's query "How tall should the tower be?", the server will perform the following operations: After receiving the text information output by the large language model, the server will use the built-in text-to-speech (TTS) service to convert the text information into natural and fluent voice information. This conversion process ensures that the text information can be understood and accepted by the user in the form of voice. During the conversion process, the server will select voice characteristics suitable for the user's age and interests, such as the speed, tone and style of the voice, based on the user's personal information and preference settings. For example, for an 8-year-old child user, the server will select a voice characteristic with a slower speed and lively tone to increase the attractiveness and comprehensibility of the information. After the conversion is completed, the server stores the generated voice information in an accessible storage medium so that the main toy unit can download and play it.

[0041] Step 1023: The server sends a notification of successful voice information storage to the main toy unit through the MQTT Broker. After receiving the notification, the main toy unit downloads the stored voice information and plays it, completing the interactive response.

[0042] Exemplarily, after the server successfully converts the text information generated by the large language model into voice and stores it, it sends a confirmation notification to the main toy unit through the MQTT Broker. The notification contains the storage location, unique identifier, and synchronization information of the voice information to ensure that the main toy unit can accurately identify and download the corresponding voice content. After receiving the notification forwarded by the MQTT Broker, the main toy unit establishes a connection with the server through the network and downloads the stored voice file according to the information in the notification. After the voice file is downloaded, the speaker of the main toy unit will play the voice information. For example, the speaker clearly plays "In the Middle Ages, the towers of the castle were used for observation and defense. Their height was usually proportional to the strategic importance of the castle, usually more than 20 meters", providing users with real-time construction guidance and historical knowledge. In addition, while playing the voice information, the main toy unit may also provide visual cues that match the voice content through built-in LED lights or other visual feedback mechanisms.

[0043] Step 103: The main toy unit dynamically adjusts the interaction mode and output content according to the user setting mode, voice input, connection status of each toy unit and RFID tag information.

[0044] For example, users can input personal information through the mobile phone interactive interface to customize the interactive experience of the toy. The user's choice is recorded by the system and used to guide and optimize the subsequent interactive content to ensure that the interactive experience matches the user's choice. Further, the main toy unit collects the user's voice instructions through the built-in microphone and transmits these instructions to the server for further processing. The server uses the ASR service to convert the received voice instructions into text format for in-depth analysis and understanding of the user's instruction content. In addition, the main toy unit monitors the connection status of the auxiliary toy unit through the RFID reader to ensure that the system can respond to the physical interaction between the toy units in real time. When the auxiliary toy unit is approached, connected or removed, the RFID reader reads the corresponding RFID tag information and notifies the server of these state changes in real time. After receiving the user's voice instructions and the RFID tag information of the auxiliary toy unit, the server will perform a series of dynamic adjustment operations to ensure the personalization and educational nature of the interactive content. The server will access a comprehensive user information knowledge base, which not only contains basic information about the user, such as age, preferences and historical interaction records, but also integrates educational content and historical data related to toy interaction. When the user adds an auxiliary toy unit of a castle tower during the process of building a castle, the server will recognize this action and retrieve information related to the tower from the knowledge base. This information may include the historical background of the tower, architectural features, etc. The server will dynamically generate interactive content suitable for the user's age and interests based on the user's personal information and the current interaction context. For example, if the user is an 8-year-old child interested in history, the server may provide a simplified historical story about the origin of the castle tower. At the same time, the server will dynamically adjust the content of the knowledge base based on the user's interactive feedback and newly collected information after receiving the data generated by the large model to improve the accuracy and relevance of future interactions. The server sends the adjusted interactive content to the main toy unit, and the main toy unit generates personalized interactive responses based on this information. It will include playing educational voice information related to the castle tower and providing building suggestions. At the same time, the server will monitor the user's interaction process and collect new data points, such as user feedback on specific information or new points of interest. These data will be used to further enrich the user information knowledge base, so that the system can evolve over time and better meet the personalized needs of users. Figure 4 As shown, the specific implementation of step 103 includes:

[0045] Step 1031: The user inputs user information, including age, toy type, knowledge type, and voiceprint, and starts interacting with the toy voice and assembly.

[0046] For example, reference Figure 5In the mobile phone interactive interface shown, users can enter "age", "toy type", "knowledge type" and "voiceprint" as the basic settings of the main toy unit. For example, users can select "8 years old" as age, "castle" as toy type, "historical story" as knowledge type, and record a voiceprint sample for subsequent voice recognition. The user can start the toy by voice activation or physical button activation. When voice activation, the user starts the toy by saying a preset wake-up word, such as "Hey, toy buddy, I'm ready." When physical button activation, the user starts the interaction by pressing the physical button on the main toy unit. This action is recognized by the controller of the main toy unit and triggers the start sequence. After that, the microphone starts to collect the user's voice information. For example, if the user says "I want to build a castle", this voice information will be recorded and converted into digital signals. These signals are transmitted to the server through MQTT Broker; at the same time, the RFID reader of the main toy unit reads the RFID tag information of the auxiliary toy unit when the user brings the main toy unit and the auxiliary toy unit close together. After reading, this RFID information will also be transmitted to the server through MQTT Broker.

[0047] Step 1032: After the user information, voice information, and RFID tag information are sent to the server, they are stored in the user information knowledge base.

[0048] For example, when a user places an auxiliary toy unit representing a castle tower on the main toy unit during the process of building a castle model, the RFID reader in the main toy unit immediately reads the RFID tag information of the auxiliary toy unit. The RFID tag information contains the specific identification of the auxiliary toy unit. At the same time, the user's voice command "I want to know the history of the tower" is captured by the microphone of the main toy unit and converted into a digital signal. These signals are transmitted to the server via the MQTT Broker, and the server uses the automatic speech recognition (ASR) service to convert the voice signal into a text format for further analysis and processing. After receiving the RFID tag information and the textual voice command, the server combines this information with the existing data in the user information knowledge base. The user information knowledge base stores the user's personal information, including but not limited to age, toy type, knowledge type, and voiceprint data. The server updates the user information knowledge base by comparing and integrating this information to ensure that it contains the user's latest preferences and interaction records.

[0049] Step 1033: The server sends the latest user information knowledge base information to the large language model, and the large model dynamically adjusts the output content based on the latest knowledge base information.

[0050] Exemplarily, after updating the user information knowledge base, the server sends the latest user information, including the user's interest in castle towers, the newly added auxiliary toy unit information, and the user's personal information, such as age and knowledge type, to the large language model. This information forms the basis for the large language model to analyze and generate interactive content. After receiving the information provided by the server, the large language model conducts in-depth analysis to understand the user's specific interests and needs. Based on these analysis results, the large language model generates personalized interactive content. For example, if the user shows interest in the defensive function of the castle tower, the large language model may generate a detailed narrative that introduces the defensive role and architectural features of the castle tower in history, taking into account the user's age and interests, ensuring that the content is both educational and attractive. The generated text information is then converted into voice information by the server through the text-to-speech (TTS) service. This conversion process involves converting text data into a natural voice stream so that the user can hear it through the speaker of the main toy unit. The converted voice information is stored at the specified storage address of the server. After storing the voice information, the server sends a storage completion notification to the main toy unit through the MQTT Broker. The notification contains the storage address of the voice information and the necessary synchronization information to ensure that the main toy unit can accurately identify and download the corresponding voice content. After receiving the notification of storage completion, the main toy unit establishes a connection with the server through the network and downloads the voice file according to the storage address in the notification. After the download is complete, the control unit of the main toy unit activates the built-in speaker to play the downloaded voice information to complete the interactive response with the user. In addition, during the process of user interaction with the toy, the server will continue to collect user feedback information and new interaction data, such as the user's interest in specific historical information or the construction behavior of other parts of the castle. These data are used to further enrich the user information knowledge base, so that the system can evolve over time and better meet the personalized needs of users.

[0051] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A toy interaction method combining RFID with a large language model, the method comprising: A main toy unit and multiple auxiliary toy units, the main toy unit is equipped with a controller connected to an RFID reader / writer, a speaker, a microphone and an RFID tag, and each auxiliary toy unit is equipped with an RFID tag; When the main toy unit is started, the user can connect and set the main toy unit mode through the mobile phone, the microphone collects voice information and sends a signal to the large language model, and when the auxiliary toy unit is close to the main toy unit, the RFID tag is activated and sends a signal to the large language model; After the model analyzes the signal, it generates a model output and returns it to the main toy unit, which then completes the interactive response; The main toy unit dynamically adjusts the interaction mode and output content according to the user setting mode, voice input, connection status of each toy unit and RFID tag information.

2. The method according to claim 1, characterized in that: The controller of the main toy unit has the ability to generate interactive responses in real time, and the interactive responses are model outputs generated based on the large language model.

3. The method according to claim 1 or 2, characterized in that: The main toy unit has learning capabilities, can record the interaction history and user preferences with each auxiliary toy unit, and optimize the interaction mode and output content in real time.

4. The method according to claim 1, characterized in that: The RFID reader / writer has the ability to read multiple RFID tags and can distinguish the RFID tag information of different auxiliary toy units.

5. The method according to claim 4, characterized in that: The controller of the main toy unit has a logic function for determining a response based on the connection sequence, and can determine the connection sequence based on the RFID tag information and connection status of the auxiliary toy unit, and return a corresponding model response based on the current connection sequence.

6. The method according to claim 1, characterized in that: The speaker and microphone of the main toy unit support voice recognition and speech synthesis functions, so that interactive responses can be made by voice.

7. The method according to claim 6, characterized in that: The main toy unit can conduct multiple rounds of dialogue interaction based on the voice recognition results and RFID tag information combined with the model output generated by the large language model.

8. The method according to claim 1, characterized in that: The connection state identification mechanism between the main toy unit and the auxiliary toy unit includes approach, contact and separation, and each connection state corresponds to a different interaction mode.

9. The method according to claim 1, characterized in that: The connection between the main toy unit and the cloud server is wireless, and the wireless connection uses the Wi-Fi communication protocol.

10. The method according to claim 1, characterized in that: The main toy unit has multi-language processing capabilities and can provide corresponding language interactions based on the user's geographical location and language preference.

11. The method according to claim 1, characterized in that: The main toy unit is capable of recording the user's interaction data and, with the user's consent, using the data for further training and optimization of the model.

12. The method according to claim 1, characterized in that: The text information collected by the microphone also includes voiceprint recognition, which can identify the voice characteristics of a specific user to provide a personalized interactive response.

13. The method according to claim 1, characterized in that: Users can wirelessly connect to the main toy unit through their mobile phones and set the interaction mode of the main toy unit to achieve a personalized interactive experience.

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