AI-based digital intelligent switch cabinet system control method and device

Abstract

The present disclosure provides an AI-based digital intelligent switch cabinet system control method and device. The method comprises: activating the control system according to user voice information; collecting user identity information, the identity information comprising face image information and voiceprint feature information; if the user identity information is authenticated, the user is granted operation permission; receiving and identifying user voice instructions, and judging the type of the voice instructions through an authentication routing center; and performing corresponding operations according to the type of the voice instructions. The AI-based digital intelligent switch cabinet system control method in the present disclosure can realize intelligent reasoning and accurate question answering of complex power problems.

Description

Technical Field

[0001] This disclosure relates to the field of switchgear control technology, specifically to an AI-based digital intelligent switchgear system control method and device. Background Technology

[0002] With the deepening of the construction of new power systems, distribution networks are rapidly evolving towards digitalization and intelligence. As the core equipment in the distribution network responsible for power distribution, control, and protection, the operating status of switchgear directly affects the stability and power supply reliability of the entire power grid. Power grid operation places higher demands on equipment safety, economy, and operation and maintenance efficiency. Traditional operation and maintenance methods that rely on manual inspection, regular maintenance, and post-event handling are no longer sufficient to meet the current complex and ever-changing operating environment and high-standard service requirements.

[0003] Against this backdrop, promoting the transformation of switchgear from passive fault response to proactive predictive intelligent operation and maintenance has become an important part of the intelligent transformation of power systems. Intelligent switchgear should not only have real-time monitoring capabilities, but also achieve efficient human-machine interaction, intelligent operation and maintenance decision-making, and automated operation execution, thereby comprehensively improving the level of lean equipment management.

[0004] Although current "smart switchgear" systems generally implement basic status monitoring functions, such as data collection through the deployment of sensors for temperature, current, voltage, and partial discharge, and can provide preliminary health status assessments and tiered alarm mechanisms, many problems still emerge in practical applications: Low efficiency of human-computer interaction: Existing systems mainly rely on physical buttons, touch screens or graphical user interfaces (GUIs) for information interaction. In complex on-site operating environments (such as high noise, poor lighting, and the need for two-handed operation), the problems of inconvenient operation and slow response are particularly prominent.

[0005] Weak identity and access control: Identity authentication methods are mostly password input or IC card recognition, lacking a biometric-based identity verification mechanism, which poses security risks and makes it difficult to ensure the compliance of high-security operations (such as remote opening and closing of circuit breakers).

[0006] Limited knowledge service capabilities: The system is limited to data collection and simple alarms, lacking the ability to structure and intelligently integrate and apply historical equipment data, technical documents, expert experience, and fault cases, and thus cannot provide maintenance personnel with timely and accurate knowledge support and decision-making assistance.

[0007] Fragmented multi-source heterogeneous data: Various types of sensor data, image information, text logs, etc. are often in isolation, lacking effective fusion analysis and in-depth mining, making it difficult for the system to form a comprehensive understanding of the overall status of the equipment and predict its trends.

[0008] The aforementioned problems severely restrict the practicality and intelligence level of intelligent switchgear systems, and urgently need to be addressed by introducing advanced artificial intelligence technologies. Summary of the Invention

[0009] The purpose of this disclosure is to overcome the shortcomings of the prior art and provide an AI-based digital intelligent switchgear system control method and device. This AI-based digital intelligent switchgear system control method can realize intelligent reasoning and accurate question answering for complex power problems.

[0010] According to a first aspect of the present disclosure, an AI-based digital intelligent switchgear system control method is provided, the method comprising: The control system is activated based on the user's voice information; Collect user identity information, which includes facial image information and voiceprint feature information; If the user's identity information is successfully authenticated, the user is granted operating permissions; Receive and recognize user voice commands, and determine the type of the voice command through the authentication routing center; Perform the corresponding operation according to the type of the voice command.

[0011] In one embodiment, the method further includes: If user identity information authentication fails, a voice prompt will say "Authorization failed" and the control system will lock for a preset time.

[0012] In one embodiment, before determining the type of the voice command through the authentication routing center, the method further includes: Convert the user's voice commands into text commands; The process of determining the type of the voice command through the authentication routing center includes... The authentication routing center determines the type of the text instruction.

[0013] In one embodiment, performing the corresponding operation based on the type of the voice command includes: If the voice command is a pre-set command, then based on the voice command and the current status information of the switch cabinet, a confirmation text is returned indicating whether the conditions and operation information corresponding to the voice command are met. If a confirmation text is returned indicating that the conditions and operation information corresponding to the voice command are met, then a voice broadcast will be performed and a confirmation pop-up window will be displayed on the screen. Collect the user's operation commands in response to the confirmation pop-up, and collect the user's identity information again to perform secondary identity authentication for the current user; If the current user's secondary authentication is successful, then the user will perform the corresponding operation according to the operation instructions given in the confirmation pop-up.

[0014] In one embodiment, the method further includes: The system will announce the operation results via voice and update the device status. An operation log is generated based on the voice commands, operation instructions, operation results, and device status.

[0015] The method further includes: If the current user's secondary authentication fails, a voice prompt will say "Authorization failed," and the system will return to receive and recognize the user's voice commands again.

[0016] In one embodiment, performing the corresponding operation based on the type of the voice command includes: If the voice command is a complex knowledge request command, the voice command will be sent to the RAG module via API to retrieve relevant fragments from the knowledge base; The retrieved relevant segments and voice commands are input into a large model to generate diagnostic suggestions / operational guidance; Verify the diagnostic suggestions / operational guidelines output by the large model; If the execution rate of the diagnostic suggestions / operation guidance output by the large model is greater than a preset value, then flow-controlled speech synthesis is performed and broadcast, and the diagnostic suggestions / operation guidance is visualized on the screen.

[0017] In one embodiment, the method further includes: Based on the diagnostic suggestions / operation instructions, retrieve the relevant knowledge base to obtain the corresponding knowledge links, and display the corresponding knowledge links on the screen.

[0018] In one embodiment, determining the type of the voice command through the authentication routing center includes: If the user's voice command information contains the keywords "close / open / query", then the user command is determined to be a preset command. If the user's voice command information contains "why / how / analyze", then the user command is determined to be a complex knowledge request command.

[0019] According to a second aspect of the present disclosure, an AI-based digital intelligent switchgear system control device is provided. The device includes: an activation module, a data acquisition module, an authentication module, a processing module, and an operation module; wherein... The activation module is used to activate the control system based on the user's voice information; The acquisition module is used to acquire user identity information, which includes facial image information and voiceprint feature information. The authentication module is used to grant the user operation permissions when the user's identity information is successfully authenticated; The processing module is used to receive and recognize user voice commands, and determine the type of the voice command through the authentication routing center; The operation module is used to perform corresponding operations according to the type of the voice command.

[0020] This disclosure provides an AI-based digital intelligent switchgear system control method. By constructing an intelligent switchgear system with natural language interaction as the entry point, multimodal identity authentication as the guarantee, and an enhanced retrieval AI model as the decision engine, it achieves basic command responses based on natural language voice interaction at the edge device, such as voice-activated switching control of the switchgear, and access to HMI device parameters and historical data. Through local deployment of a RAG+ professional large language model, it achieves complex command knowledge reasoning responses based on natural language voice interaction at the edge device. Therefore, the method provided in this embodiment not only has significant technical advancement and engineering feasibility, but also represents a key path to promote the leap of power equipment towards an advanced form of intelligent operation and maintenance characterized by "knowable status, controllable behavior, and predictable risks," possessing important practical significance and broad application prospects. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0022] Figure 1 This is a schematic diagram of a control method for an AI-based digital intelligent switchgear system provided in an embodiment of this disclosure.

[0023] Figure 2 This is a flowchart illustrating an AI-based digital intelligent switchgear system control method provided in an embodiment of this disclosure.

[0024] Figure 3 This is a flowchart illustrating the intelligent control and data retrieval process of an AI-based digital intelligent switchgear system, provided as an embodiment of this disclosure.

[0025] Figure 4 This disclosure provides an enhanced retrieval AI large model switchgear knowledge reasoning process for an AI-based digital intelligent switchgear system.

[0026] Figure 5 This is an architecture diagram of an AI-based digital intelligent switchgear system control device provided in an embodiment of this disclosure. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0028] Figure 1 This diagram illustrates a control method for an AI-based digital intelligent switchgear system, as provided in an embodiment of this disclosure. Figure 1 As shown, maintenance personnel activate the device using a wake word (e.g., "Hello! Xiao Tuo"). After activation, the device synchronously collects facial images and voiceprint information to complete identity verification through multimodal fusion. Subsequently, the system dynamically grants permission levels (e.g., general query, device operation, system configuration, etc.) based on the identity. When the system is in an active state, it recognizes voice commands in real time (e.g., device one-click power outage) and determines whether the command type is a basic preset command or a complex knowledge request (requiring reasoning) through the authentication routing center.

[0029] If the command is a pre-set basic instruction (taking control instructions as an example), the basic instruction system will, based on the instruction requirements and the current real-time system status, return whether the conditions for executing the instruction are met and operation confirmation text (e.g., whether the switch is ready to close and whether to confirm closing). Using flow control technology, the system synthesizes the returned text into speech and broadcasts the returned voice result, while simultaneously displaying the returned content in real-time on the local display screen. Subsequently, the system collects the voiceprint and facial image information of the maintenance personnel confirming the operation, completes identity verification through multimodal fusion, and then executes the device control operation. If the authentication routing center's decision command type is a complex knowledge request command, the system will send the knowledge request command to the RAG module via API. RAG will then retrieve relevant fragments from the knowledge base (such as similar fault cases, operating procedures, etc.) and input the command plus the retrieved fragments into the large model to generate diagnostic suggestions / operational guidance. Finally, the output of the large model is fed back via both flow-controlled speech synthesis and on-screen dual-channel feedback.

[0030] It should be noted that this embodiment deploys a Domain Large Model (LLM) locally, integrating real-time device data, historical cases, and technical documents to construct a RAG-enhanced power knowledge graph (fault tree, operating procedures, and component association library).

[0031] Figure 2 This is a flowchart illustrating an AI-based digital intelligent switchgear system control method provided in an embodiment of this disclosure. Figure 2 As shown, the method includes: Step 201: Activate the control system based on the user's voice information; In this step, when the maintenance personnel speak a newline word (such as "Hello! Xiao Tuo"), the terminal activates the microphone, camera, and display screen.

[0032] Step 202: Collect user identity information, which includes facial image information and voiceprint feature information; In this step, the terminal simultaneously collects voiceprint feature information and facial image information, performs noise reduction processing on the voiceprint feature information, and performs infrared liveness detection processing on the facial image information.

[0033] Step 203: If the user's identity information is successfully authenticated, then grant the user operation permissions; In this step, if the matching degree of voiceprint feature information is determined to be greater than or equal to 95%, and the matching degree of face image information is greater than or equal to 98%, then the user is granted permission level (e.g., query / operation / configuration).

[0034] In one embodiment, the method further includes: If user identity information authentication fails, a voice prompt will say "Authorization failed" and the control system will lock for a preset time.

[0035] In this embodiment, if the user's identity information authentication fails, a voice prompt will say "Authorization failed" and the control system will lock for 30 seconds.

[0036] Step 204: Receive and recognize user voice commands, and determine the type of the voice command through the authentication routing center; In one embodiment, determining the type of the voice command through the authentication routing center includes: If the user's voice command information contains the keywords "close / open / query", then the user command is determined to be a preset command. If the user's voice command information contains "why / how / analyze", then the user command is determined to be a complex knowledge request command.

[0037] In one embodiment, before determining the type of the voice command through the authentication routing center, the method further includes: Convert the user's voice commands into text commands; The process of determining the type of the voice command through the authentication routing center includes: The authentication routing center determines the type of the text instruction.

[0038] In this embodiment, the user's voice command is first converted into a text command (such as "execute switchgear 002 power outage"), and then the authentication routing center determines the type of the text command.

[0039] Step 205: Perform the corresponding operation according to the type of the voice command.

[0040] In one implementation, performing the corresponding operation based on the type of the voice command includes: If the voice command is a pre-set command, then based on the voice command and the current status information of the switch cabinet, a confirmation text is returned indicating whether the conditions and operation information corresponding to the voice command are met. If a confirmation text is returned indicating that the conditions and operation information corresponding to the voice command are met, then a voice broadcast will be performed and a confirmation pop-up window will be displayed on the screen. Collect the user's operation commands in response to the confirmation pop-up, and collect the user's identity information again to perform secondary identity authentication for the current user; If the current user's secondary authentication is successful, then the user will perform the corresponding operation according to the operation instructions given in the confirmation pop-up.

[0041] In one embodiment, the method further includes: The system will announce the operation results via voice and update the device status. An operation log is generated based on the voice commands, operation instructions, operation results, and device status.

[0042] In one embodiment, the method further includes: If the current user's secondary authentication fails, a voice prompt will say "Authorization failed," and the system will return to receive and recognize the user's voice commands again.

[0043] The following section uses barrier gate control as an example to explain in detail the specific implementation steps of this method. Figure 3 As shown, this implementation method includes the following steps: Step 1: Wake-up and Activation When the maintenance personnel say the wake-up word (such as "Hello! Xiao Tuo"), the terminal activates the microphone, camera, and display screen.

[0044] Step 2: Multimodal Identity Authentication Simultaneous acquisition: voiceprint features (noise reduction processing) + face image (infrared liveness detection); Authentication process: If the voiceprint matching accuracy is ≥95% and the face matching accuracy is ≥98%, grant the appropriate permission level (query / operation / configuration). else: The system will display a voice prompt saying "Authentication failed" and lock the device for 30 seconds.

[0045] Step 3: Instruction Classification Decision: Real-time speech recognition and text conversion (e.g., "Execute switchgear 002 power outage"); The decision types of the authentication routing center are shown in Table 1: Table 1

[0046] Step 4: Basic Instruction Processing System detection execution conditions: SELECT Operational Status FROM Device Real-Time Table; WHERE Cabinet Number='002' AND Status='Standby' AND Lockout Signal=0.

[0047] Speech synthesis feedback: "Kiosk 002 is ready to trip. Do you confirm the operation?" Dual-channel output: Voice broadcast (TTS flow control technology, noise reduction gain); A red confirmation pop-up window (including an operation risk warning) is displayed on the screen.

[0048] Step 5, Secondary Authentication and Execution: After receiving the confirmation command, the face image information and voiceprint feature information are collected again (1-second timeout detection).

[0049] Perform the following actions after successful authentication: # Send control commands: MQTT publish -t "cmd / swg-002" -m "SHUTDOWN_NOW"; Execution result feedback: "002 switchgear has been successfully tripped. Current status: Power outage." In another implementation, the step of performing the corresponding operation based on the type of the voice command includes: If the voice command is a complex knowledge request command, the voice command will be sent to the RAG module via API to retrieve relevant fragments from the knowledge base; The retrieved relevant segments and voice commands are input into a large model to generate diagnostic suggestions / operational guidance; Verify the diagnostic suggestions / operational guidelines output by the large model; If the execution rate of the diagnostic suggestions / operation guidance output by the large model is greater than a preset value, then flow-controlled speech synthesis is performed and broadcast, and the diagnostic suggestions / operation guidance is visualized on the screen.

[0050] In one embodiment, the method further includes: Based on the diagnostic suggestions / operation instructions, retrieve the relevant knowledge base to obtain the corresponding knowledge links, and display the corresponding knowledge links on the screen.

[0051] The following section uses barrier gate control as an example to explain in detail the specific implementation steps of this method. Figure 4 As shown, this implementation method includes the following steps: Step 1: Voice command input: When maintenance personnel make complex knowledge requests (such as "Why is the temperature of the A phase contact arm in cabinet 002 too high?"), the terminal activates the voice recognition module (end-side ASR model).

[0052] Step 2: Command Classification and Routing The authentication routing center analyzes the characteristics of the commands: If "why" is in the text, or "how" is in the text, or "analysis" is in the text: instruction_type = "COMPLEX_KNOWLEDGE"; else: instruction_type = "BASIC_CONTROL"; Complex requests are sent to the RAG module via API.

[0053] Step 3: RAG Knowledge Retrieval The search results for the local knowledge base are shown in Table 2: Table 2

[0054] Similarity ranking algorithm: BM25 + cosine similarity.

[0055] Step 4: Large model inference generation.

[0056] Input format: json { Query: "Why is the contact arm temperature of phase A in cabinet 002 too high?" "context": [ Case CX2023-018: Loose bolts cause increased contact resistance... "Procedure DOC-005: Standard Procedure for Cleaning Touch Arms..." "Knowledge Graph Node: Contact Arm Temperature Rise > 65K Requires Emergency Handling" ] } Local LLM response generation: Possible reasons: 1. Loose bolts (85% probability) - See case CX2023-018; 2. Contact arm oxidation (probability 10%) - Reference procedure DOC-005.

[0057] Recommendations: - Immediately reduce the load to below 800A; - Re-measured using an infrared thermal imager; Clean the contact arm and tighten the bolts according to the procedure.

[0058] Step 5: Multimodal results feedback.

[0059] Speech synthesis (TTS flow control): When ambient noise is >70dB, enhance the low-frequency component; Key parameters are automatically emphasized (e.g., "85%").

[0060] Screen display: <h3> Diagnostic results< / h3> Cause: Loose bolts (85%) <button @click="showCase('CX2023-018')"> View Case Studies< / button> ； Solution: Reduce load + clean and tighten. <button @click="showSOP('DOC-005')"> See procedures< / button> .

[0061] This disclosure provides an AI-based digital intelligent switchgear system control method. By constructing an intelligent switchgear system with natural language interaction as the entry point, multimodal identity authentication as the guarantee, and an enhanced retrieval AI model as the decision engine, it achieves basic command responses based on natural language voice interaction at the edge device, such as voice-activated switching control of the switchgear, and access to HMI device parameters and historical data. Through local deployment of a RAG+ professional large language model, it achieves complex command knowledge reasoning responses based on natural language voice interaction at the edge device. Therefore, the method provided in this embodiment not only has significant technical advancement and engineering feasibility, but also represents a key path to promote the leap of power equipment towards an advanced form of intelligent operation and maintenance characterized by "knowable status, controllable behavior, and predictable risks," possessing important practical significance and broad application prospects.

[0062] Figure 5 This is an architecture diagram of an AI-based digital intelligent switchgear system control device provided in an embodiment of this disclosure. Figure 5 As shown, the device includes: an activation module 501, a data acquisition module 502, an authentication module 503, a processing module 504, and an operation module 505; wherein, the activation module 501 is used to activate the control system based on user voice information; the data acquisition module 502 is used to acquire user identity information, including facial image information and voiceprint feature information; the authentication module 503 is used to grant the user operation permissions when the user's identity information is successfully authenticated; the processing module 504 is used to receive and recognize user voice commands, and determine the type of the voice command through an authentication routing center; the operation module is used to perform corresponding operations according to the type of the voice command.

[0063] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] Although preferred embodiments of this disclosure have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this disclosure.

[0068] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

Claims

1. A control method for a digital intelligent switchgear system based on AI, characterized in that, The method includes: The control system is activated based on the user's voice information; Collect user identity information, which includes facial image information and voiceprint feature information; If the user's identity information is successfully authenticated, the user is granted operating permissions; Receive and recognize user voice commands, and determine the type of the voice command through the authentication routing center; Perform the corresponding operation according to the type of the voice command.

2. The method according to claim 1, characterized in that, The method further includes: If user identity information authentication fails, a voice prompt will say "Authorization failed" and the control system will lock for a preset time.

3. The method according to claim 1, characterized in that, Before determining the type of the voice command through the authentication routing center, the method further includes: Convert the user's voice commands into text commands; The process of determining the type of the voice command through the authentication routing center includes: The authentication routing center determines the type of the text instruction.

4. The method according to claim 3, characterized in that, The step of performing the corresponding operation based on the type of the voice command includes: If the voice command is a pre-set command, then based on the voice command and the current status information of the switch cabinet, a confirmation text is returned indicating whether the conditions and operation information corresponding to the voice command are met. If a confirmation text is returned indicating that the conditions and operation information corresponding to the voice command are met, then a voice broadcast will be performed and a confirmation pop-up window will be displayed on the screen. Collect the user's operation commands in response to the confirmation pop-up, and collect the user's identity information again to perform secondary identity authentication for the current user; If the current user's secondary authentication is successful, then the user will perform the corresponding operation according to the operation instructions given in the confirmation pop-up.

5. The method according to claim 4, characterized in that, The method further includes: The system will announce the operation results via voice and update the device status. An operation log is generated based on the voice commands, operation instructions, operation results, and device status.

6. The method according to claim 4, characterized in that, The method further includes: If the current user's secondary authentication fails, a voice prompt will say "Authorization failed," and the system will return to receive and recognize the user's voice commands again.

7. The method according to claim 1, characterized in that, The step of performing the corresponding operation based on the type of the voice command includes: If the voice command is a complex knowledge request command, the voice command will be sent to the RAG module via API to retrieve relevant fragments from the knowledge base; The retrieved relevant segments and voice commands are input into a large model to generate diagnostic suggestions / operational guidance; Verify the diagnostic suggestions / operational guidelines output by the large model; If the execution rate of the diagnostic suggestions / operation guidance output by the large model is greater than a preset value, then flow-controlled speech synthesis is performed and broadcast, and the diagnostic suggestions / operation guidance is visualized on the screen.

8. The method according to claim 6, characterized in that, The method further includes: Based on the diagnostic suggestions / operation instructions, retrieve the relevant knowledge base to obtain the corresponding knowledge links, and display the corresponding knowledge links on the screen.

9. The method according to claim 1, characterized in that, The process of determining the type of the voice command through the authentication routing center includes: If the user's voice command information contains the keywords "close / open / query", then the user command is determined to be a preset command. If the user's voice command information contains "why / how / analyze", then the user command is determined to be a complex knowledge request command.

10. A control device for a digital intelligent switchgear system based on AI, characterized in that, The device includes: an activation module, a data acquisition module, an authentication module, a processing module, and an operation module; wherein, The activation module is used to activate the control system based on the user's voice information; The acquisition module is used to acquire user identity information, which includes facial image information and voiceprint feature information. The authentication module is used to grant the user operation permissions when the user's identity information is successfully authenticated; The processing module is used to receive and recognize user voice commands, and determine the type of the voice command through the authentication routing center; The operation module is used to perform corresponding operations according to the type of the voice command.

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