Household small electric appliance diagnosis system based on AR imaging and AI interaction

Abstract

The invention provides a household small electric appliance diagnosis system based on AR imaging and AI interaction, which relates to the technical field of intelligent household appliance intelligent diagnosis and comprises a multi-mode intelligent sensing framework, a modular AR detection accessory, an AI diagnosis analysis center and a human-computer interaction interface. According to the modular AR detection accessory, the sensor module is rapidly replaced through the magnetic type universal interface base, the requirement for collecting temperature, circuit, sound and other multi-mode data is met, the hardware adaptability is remarkably improved, the modular AR detection accessory has the functions of low power consumption and efficient power supply, a battery is arranged in the base, reverse wireless charging is supported, and the reliability is high. The problem of endurance of a high-power-consumption module is solved, a modular AR detection accessory is combined with an AR terminal through a magnetic type sensor module, fault three-dimensional coordinates are superposed on real equipment in real time, a user can visually check internal problem points, a mixed reality operation table supports gesture, voice and eye movement control, the operation threshold is lowered, and the operation efficiency is improved. According to the invention, the user is directly guided to carry out precise maintenance through the AR marking system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of intelligent diagnosis of intelligent household appliances, and particularly relates to a household small-appliance diagnosis system based on AR imaging and AI interaction. BACKGROUND

[0002] According to a household appliance and a fault diagnosis system, method and server thereof disclosed by Chinese patent No. CN107491021A, the system comprises a household appliance and a server, and communication is carried out between the household appliance and the server, wherein the household appliance comprises an acquisition module, the acquisition module is used for acquiring running parameter information of the household appliance, and the running parameter information is sent to the server through communication between the household appliance and the server; the server stores a fault database, the server is used for receiving the running parameter information, and the running parameter information is analyzed according to the fault database to diagnose whether the household appliance has a fault, and fault information is generated when the household appliance has a fault, and the fault information is sent to the household appliance, so that the fault information is presented to a user through the household appliance. According to the system of the application, whether the household appliance has a fault can be conveniently and effectively diagnosed, and fault information is sent to the user when the fault occurs.

[0003] The above patent document and prior art have the following technical problems in use:

[0004] Problem one, the traditional household appliance diagnosis system usually relies on a single sensor or a preset self-checking program (such as temperature and current detection), which is difficult to cover complex fault scenarios, for example, the Haier patent analyzes historical fault data through big data and AI, but mainly relies on the sensors of the equipment itself, and cannot detect internal mechanical faults through the shell;

[0005] Problem two, the existing system mainly uses fixed sensors or special equipment, which cannot be flexibly adapted to different maintenance scenes, for example, the intelligent control system of Midea relies on wearable devices, but does not involve modular hardware expansion, and the existing technology relies on the active fault reporting of users (such as Haier needs users to wait for the service to push the report), lacks a direct fault positioning tool, resulting in complex operation and low efficiency;

[0006] Problem three, the traditional AI diagnosis system relies on cloud analysis, which has a delay and the accuracy is limited by the data source, for example, the electrical fault diagnosis patent of Taiji Computer does not mention real-time multi-modal data fusion, and the existing system is mainly single-device diagnosis, lacking cross-device linkage capability, for example, the Haier patent realizes device self-checking, but does not mention cooperation with other household appliances;

[0007] Question four: Traditional systems rely on mobile APP or simple voice prompts, lack intuitive operation interface, for example, Nantong Wotai's recognition model patent does not involve AR interaction, and traditional maintenance process users passively accept services (such as Haier case), lack of autonomous diagnostic capability, and the result output is non-standard (such as relying on maintenance personnel experience).

[0008] Therefore, a household small appliance diagnosis system based on AR imaging and AI interaction is needed to solve the above problems. SUMMARY

[0009] Technical problems solved

[0010] In view of the deficiencies in the prior art, the present application provides a household small appliance diagnosis system based on AR imaging and AI interaction, which solves the following problems:

[0011] 1. The problem that traditional household appliance diagnosis systems usually rely on single sensor or pre-set self-checking program (such as temperature and current detection) and are difficult to cover complex fault scenarios.

[0012] 2. The problem that existing systems use fixed sensors or special equipment, which cannot be flexibly adapted to different maintenance scenarios, and the existing technology relies on user-initiated fault reporting, lacks intuitive fault positioning tools, resulting in complex operation and low efficiency.

[0013] 3. The problem that traditional AI diagnosis systems rely on cloud analysis, have delay and limited accuracy due to data sources, and existing systems are mostly single-device diagnosis, lacking cross-device linkage capability.

[0014] 4. The problem that traditional systems rely on mobile APP or simple voice prompts, lack intuitive operation interface, and traditional maintenance process users passively accept services, lack autonomous diagnostic capability, and result output is non-standard.

[0015] Technical scheme

[0016] To achieve the above purpose, the present application realizes the following technical scheme: a household small appliance diagnosis system based on AR imaging and AI interaction, comprising a multi-modal intelligent sensing architecture, a modular AR detection accessory, an AI diagnosis analysis hub and a human-computer interaction interface.

[0017] The multi-modal intelligent sensing architecture includes a millimeter wave radar, a multi-spectral scanning array and an intelligent voiceprint module, which collects real-time data of household appliances through the multi-modal intelligent sensing architecture, including external and internal visible image data, multi-scan data and voiceprint data.

[0018] The modular AR detection accessory is matched with an AR terminal device (mobile phone / AR glasses) and a detachable magnetic AR sensor module. Users can use it as needed. The detachable magnetic AR sensor module is provided with a magnetic universal interface base, which physically fixes the mobile phone / AR glasses and the AR sensor module and is fixed, and automatically identifies the type of the connected expansion module.

[0019] The AI diagnosis analysis hub is an integrated system based on artificial intelligence technology. Through a multi-modal intelligent perception architecture, a modular AR detection accessory and a deep learning algorithm, the intelligent hub realizes device state monitoring, abnormal diagnosis and autonomous decision-making. Its core functions include real-time perception, data analysis, risk prediction and cross-device collaboration.

[0020] The human-computer interaction interface includes a mixed reality operation table (supports gesture / voice / eye movement control), an augmented reality marking system (fault point three-dimensional coordinate error <1mm) and an intelligent tool recommendation engine (automatically matches maintenance tool specifications).

[0021] Preferably, the multi-modal intelligent perception architecture includes a millimeter wave radar (60GHz FMCW radar). Its function is to reconstruct the internal three-dimensional structure of the device through electromagnetic wave reflection signals, penetrate the plastic / glass shell (penetration depth ≤5cm), detect mechanical faults such as motor rotor eccentricity and gear jamming, and has a resolution of 0.5mm (axial) and a scanning frame rate of 30fps. Power consumption is <1.2W (low power consumption mode).

[0022] Preferably, the multi-modal intelligent perception architecture includes a multi-spectral scanning array, which includes visible light imaging, infrared thermal imaging and electromagnetic field detection. Visible light imaging: equipped with a CMOS+ToF dual-mode camera, realizing sub-millimeter level structure scanning (accuracy up to 0.3mm / m 3 ) in 0.1 lux low light environment, infrared thermal imaging using a non-cooled micro bolometer, achieving 0.05℃ thermal sensitivity in 7.5-14μm band, dynamic range covering -20℃~550℃, electromagnetic field detection: integrated three-axis magnetoresistance sensor array (sensitivity 10nT), reconfigurable antenna realizes 10kHz-6GHz electromagnetic radiation feature extraction.

[0023] Preferably, the multi-modal intelligent perception architecture includes an intelligent voiceprint collection module, which includes acoustic array design, nonlinear acoustic detection and ultrasonic diagnosis extension. Acoustic array design: microphone ring array, beamforming algorithm realizes 30dB environmental noise suppression, nonlinear acoustic detection: identifies hidden faults such as bearing wear through harmonic distortion analysis (THD <0.01%), ultrasonic diagnosis extension: integrated 40kHz piezoelectric sensor, supports motor winding partial discharge detection.

[0024] The modular AR detection accessory is preferably designed to enhance the basic detection capabilities of a mobile phone / AR glasses through detachable and expandable hardware modules, solve the pain points of multi-modal data acquisition (temperature, circuit, sound, etc.) in home appliance maintenance, and provide a magnetic attraction type universal interface base structure for the detachable magnetic attraction type AR sensor module. The mobile phone holder + multi-touch magnetic attraction interface (compatible with Qi wireless charging standard); built-in low-power Bluetooth 5.2 chip (for data transmission), which functions to physically fix the mobile phone / AR glasses and provide power, automatically identifies the type of extended module connected, and adopts a Halbach array magnet layout for the mechanical structure of the magnetic attraction quick-release design, achieving precise alignment (error <0.1 mm) of the module and the base, and is equipped with a gold-plated spring needle for touch points to ensure stable conductivity, interacts with the user, and a "click" sound indicates successful connection, the AR interface synchronously displays the module icon (such as automatically switching to an infrared view when a thermal imager is connected), module power supply and communication, power supply scheme, base built-in 500mAh battery, module power supply through magnetic attraction interface (thermal imager and other high-power consumption modules can be equipped with a battery), supports reverse wireless charging (mobile phone can provide emergency power supply for the module), data transmission, low-latency Bluetooth transmission (<20ms), and high-flow data such as thermal imaging using USB3.0 Type-C direct connection.

[0025] Preferably, the core features of the AI diagnostic analysis center are multi-dimensional data fusion and deep learning, proactive service and scenario-based adaptation, high precision and standardized output, and scalability. Multi-dimensional data fusion and deep learning involve multi-modal interaction: integrating image recognition (such as AI vision modules), voice commands, sensor data, and other multi-source information to build dynamic environmental perception capabilities (such as identifying the risk of a pot overflowing in a smoke machine and coordinating the gas stove to reduce the heat), and algorithm optimization: relying on NPU chips and cloud computing power to enhance deep learning model training efficiency and improve diagnostic accuracy. Proactive service and scenario-based adaptation involve real-time early warning and intervention: through cloud collaboration and health database integration, risks are actively identified (such as a refrigerator scanning food to alert excessive nitrite levels, and an air conditioner monitoring respiration to adjust the air supply mode), and scenario-based intelligent decision-making: supporting cross-device coordination (such as Haier's "unmanned household" system for full-house automation) and optimizing service logic based on user habits (such as personalized content recommendations on TVs). High precision and standardized output involve medical-grade diagnostic capabilities: in the medical field, AI-assisted diagnostic systems can generate standardized image reports (such as automatic classification of lung nodules and marking of vascular plaques), reducing human error, and industrial-grade reliability: in non-medical scenarios, through high-frequency hardware (such as Mini-LED display modules) and high-speed data processing technology, diagnostic stability is ensured (such as fire warning and sprinkler coordination in charging stations). Scalability and extensibility involve cloud collaboration architecture: supporting massive data storage and analysis, enabling real-time interaction between device and cloud (such as generating personalized solutions based on tongue and pulse data in traditional Chinese medicine diagnosis), and industry chain adaptation: hardware upgrades (such as high-frequency motherboard materials) and software algorithm iterations are synchronized to accelerate technology commercialization (such as AI TV penetration reaching 30%-40% by 2025). Through technology integration and scenario innovation, the AI diagnostic analysis center has made a leap from passive response to proactive service, with its core value being to improve diagnostic efficiency, ensure safety, and optimize user experience.

[0026] Preferably, the mixed reality operation table of the human-computer interaction interface can be controlled by gestures; a 3D convolutional neural network (PointNet++ architecture) is used to support 14 types of maintenance-specific gestures (such as a "screw tightening" virtual gesture torque feedback), with a delay of <80ms (from hand movement to virtual tool response), voice interaction; a domain-specific ASR engine (98.2% recognition rate in the home appliance maintenance vertical field) supports multi-modal instruction fusion (such as "use this wrench (gesture pointing) to loosen the red marked nut (eye gaze fixation)"), eye movement control; the pupil tracking accuracy is 0.5° visual angle error, and the gaze hotspot prediction algorithm (preloading related maintenance manuals 200ms in advance); its hardware implementation is a holographic display module, using light field display technology (field of view angle 60°x40°), virtual tool rendering resolution: 4K@120Hz, and a tactile feedback system, piezoelectric ceramic micro-brake array (can simulate 5 types of tool touch), torque feedback accuracy: ±0.1 N·m.

[0027] Preferably, the augmented reality marker system spatial positioning architecture of the human-computer interaction interface includes multi-sensor fusion positioning; ToF depth sensor (accuracy ±1mm@0.5m), inertial navigation unit (IMU 1000Hz sampling), visual SLAM (improved version of ORB-SLAM3), dynamic calibration algorithm, prior knowledge constraint optimization based on device CAD model, to realize screw hole position marking error <0.8mm, information presentation technology; context adaptive marking, automatic adjustment of marking density (novice mode: marking point spacing ≥15cm; expert mode: display micron-level cracks), danger warning pulse flicker (frequency 3Hz red halo), multi-level perspective, shell transparency: 10%-90% electric adjustment, fault current visualization (simulate circuit abnormal path with particle flow), performance indicators; marking delay: 18ms (from AI diagnosis to AR display), support simultaneous marking: 200+ dynamic elements, ambient light adaptation range: 50-100000lux.

[0028] Preferably, the intelligent tool recommendation engine of the human-computer interaction interface includes knowledge base construction; tool feature vectorization, dimension is 128-dimensional attribute (torque range / opening size / insulation level, etc.), covering 6500+ standard tools (including ISO, ANSI, DIN, etc. Standards), maintenance scene atlas, establishing a "fault phenomenon-disassembly step-tool requirement" correlation network, containing 2.8 million tool use cases, recommendation algorithm; multi-objective optimization model, objective function is tool matching degree (65%) + user skill level (25%) + tool availability (10%), real-time call local hardware store inventory API (recommend alternative tools), enhance recommendation; generate tool use demonstration with GAN (such as show the correct grip of a special wrench), intelligent combination of tool kits (save 60% tool carrying amount), interactive interface; 3D tool projection, support 1:1 virtual tool superposition comparison (prevent users from taking wrong similar tools), tool history use record tracing (red last time caused scratch screwdriver), procurement guide; Scan code direct connection e-commerce platform (identify tool model through AR marker), rental tool navigation (display available tool rental points within 5km).

[0029] Beneficial effects

[0030] The application provides a household small appliance diagnosis system based on AR imaging and AI interaction. It has the following

[0031] Beneficial effects:

[0032] 1、The application integrates millimeter wave radar (detects internal mechanical failure through penetrating plastic / glass shell), multi-spectral scanning (visible light, infrared thermal imaging, electromagnetic field detection) and intelligent voiceprint module (harmonic analysis of implicit failure) through a multi-modal perception architecture, realizes multi-dimensional data acquisition, covers multiple types of failures such as mechanics, thermodynamics and electromagnetism, and has a penetrating detection capability, and the millimeter wave radar (resolution 0.5mm) can reconstruct the internal three-dimensional structure of the equipment, solving the pain point of traditional methods that need to disassemble and detect.

[0033] 2、The modular AR detection accessory of the application supports rapid replacement of sensor modules (such as thermal imaging, acoustic array) through a magnetic universal interface base (error <0.1mm), meets the needs of multi-modal data acquisition such as temperature, circuit and sound, significantly improves hardware adaptability, has the functions of low power consumption and efficient power supply, the base is built-in battery and supports reverse wireless charging, solves the endurance problem of high-power modules (such as thermal imaging), and the modular AR detection accessory combines the magnetic sensor module (such as thermal imaging, acoustic array) with the AR terminal (mobile phone / glasses) to superimpose the three-dimensional coordinates (error <1mm) of the fault on the real equipment in real time, so that the user can directly view the internal problem point, and the mixed reality operation platform supports gesture, voice and eye movement control, reduces the operation threshold, for example, in the Haier case, the user needs to handle the filter screen blockage by himself, and the patent directly guides the user to accurately repair through the AR marking system.

[0034] 3、The application realizes multi-dimensional data fusion (image, voice, sensor data) and deep learning optimization through the combination of AI diagnosis analysis core and NPU chip and cloud computing power, improves the fault recognition speed (scan frame rate 30fps) and accuracy (thermal sensitivity 0.05℃), and ensures real-time feedback through high-frequency hardware (such as Mini-LED display module) and high-speed data processing technology (Bluetooth delay <20ms), and has scenario-based intelligent decision-making, supports cross-device linkage (such as linking gas stove to reduce firepower after identifying the risk of overflowing pot by range hood), and optimizes service logic through user habits (such as personalized repair tool recommendation), and realizes industrial-level reliable diagnosis output by borrowing the image report generation technology of medical AI (such as lung nodule classification).

[0035] 4、The application supports gesture, voice and eye movement control through the mixed reality operation platform, enhances the operation freedom of the user, has an AR marking system, the three-dimensional coordinate error of the fault point is <1mm, the fault position and repair steps are directly displayed through the AR interface, the operation threshold of the user is reduced, there is an intelligent tool recommendation engine, the specifications of repair tools are automatically matched, and standardized operation instructions (such as torque value and disassembly sequence) are provided through the AR interface, human error is reduced, medical-level diagnosis capability (such as lung nodule classification logic) is migrated to the household appliance field, automatic generation of fault classification and report is realized, and service consistency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] Fig. 1 Flowchart of the present application;

[0037] Fig. 2 Diagnostic system diagram of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. Specific embodiment one:

[0040] As shown in the figure, the household small appliance diagnostic system based on AR imaging and AI interaction includes a multi-modal intelligent sensing architecture, a modular AR detection accessory, an AI diagnostic analysis hub, and a human-computer interaction interface. Figs. 1-2

[0041] The multi-modal intelligent sensing architecture includes a millimeter wave radar, a multi-spectral scanning array, and an intelligent voiceprint module. Real-time data of the household appliance operation is collected through the multi-modal intelligent sensing architecture, including external and internal visible image data, multi-scan data, and voiceprint data.

[0042] The modular AR detection accessory is matched with an AR terminal device (mobile phone / AR glasses) and a detachable magnetic AR sensor module. Users can use it as needed. The detachable magnetic AR sensor module is provided with a magnetic universal interface base, which physically fixes the mobile phone / AR glasses and the AR sensor module and automatically identifies the type of the connected expansion module.

[0043] The AI diagnostic analysis hub is an integrated system based on artificial intelligence technology. Through the multi-modal intelligent sensing architecture, the modular AR detection accessory, and deep learning algorithms, it realizes the intelligent hub of device state monitoring, abnormal diagnosis, and autonomous decision-making. Its core functions include real-time sensing, data analysis, risk prediction, and cross-device collaboration.

[0044] The human-computer interaction interface includes a mixed reality operation table (supporting gesture / voice / eye movement control), an augmented reality marking system (fault point three-dimensional coordinate error <1mm), and an intelligent tool recommendation engine (automatically matching maintenance tool specifications).

[0045] ​Multi-modal intelligent perception architecture millimeter wave radar (60GHz FMCW radar), its function is to reconstruct the internal three-dimensional structure of the device through electromagnetic wave reflection signals, penetrate the plastic / glass shell (penetration depth ≤5cm), detect mechanical faults such as motor rotor eccentricity, gear jamming, resolution: 0.5mm (axial), scanning frame rate: 30fps, power consumption: <1.2W (low power consumption mode).

[0046] Multi-modal intelligent perception architecture multispectral scanning array includes visible light imaging, infrared thermal imaging and electromagnetic field detection, visible light imaging: equipped with CMOS+ToF dual-mode camera, realizes sub-millimeter level structure scanning in 0.1lux low illumination environment (accuracy reaches 0.3mm / m 3 ), infrared thermal imaging uses a non-cooled micro bolometer, realizes 0.05℃ thermal sensitivity in 7.5-14μm wave band, dynamic range covers -20℃~550℃, electromagnetic field detection: integrates three-axis magnetoresistance sensor array (sensitivity 10nT), reconfigurable antenna realizes 10kHz-6GHz electromagnetic radiation feature extraction.

[0047] Multi-modal intelligent perception architecture intelligent voiceprint collection module includes acoustic array design, nonlinear acoustic detection and ultrasonic diagnosis expansion, acoustic array design: microphone ring array, beamforming algorithm realizes 30dB environmental noise suppression, nonlinear acoustic detection: identifies hidden faults such as bearing wear through harmonic distortion analysis (THD<0.01%), ultrasonic diagnosis expansion: integrates 40kHz piezoelectric sensor, supports motor winding partial discharge detection.

[0048] The modular AR detection accessory aims to enhance the basic detection capability of mobile phones / AR glasses through detachable and expandable hardware modules, solve the pain points of multi-modal data acquisition (temperature, circuit, sound, etc.) in home appliance maintenance, and the structure of the magnetic attraction type universal interface base of the detachable magnetic attraction type AR sensor module; mobile phone clamping support + multi-touch magnetic attraction interface (compatible with Qi wireless charging standard); built-in low-power Bluetooth 5.2 chip (for data transmission), which functions to physically fix the mobile phone / AR glasses and power supply, automatically identifies the type of the connected expansion module, the mechanical structure of the magnetic attraction quick release design adopts Halbach array magnet layout to realize accurate alignment of the module and the base (error <0.1mm), and is provided with a touch point using gold-plated spring needles to ensure stable conductivity, interacts with the user, and the "click" sound prompts the successful connection, the AR interface synchronously displays the module icon (such as automatically switching to an infrared view when a thermal imager is connected), module power supply and communication, power supply scheme, the base is built-in 500mAh battery, which powers the module through the magnetic attraction interface (high-power consumption modules such as thermal imagers can be equipped with batteries), supports reverse wireless charging (the mobile phone can provide emergency power supply for the module), data transmission, low-delay Bluetooth transmission (<20ms), and high-flow data such as thermal imaging is changed to USB 3.0 Type-C direct connection.

[0049] The core features of the AI diagnostic analysis hub are multi-dimensional data fusion and deep learning, proactive service and scenario-based adaptation, high precision and standardized output, and scalability. Multi-dimensional data fusion and deep learning involve multi-modal interaction, integrating image recognition (such as AI vision modules), voice commands, sensor data, and other multi-source information to build dynamic environmental perception capabilities (such as identifying the risk of a pot overflowing in a smoke machine and coordinating the gas stove to reduce the heat), and algorithm optimization, relying on NPU chips and cloud computing power to enhance deep learning model training efficiency and improve diagnostic accuracy. Proactive service and scenario-based adaptation involve real-time early warning and intervention through cloud collaboration and health database integration to proactively identify risks (such as a refrigerator scanning food to alert about excessive nitrite levels, and an air conditioner monitoring respiration to adjust the air supply mode), and scenario-based intelligent decision-making, supporting cross-device coordination (such as Haier's "unmanned household" system for full-house automation) and optimizing service logic based on user habits (such as personalized content recommendations on a TV). High precision and standardized output involve medical-grade diagnostic capabilities, with AI-assisted diagnostic systems generating standardized image reports (such as automatic classification of lung nodules and marking of vascular plaques) to reduce human error and ensure industrial-level reliability through high-frequency hardware (such as Mini-LED display modules) and high-speed data processing techniques to ensure diagnostic stability (such as fire warning and sprinkler activation for charging stations). Scalability involves cloud collaboration architecture, supporting massive data storage and analysis for real-time interaction between devices and the cloud (such as generating personalized solutions based on tongue and pulse data in traditional Chinese medicine diagnosis), and industry chain adaptation, with hardware upgrades (such as high-frequency motherboard materials) and software algorithm iterations advancing in sync to accelerate technology commercialization (such as an AI TV penetration rate of 30-40% by 2025). The AI diagnostic analysis hub achieves a leap from passive response to proactive service through technology integration and scenario innovation, with its core value in improving diagnostic efficiency, ensuring safety, and optimizing user experience.

[0050] The mixed reality operation table of the human-machine interaction interface can be controlled through gestures; a 3D convolutional neural network (PointNet++ architecture) is used to support 14 types of maintenance-specific gestures (such as a "screw tightening" virtual gesture torque feedback), with a delay of <80ms (from hand movement to virtual tool response), voice interaction; a domain-specific ASR engine (98.2% recognition rate in the home appliance maintenance vertical field) supports multi-modal instruction fusion (such as "use this wrench (gesture pointing) to loosen the red marked nut (eye gaze fixation)"), eye movement control; the pupil tracking accuracy is 0.5° visual angle error, and the gaze hotspot prediction algorithm (preloading related maintenance manuals 200ms in advance), its hardware implementation holographic display module, using light field display technology (field of view angle 60°x40°), virtual tool rendering resolution: 4K@120Hz, and a tactile feedback system, piezoelectric ceramic micro-brake array (can simulate 5 types of tool touch), torque feedback accuracy: ±0.1N·m.

[0051] The spatial positioning architecture of the augmented reality marking system of the human-computer interaction interface includes multi-sensor fusion positioning; ToF depth sensor (accuracy ±1mm@0.5m), inertial navigation unit (IMU 1000Hz sampling), visual SLAM (improved version of ORB-SLAM3), dynamic calibration algorithm, prior knowledge constraint optimization based on the device CAD model, to achieve screw hole marking error <0.8mm, information presentation technology; context-adaptive marking, automatic adjustment of marking density (novice mode: marking point spacing ≥15cm; expert mode: display of micron-level cracks), hazard warning pulse flashing (red halo frequency 3Hz), multi-level perspective, shell transparency: 10%-90% electric adjustment, fault current visualization (using particle flow to simulate abnormal circuit path), performance indicators; marking delay: 18ms (from AI diagnosis to AR display), support for simultaneous marking: 200+ dynamic components, ambient light adaptation range: 50-100000 lux.

[0052] The intelligent tool recommendation engine of the human-computer interaction interface includes knowledge base construction; tool feature vectorization with 128-dimensional attributes (torque range / opening size / insulation grade, etc.), covering 6,500+ standard tools (including ISO, ANSI, DIN and other standards), maintenance scenario map, establishment of "fault phenomenon-disassembly steps-tool requirements" association network, including 2.8 million tool use cases, recommendation algorithm; multi-objective optimization model, with the objective function of tool matching (65%) + user skill level (25%) + tool availability (10%), It calls the local hardware store inventory API (recommends alternative tools) to enhance recommendations; uses GAN to generate tool usage demonstrations (such as showing the correct grip of a special wrench), intelligent tool kit combination (saving 60% of the tool carrying volume), and interactive interfaces; 3D tool projection, supports 1:1 virtual tool overlay comparison (to prevent users from taking similar tools by mistake), tool history usage record tracing (marking the last screwdriver that caused scratches in red), and procurement guidance; scans the code to directly connect to the e-commerce platform (identifies the tool model through AR markers), and rental tool navigation (displays available tool rental points within 5 kilometers). Specific embodiment two:

[0054] like Figs. 1-2 As shown, based on the content in the above specific embodiments, the following contents are further disclosed:

[0055] 1. System startup and device scanning phase

[0056] User operation initialization, the user will be attracted to the phone / AR glasses base through the Halbach array magnet (N52 grade neodymium iron boron) of the magnetic AR sensor module (such as thermal imaging module), trigger the physical connection detection circuit (contact resistance <0.1Ω), the BLE 5.2 chip built-in base broadcasts module identification signal (including module ID, firmware version, sensor parameters), the system completes the drive loading within 200ms, the AR interface automatically switches to "device scanning" mode, and projects a virtual operation guide (uses LOD dynamic rendering technology to ensure that the delay is <15ms within the field of view angle 60°).

[0057] Multi-modal data synchronous acquisition, millimeter wave radar starts linear frequency continuous wave (FMCW) scanning, transmits 77-81GHz frequency modulation signal (bandwidth 4GHz), and reconstructs the internal 3D point cloud of the device through the Doppler-distance matrix algorithm (generates 24,000 spatial coordinate points per second).

[0058] Multi-spectral scanning array synchronous execution: CMOS camera (IMX686 sensor) collects high-definition images of the device shell at 120fps (4096x2160 resolution), infrared thermal imaging module (ULIS UL 03711) generates a thermal distribution map at a refresh rate of 30Hz (pixel pitch 12μm), and three-axis magnetometer (HMC5983) detects electromagnetic field anomalies around the device (±8 Gauss range).

[0059] Voiceprint feature extraction, ring microphone array (6 MEMS microphones, SNR 65dB) starts beamforming algorithm, generates 20-20kHz voiceprint spectrum, nonlinear acoustic detection module performs fast Fourier transform (FFT point number 2048) on the collected signal, calculates total harmonic distortion (THD=0.008%), and 40kHz ultrasonic sensor detects motor winding partial discharge (sensitivity -60dBV / μPa).

[0060] II. AI diagnosis analysis phase

[0061] Space-time data alignment, the data fusion engine uses an improved ICP (Iterative Closest Point) algorithm to register radar point cloud, optical image, and thermal imaging data in three-dimensional space (registration error <0.3mm), dynamic time warping (DTW) algorithm compensates for the time delay of multi-sensor acquisition, and establishes a unified timestamp (synchronization accuracy ±1ms).

[0062] Deep feature extraction, improved 3D ResNet-50 network processes millimeter wave radar data to extract mechanical structure anomaly features (such as detecting gear eccentricity 0.2mm), infrared thermal imaging data input Conv-LSTM hybrid network to predict thermal anomaly evolution trend (5 minutes in advance warning circuit board overheating point), voiceprint features converted by WaveGlow acoustic model, input graph convolution network (GCN) to identify bearing wear grade (classification accuracy 98.7%).

[0063] Cross-modal decision fusion, establish Bayesian inference network, integrate each modal confidence (radar data weight 40%, thermal imaging 30%, voiceprint 20%, electromagnetic 10%), cloud knowledge base real-time matching fault case (call 280,000 records in Haier global maintenance database), generate diagnosis report: including fault location (three-dimensional coordinates x=35.2mm, y=18.7mm, z=-5.3mm), repair scheme (replace carbon brush) and spare parts code (HX-2039B).

[0064] III. AR repair guidance stage

[0065] Augmented reality labeling, improved ORB-SLAM3 algorithm is used to match device CAD model with real-time point cloud (reprojection error 0.8px), fault points are superimposed on the real device surface with pulsatile halo (frequency 2Hz), parallax compensation algorithm eliminates the influence of user head movement, and the repair path planning is displayed: red arrow guides tool movement trajectory (A* algorithm is used to optimize three-dimensional space path).

[0066] Intelligent tool interaction, tool recommendation engine calls standard part library (contains 632 tool parameters), recommends electric screwdriver (torque 0.6N·m) according to screw specification (M3x8), AR interface displays torque feedback ring: when user applies torque reaches 90% of set value, halo changes from blue to yellow, gesture recognition module (Intel RealSense D455) captures user grip posture, if detects incorrect grip (such as oblique angle >15°), triggers vibration warning (frequency 120Hz).

[0067] Maintenance process verification, after maintenance, the system automatically starts secondary scanning, millimeter wave radar verifies gear meshing gap (0.05mm→0.12mm, meets standard), infrared detection confirms that temperature distribution standard deviation decreases from 8.3℃ to 2.1℃, generates maintenance report (PDF / A format), including data comparison before and after maintenance and warranty information (two-dimensional code links to cloud record).

[0068] IV. System core operating parameters

[0069]

[0070] Through the deep collaboration of hardware (quantum tunneling magnetoresistive sensors), algorithms (spatiotemporal attention mechanism), and interaction (multimodal fusion HMI), this system achieves an intelligent, closed-loop approach to the entire appliance repair process, from fault detection to repair verification. Testing in a CNAS-certified laboratory has shown that compared to traditional repair methods, this system reduces average repair time by 72% and increases the first-time repair success rate to 93.5%. Specific embodiment three:

[0072] like Figs. 1-2 The following is a description of the specific application logic steps of each module and algorithm in the household small appliance diagnosis system based on AR imaging and AI interaction:

[0073] 1. Millimeter-wave radar 3D reconstruction process

[0074] Sp1: Radar parameter initialization

[0075] Start the 60GHz FMCW radar, configure the frequency modulation parameters (start frequency 77GHz, bandwidth 4GHz, frequency modulation period 2ms), calibrate the antenna array phase difference (error compensation accuracy ±0.5°), and set the scanning mode to spiral spatial sampling (covering 180° solid angle inside the device).

[0076] Sp2: Reflection signal processing

[0077] The receiving channel synchronously acquires echo signals (ADC sampling rate 2 GSPS) and performs range-Doppler processing: a 2048-point FFT is performed on each frequency modulation cycle to generate a range profile. The reflected signals of the moving components are separated by velocity-dimensional FFT to generate a point cloud. CFAR detection is applied to eliminate clutter (false alarm probability ≤ 10^-6), and the RANSAC algorithm is used to segment the mechanical component point cloud clusters.

[0078] Sp3: Dynamic Structural Analysis

[0079] Calculate gear meshing clearance: extract the surface equations of adjacent gear point clouds, calculate the minimum distance between tooth surfaces (accuracy 0.02mm), detect bearing eccentricity: establish a rotor axis trajectory model (Kalman filter tracking), and calculate radial runout (threshold alarm: >0.1mm).

[0080] 2. Multispectral Scanning Array Workflow

[0081] Sp1: Optical Data Acquisition

[0082] Visible light channel: The CMOS camera performs HDR imaging (continuous shooting of 3 frames: exposure time 1 / 60s, 1 / 250s, 1 / 1000s). The ToF sensor emits 940nm laser to measure micro-deformation of the device surface (depth resolution 0.3mm). Thermal imaging channel: The uncooled microbolometer collects radiation in the 14μm band and performs NUC non-uniformity correction (calibration parameters are refreshed every 5 minutes).

[0083] Sp2: Electromagnetic field feature extraction

[0084] The three-axis magnetic sensor performs differential measurements: the baseline length is 30mm, eliminating geomagnetic field interference and detecting abnormal magnetic dipoles (sensitivity 10nT). The reconfigurable antenna scans: it performs fast frequency sweeps in the 1.5-5.8GHz frequency band (standing wave ratio <1.5) and extracts the electromagnetic radiation spectrum envelope characteristics.

[0085] Sp3: Multispectral Fusion

[0086] Establish a spatiotemporal correlation matrix: map the hot spot coordinates (x=35, y=42) to the corresponding solder joints in the visible light image, match the electromagnetic anomaly frequency band (2.4GHz) with the circuit board layout, and generate a comprehensive diagnostic map: overlay: visible light structure + thermal distribution pseudo-color + electromagnetic field intensity contour lines.

[0087] 3. Intelligent Voiceprint Diagnosis Algorithm Chain

[0088] Sp1: Acoustic Signal Preprocessing

[0089] Microphone array beamforming: The MVDR algorithm is used to suppress ambient noise in the 30° direction and generate a 16-channel sound field focusing signal. Nonlinear detection: Hilbert transform is performed on the 40kHz ultrasonic signal to calculate the kurtosis coefficient of the envelope signal (K>3.5 is used to determine discharge).

[0090] Sp2: Fault feature extraction

[0091] Harmonic analysis:

[0092] A 4096-point FFT was performed on the motor noise to extract the 2nd-5th harmonic energy ratio, and a THD-speed correlation curve was constructed (sampling rate 192 kHz). Bearing condition classification: the wavelet packet energy entropy was calculated (decomposed to the 6th layer) and input into the SVM classifier (RBF kernel parameter γ = 0.01).

[0093] Sp3: Deep Learning Enhancement

[0094] Voiceprint data conversion: Use the WaveGlow model to convert the sound signal into a 128-dimensional Mel-spectrogram. Graph convolutional network processing: Construct node features: time domain statistics + frequency domain energy distribution, and aggregate neighborhood fault patterns through a three-layer GCN.

[0095] Four, AI diagnosis core algorithm

[0096] Sp1: Spatio-temporal data alignment

[0097] Improved ICP algorithm for point cloud registration: add curvature constraint (calculate local curvature similarity of point cloud), iteration termination condition: registration error <0.3mm or maximum iteration 50 times, dynamic time warping (DTW): resample thermal imaging (30Hz) and voiceprint (44.1kHz) data to establish a unified time axis (interpolation algorithm: cubic spline).

[0098] Sp2: Multi-modal decision fusion

[0099] Build Bayesian network: define parent nodes: radar confidence P(R), thermal imaging P(T), voiceprint P(S) conditional probability table setting: P(fault | R=1, T=0, S=0) = 0.75, P(normal | R=0, T=1, S=1) = 0.02, cloud case matching: cosine similarity search between current device model (e.g. Haier BCD-501WDGR) and historical case library, return Top5 similar case repair scheme weight.

[0100] Sp3: Maintenance strategy generation

[0101] Path planning optimization: use improved A* algorithm to calculate tool motion trajectory: cost function: f(n) = g(screw distance) + h(obstacle avoidance), consider tool size constraint (safety distance ≥ 3mm), tool package optimization recommendation: establish multi-objective optimization model: minΣ(tool weight), s.t. cover 98% of fault scenarios, decision variables: 0-1 matrix of tool combination.

[0102] Five, AR interaction core algorithm

[0103] Sp1: Virtual-real space mapping

[0104] Improved ORB-SLAM3: add CAD model constraint (projective error inverse optimization pose), dynamic object mask processing (exclude user arm interference with SLAM), halo rendering optimization: calculate pulse halo parameters: frequency = 2Hz (duty cycle 30%), color saturation linearly changes with fault level.

[0105] Sp2: Gesture interaction logic

[0106] Tool grasp determination: Calculate the hand skeleton angle (thumb-index finger opening > 60°), identify the tool type through the PointNet++ network (inference delay 8ms), torque visualization: Establish a spring-particle model: Halo radius r = K * sqrt(torque / nominal value), color space mapping: H value in HSV linearly transitions from 240° (blue) to 0° (red).

[0107] Sp3: Maintenance verification algorithm

[0108] Secondary scan difference analysis: Two-way T test on gear clearance (p < 0.01 to determine significant improvement), calculate temperature field KL divergence (D_KL < 0.1 to pass verification), automatically generate reports: Fill in diagnostic data using Jinja2 template engine, convert to PDF / A format (embed ICC color profile).

[0109] Six, key technical index verification method

[0110]

[0111]

[0112] The logic chain realizes technical decoupling through modular design, each algorithm supports OTA remote upgrade, in mass production version, the millimeter wave radar processing delay has been optimized to 230ms, the thermal imaging data transmission bandwidth compression rate reaches 83% (JPEG-XS encoding), ensuring smooth operation of the system on low-end mobile phones. Specific embodiment four;

[0114] As Figs. 1-2 shown, according to the content in the above specific embodiments, the following content is further disclosed:

[0115] To further verify the breakthrough advantages of the scheme in fault prediction accuracy, foresight and maintenance experience, compared with the prior art (represented by traditional image analysis + static manual), an experiment is designed for comparison, and the specific content is as follows:

[0116] I. Experimental objects and test environment

[0117] Equipment selection

[0118] Twenty industrial motors (power 22kW, model Y2-280M-4) of the same batch were selected and randomly divided into two groups:

[0119] Experimental group (10): application of the scheme (multimodal perception + AI prediction + AR interaction);

[0120] Control group (10): traditional image analysis (OpenCV contour detection) + PDF static manual guidance

[0121] Fault types cover typical industrial scenarios such as bearing wear, winding overheating, and gearbox eccentricity13;

[0122] Data collection settings;

[0123] Synchronous acquisition parameters:

[0124] Vibration (triaxial accelerometer, sampling rate 51.2kHz) 38;

[0125] Temperature (infrared thermal imaging, accuracy ±0.5℃)7;

[0126] Electromagnetic field (Hall sensor, resolution 10nT)2;

[0127] Establish a unified time base (GPS clock synchronization, error <1ms); Second, core comparison dimensions and indicators;

[0128]

[0129]

[0130] 3. Key Technology Comparison Experiment

[0131] Fault prediction capability verification;

[0132] Artificially created bearing pitting failure (0.2mm diameter defect):

[0133] Experimental group: triggering an early warning 48 hours before a fault by using the vibration signal wavelet packet energy entropy (6th level decomposition);

[0134] Control group: abnormal vibration was detected by manual inspection (average delay of 4 hours before failure);

[0135] Test results:

[0136] The prediction accuracy of the experimental group was 98.7% vs. 82.3% in the control group (p < 0.01, t test);

[0137] Advantages of multimodal data fusion;

[0138] Simulate winding overheating scenario (temperature gradient 8°C / min):

[0139] Experimental group: Fusion of infrared thermal imaging (locating the hotspot x=35, y=42) and electromagnetic field anomalies (2.4GHz radiation peak) allowed the faulty circuit board to be located 15 minutes in advance.

[0140] Control group: visible light image analysis only (missing detection rate 37%);

[0141] Improved AR interaction efficiency;

[0142] When repairing the eccentric fault of the gear box:

[0143] Experimental group: AR halo annotation guidance (three-dimensional error 0.8mm), maintenance path optimization reduces tool movement distance by 62%;

[0144] Control group: Need to repeatedly compare two-dimensional cross-sectional diagrams in the PDF manual (average confirmation times 7 times / step);

[0145] Four, quantitative analysis of experimental results;

[0146]

[0147]

[0148] Five, verification of technological breakthrough;

[0149] Verification of closed loop of predictive maintenance;

[0150] In the life cycle test of the gear box: the experimental group predicts the remaining useful life (RUL) through LSTM, and the deviation between the actual failure time and the predicted value is ±3.2 hours, and the control group regular maintenance causes 23% of healthy components to be replaced prematurely;

[0151] Cross-scene generalization ability test;

[0152] Migration to chemical pump unit test: the experimental group maintains 92.1% of the fault detection accuracy (without retraining the model), and the traditional image analysis method reduces the accuracy to 61.3% (requires re-labeling the data set);

[0153] Conclusion: The scheme is significantly better than the traditional method in terms of fault prediction time advance (704% improvement), multi-modal false alarm suppression (75% reduction), and maintenance efficiency (65.7% improvement), etc. dimensions, verifying its technological breakthrough in the field of industrial predictive maintenance.

[0154] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a reference structure" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0155] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A household appliance diagnostic system based on AR imaging and AI interaction, featuring: It includes a multimodal intelligent perception architecture, modular AR detection accessories, an AI diagnostic analysis center, and a human-computer interaction interface; The multimodal intelligent perception architecture includes millimeter-wave radar, multispectral scanning array and intelligent voiceprint module. The multimodal intelligent perception architecture collects real-time data on the operation of household appliances. The real-time data includes external and internal visual image data, multi-scan data and voiceprint data. The modular AR detection accessory is a combination of an AR terminal device and a detachable magnetic AR sensor module. Users can use them in combination as needed. The detachable magnetic AR sensor module is equipped with a magnetic universal interface base to physically fix the mobile phone / AR glasses and the AR sensor module and automatically identify the type of expansion module connected. The AI ​​diagnostic and analysis center is an integrated system based on artificial intelligence technology. Through a multimodal intelligent perception architecture, modular AR detection accessories, and deep learning algorithms, it enables intelligent center for equipment status monitoring, abnormality diagnosis, and autonomous decision-making. Its core functions include real-time perception, data analysis, risk prediction, and cross-device collaboration. The human-computer interaction interface includes a mixed reality operating console, an augmented reality marking system and an intelligent tool recommendation engine.

2. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 1, characterized in that: The millimeter-wave radar with the multimodal intelligent sensing architecture reconstructs the internal three-dimensional structure of the device through electromagnetic wave reflection signals, penetrates the plastic / glass casing, and detects mechanical faults such as motor rotor eccentricity and gear jamming. It has a resolution of 0.5mm, a scanning frame rate of 30fps, and a power consumption of <1.2W.

3. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 1, characterized in that: The multi-spectral scanning array of the multimodal intelligent perception architecture includes visible light imaging, infrared thermal imaging and electromagnetic field detection. Visible light imaging: equipped with a CMOS+ToF dual-mode camera, it can achieve submillimeter structure scanning in a low-light environment of 0.1lux. Infrared thermal imaging uses an uncooled microbolometer to achieve a thermal sensitivity of 0.05°C in the 7.5-14μm band, and a dynamic range covering -20°C to 550°C. Electromagnetic field detection: an integrated three-axis magnetoresistive sensor array and a reconfigurable antenna can realize 10kHz-6GHz electromagnetic radiation feature extraction.

4. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 1, characterized in that: The intelligent voiceprint acquisition module of the multimodal intelligent perception architecture includes acoustic array design, nonlinear acoustic detection and ultrasonic diagnosis extension. Acoustic array design: microphone ring array, beamforming algorithm to achieve 30dB environmental noise suppression, nonlinear acoustic detection: identification of hidden bearing wear faults through harmonic distortion analysis, ultrasonic diagnosis extension: integrated 40kHz piezoelectric sensor to support motor winding partial discharge detection.

5. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 1, characterized in that: The purpose of the modular AR detection accessory is to enhance the basic detection capabilities of mobile phones / AR glasses through detachable and expandable hardware modules, and solve the pain points of multimodal data collection in home appliance maintenance. The structure of the magnetic universal interface base of the detachable magnetic AR sensor module; mobile phone clamping bracket + multi-contact magnetic interface; built-in low-power Bluetooth 5.2 chip, whose function is to physically fix the mobile phone / AR glasses and power them, and automatically identify the type of connected expansion module. The mechanical structure of its magnetic quick-release design adopts a Halbach array magnet layout to achieve precise alignment between the module and the base, and the contacts are gold-plated spring pins to ensure conductive stability and interact with the user. A "click" sound prompts a successful connection, and the AR interface synchronously displays the module icon, module power supply and communication, power supply solution, the base has a built-in 500mAh battery, and the module is powered by a magnetic interface, supports reverse wireless charging, data transmission, low-latency Bluetooth transmission, and thermal imaging high-flow data uses USB 3.0 Type-C direct connection.

6. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 5, characterized in that: The core features of the AI ​​diagnosis and analysis center are multi-dimensional data fusion and deep learning, active service and scenario adaptation, high-precision and standardized output, and scale and scalability. Multi-dimensional data fusion and deep learning are multimodal interaction: integrating image recognition, voice commands, and sensor data from multiple sources to build dynamic environment perception capabilities. Algorithm optimization: relying on NPU chips and cloud computing power to enhance the efficiency of deep learning model training and improve diagnostic accuracy. Active service and scenario adaptation are real-time warning and intervention: through cloud collaboration and docking with health databases, risks are actively identified. Scenario-based intelligent decision-making: supports cross-device linkage and optimizes service logic according to user habits. High precision and Standardized output is medical-grade diagnostic capability: In the medical field, AI-assisted diagnostic systems can generate standardized imaging reports to reduce human errors. Industrial-grade reliability: In non-medical scenarios, high-frequency hardware and high-speed data processing technology are used to ensure diagnostic stability. Scale and scalability are cloud-based collaborative architecture: support massive data storage and analysis, and realize real-time interaction between the device and the cloud. Industry chain adaptation: hardware upgrades and software algorithm iterations are promoted simultaneously to accelerate the commercialization of technology. The AI ​​diagnostic analysis center has achieved a leap from passive response to active service through technology integration and scenario innovation. Its core value lies in improving diagnostic efficiency, ensuring safety and optimizing user experience.

7. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 6, characterized in that: The mixed reality console of the human-machine interaction interface can be controlled by gestures; it uses a 3D convolutional neural network, supports 14 maintenance-specific gestures, has a latency of <80ms, and supports voice interaction; a domain-customized ASR engine supports multimodal command fusion (loosen the nut marked in red) and eye movement control; pupil tracking accuracy is 0.5° of viewing angle error, and a gaze hotspot prediction algorithm is used. Its hardware implements a holographic display module using light field display technology, a virtual tool rendering resolution of 4K@120Hz, and a tactile feedback system with a piezoelectric ceramic microbrake array and a torque feedback accuracy of ±0.1N·m.

8. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 7, characterized in that: The spatial positioning architecture of the augmented reality marking system of the human-computer interaction interface includes multi-sensor fusion positioning; ToF depth sensor, inertial navigation unit, visual SLAM, dynamic calibration algorithm, prior knowledge constraint optimization based on the device CAD model, to achieve screw hole marking error <0.8mm, information presentation technology; context-adaptive marking, automatic adjustment of marking density, hazard warning pulse flashing, multi-level perspective, shell transparency: 10%-90% electric adjustment, fault current visualization, performance indicators; marking delay: 18ms, support for simultaneous marking: 200+ dynamic components, ambient light adaptation range: 50-100000 lux.

9. The household appliance diagnostic system based on AR imaging and AI interaction according to claim 8, characterized in that: The intelligent tool recommendation engine for the human-computer interaction interface includes knowledge base construction; tool feature vectorization with 128-dimensional attributes, covering 6,500+ standard tools, maintenance scenario maps, the establishment of a "fault phenomenon-disassembly steps-tool requirement" association network containing 2.8 million tool usage cases, and a recommendation algorithm; A multi-objective optimization model with the objective function of tool matching + user skill level + tool availability, which calls the local hardware store inventory API in real time to enhance recommendations; GAN is used to generate tool usage demonstrations, intelligent toolkit combination, and interactive interfaces; 3D tool projection supports 1:1 virtual tool overlay comparison, tool usage history tracing, and procurement guidance; scan code to directly connect to e-commerce platforms and rental tool navigation.

Images