Household equipment intelligent fault early warning and after-sales optimization system based on multi-sensor fusion and wireless transmission

Abstract

The invention relates to a household equipment intelligent fault early warning and after-sales optimization system based on multi-sensor fusion and wireless transmission, which comprises a sensing layer, a transmission layer, a cloud intelligent layer and an application layer, and is characterized in that the sensing layer comprises a heterogeneous sensor cluster and an edge computing node, and the heterogeneous sensor cluster deploys a multi-dimensional sensor; and mechanical, electrical and environmental parameters are covered. Through a physical model and data-driven double-engine architecture, a flexible sensing technology and a decentralized service network, full-chain intelligent reconstruction from fault early warning to after-sales service is realized, and analyzed data is timely and stably transmitted back to an after-sales service center or a specified monitoring platform. Through deep analysis and processing of the sensor data, a scientific fault prediction model can be established, and the model can be utilized to accurately predict possible faults of the equipment and perform early warning before the faults occur, so that a user can know potential problems of the equipment in advance and take corresponding measures in time.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of household appliances, and particularly to an intelligent fault warning and after-sales optimization system for household appliances based on multi-sensor fusion and wireless transmission. Background Art

[0002] Household appliances mainly refer to various electrical and electronic appliances used in homes and similar places. They are also called civil appliances and household appliances. Household appliances have liberated people from heavy, trivial, and time-consuming housework, created a more comfortable, beautiful, and healthy living and working environment for humans, provided rich and colorful cultural and entertainment conditions, and have become necessities for modern family life.

[0003] With the continuous progress of technology, household appliances have become more and more intelligent, and intelligent devices are also used for detection and warning during the repair process. For example, a method and system for intelligent fault diagnosis and repair guidance of household appliances proposed in Chinese Patent No. 202411713178.0 includes: receiving a fault code sent by a household appliance; sending the fault code to a maintenance end so that the maintenance end can determine the fault type according to the fault code, and sending a repair plan to the terminal device of a preset first user when it is determined that the first user has the ability to solve the fault type by himself.

[0004] However, the existing technology only reports the fault code after a fault occurs. However, there will also be some abnormal data when the household appliance has not yet failed. If these abnormal data can be analyzed and predicted in a timely manner, the occurrence of household appliance faults can be avoided. Therefore, an intelligent fault warning and after-sales optimization system for household appliances based on multi-sensor fusion and wireless transmission is proposed to solve the above problems. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent fault warning and after-sales optimization system for household appliances based on multi-sensor fusion and wireless transmission, which has the advantages of timely obtaining abnormal data of household appliances for prediction and avoiding the occurrence of household appliance faults, and solves the problem that the existing technology only reports the fault code after a fault occurs. However, there will also be some abnormal data when the household appliance has not yet failed. If these abnormal data can be analyzed and predicted in a timely manner, the occurrence of household appliance faults can be avoided.

[0006] To achieve the above object, the present invention provides the following technical solution: An intelligent fault warning and after-sales optimization system for household appliances based on multi-sensor fusion and wireless transmission, including a sensing layer, a transmission layer, a cloud intelligent layer, and an application layer:

[0007] The perception layer includes heterogeneous sensor clusters and edge computing nodes. The heterogeneous sensor clusters deploy multi-dimensional sensors, covering mechanical, electrical, and environmental parameters. Self-powered flexible sensor patches are used and directly attached to the device surface. The edge computing nodes are built with lightweight AI chips in each device for local feature extraction and preliminary anomaly detection;

[0008] The transmission layer adopts a hybrid wireless protocol and introduces a dynamic protocol switching algorithm to automatically select the optimal transmission path according to data priority and network status;

[0009] The cloud intelligent layer includes a multi-source heterogeneous data fusion engine, a digital twin simulation module, a fault detection algorithm, and a predictive maintenance module. The multi-source heterogeneous data fusion engine uses a spatio-temporal attention mechanism + graph neural network to mine the associated fault patterns between devices. The digital twin simulation module constructs a physical model of the device and a virtual mirror driven by real-time data to achieve fault simulation and prediction. The fault detection algorithm is based on the Transformer architecture, fusing time-domain vibration signals, frequency-domain acoustic fingerprint features, and spatial temperature distributions, and adapting to different brand devices through few-shot learning to solve the cold start problem. The predictive maintenance module fuses the physical degradation model and the LSTM prediction results to improve the long-term prediction credibility, and uses SHAP values to interpret the model, locate abnormal sensors, and trace back the fault source;

[0010] The application layer includes a visual warning interface and an intelligent after-sales optimization system. The visual warning interface uses the web to build an intelligent warning front-end interface. The intelligent after-sales optimization system includes an intelligent dispatching system and spare parts supply chain optimization. The intelligent dispatching system constructs a multi-objective optimization model based on fault level, geographical location, spare parts inventory, and technician skill tags, and uses a reinforcement learning dynamic scheduling algorithm to respond to sudden faults in real time. The spare parts supply chain optimization combines the fault prediction results with the regional device distribution to establish a spatio-temporal demand prediction model to achieve pre-positioned warehousing of spare parts.

[0011] Furthermore, the heterogeneous sensor clusters emit piezoelectric-thermoelectric composite self-powered patches with a thickness < 1mm and a bending radius < 5mm, realizing the three-in-one sensing of vibration ±50g, temperature -20 - 150°C, and acoustic fingerprint 20Hz - 20kHz. The specific steps for the heterogeneous sensor clusters to construct a multi-modal data fusion architecture are as follows:

[0012] 1) Preprocessing layer: Achieve μs-level synchronization through hardware-level PTP, and establish a sensor space coordinate system through a laser positioning system

[0013] 2) Feature-level fusion: The vibration signal uses wavelet packet decomposition to extract energy features, the acoustic fingerprint signal uses a Gammatone filter bank to generate a cochleagram, construct a thermoelectric coupling state equation, and detect hidden faults such as insulation aging through residual analysis;

[0014] 3) Decision-level fusion: probabilistically synthesize the diagnostic results of each sensor subsystem, and optimize the confidence weights in combination with the equipment maintenance history database.

[0015] Furthermore, the main control chip of the edge computing node adopts a custom AI chip C910 with a RISC-V architecture, supporting 1 TOPS computing power, integrating a neural network processing unit and a digital signal processor, meeting the multi-modal data processing requirements, and is paired with a field programmable gate array for real-time signal processing to improve the efficiency of vibration and voiceprint analysis;

[0016] Sensor interface. Supports the access of 12-channel sensors, including: analog input (vibration, temperature), digital interface (I2S for voiceprint acquisition), environmental sensors (humidity, air pressure via SPI), integrated with an energy harvesting module piezoelectric material + solar thin film, supporting wireless power supply for sensor patches;

[0017] Communication module: dual-mode wireless transmission: Wi-Fi6 and LoRa integrated, supporting dynamic switching.

[0018] Furthermore, the data processing flow of the edge computing node is specifically as follows:

[0019] 1) Preprocessing of raw signals

[0020] Vibration signal: Kalman filter for noise reduction + STFT time-frequency analysis;

[0021] Voiceprint signal: MFCC feature extraction, accelerated by FPGA;

[0022] Temperature / current: moving window mean filtering;

[0023] 2) Lightweight anomaly detection

[0024] Deploy a 1D depthwise separable convolutional network with model parameters < 100KB

[0025] 3) Feature compression and transmission

[0026] For the confirmed abnormal data, extract the key feature vectors, i.e., vibration peak value and voiceprint resonance frequency;

[0027] 4) Dynamic model update

[0028] When the cloud pushes a new fault mode, update the edge model through knowledge distillation;

[0029] When the edge computing node device is started for the first time, automatically calibrate the sensor position deviation through the voiceprint excitation signal, ensure stable operation beside high-power devices through PCB hierarchical layout, reserve SWD / JTAG interfaces for firmware burning, and broadcast diagnostic information through BLE to avoid disassembly maintenance.

[0030] Furthermore, the multi-source heterogeneous data fusion engine is based on multi-modal feature extraction and device mechanism modeling of deep learning, realizes adaptive fusion through a dynamic weight gating network, constructs a knowledge graph containing more than 3,000 fault cases, uses the TransE algorithm for embedding representation, and a graph-driven feature selection strategy.

[0031] Furthermore, the digital twin simulation module conducts real-time data acquisition and preprocessing through a physical entity interface, uses a multi-scale model library for device mechanism models, data-driven models, and hybrid models, conducts real-time solution and visualization through a simulation engine, manages full-life cycle data through a digital thread, and conducts real-time control and feedback through a virtual-real interaction interface.

[0032] Furthermore, the fault detection algorithm includes:

[0033] Signal preprocessing layer: data cleaning and feature extraction;

[0034] Single-modal detection layer: independent analysis of each sensor;

[0035] Multi-modal fusion layer: cross-sensor feature interaction;

[0036] Decision output layer: fault classification and location;

[0037] The signal preprocessing layer processes data as follows:

[0038] Processing method for vibration signals: adaptive wavelet packet decomposition;

[0039] Processing method for voiceprint signals: Mel spectrum extraction;

[0040] Processing method for temperature signals: sliding window filtering;

[0041] Processing method for current signals: harmonic analysis;

[0042] In the single-modal detection layer, based on deep feature learning with physical constraints, the DResNet + attention mechanism framework is used for vibration signal detection, and the CNN + GRU framework is used for voiceprint signal detection.

[0043] The multi-modal fusion layer is based on cross-modal interaction of graph neural networks;

[0044] The decision output layer is based on interpretable diagnosis with attention mechanism, and uses SHAP value for feature contribution degree analysis.

[0045] Furthermore, the intelligent after-sales optimization system includes the following:

[0046] Intelligent diagnosis center: remote fault diagnosis and root cause analysis;

[0047] Service Scheduling Engine: Intelligent Order Assignment and Resource Optimization;

[0048] Customer Interaction Platform: Omnichannel Customer Service and Satisfaction Management;

[0049] Spare Parts Supply Chain: Intelligent Prediction and Dynamic Inventory Management;

[0050] Quality Feedback Closed Loop: Fault Data Transmission Back and Product Improvement.

[0051] Furthermore, the core module for optimizing the spare parts supply chain includes the following:

[0052] Demand Forecasting Engine: Spare parts demand forecasting based on equipment status;

[0053] Inventory Optimization Model: Multi-objective inventory strategy optimization;

[0054] Logistics Scheduling System: Intelligent warehousing and distribution route planning;

[0055] Digital Twin Platform: Visualization and simulation of the entire supply chain process;

[0056] Blockchain Traceability System: Traceability of the entire life cycle of spare parts.

[0057] Compared with the prior art, the technical solution of this application has the following beneficial effects:

[0058] 1. Through the dual-engine architecture of physical model and data-driven, flexible sensing technology, and decentralized service network, the present invention realizes the intelligent reconstruction of the entire chain from fault warning to after-sales service. With the help of advanced wireless communication technology, the analyzed data is transmitted back to the after-sales service center or the designated monitoring platform in a timely and stable manner. By deeply analyzing and processing these sensor data, a scientific fault prediction model can be established. Using this model, potential faults of the equipment can be accurately predicted, and early warnings can be given before the faults occur, enabling users to be aware of potential problems of the equipment in advance and take corresponding measures in a timely manner.

[0059] 2. When a fault occurs in the equipment of the present invention, the system can quickly capture abnormal data and transmit the fault information back in the first time, enabling the after-sales team to respond quickly. This can not only effectively reduce the damage degree caused by the fault of the equipment, but also greatly shorten the repair time, improve the quality and efficiency of after-sales service, and provide users with a more high-quality and convenient use experience, with significant innovative and practical value. Brief Description of the Drawings

[0060] Figure 1 It is a schematic structural diagram of the intelligent fault warning and after-sales optimization system for household equipment based on multi-sensor fusion and wireless transmission of the present invention. Detailed Embodiment

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Please refer to Figure 1 , the intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission in this embodiment includes a perception layer, a transmission layer, a cloud intelligent layer, and an application layer:

[0063] The perception layer includes a heterogeneous sensor cluster and an edge computing node. The heterogeneous sensor cluster deploys multi-dimensional sensors to cover mechanical, electrical, and environmental parameters, and uses self-powered flexible sensor patches that are directly attached to the device surface. The edge computing node installs a lightweight AI chip in each device for local feature extraction and preliminary anomaly detection;

[0064] The transmission layer adopts a hybrid wireless protocol and introduces a dynamic protocol switching algorithm to automatically select the optimal transmission path according to data priority and network status;

[0065] The cloud intelligent layer includes a multi-source heterogeneous data fusion engine, a digital twin simulation module, a fault detection algorithm, and a predictive maintenance module. The multi-source heterogeneous data fusion engine uses a spatio-temporal attention mechanism + graph neural network to mine the associated fault patterns between devices. The digital twin simulation module constructs a physical model of the device and a virtual mirror driven by real-time data to achieve fault simulation and prediction. The fault detection algorithm is based on the Transformer architecture, which fuses time-domain vibration signals, frequency-domain acoustic fingerprint features, and spatial temperature distributions, and adapts to different brand devices through few-shot learning to solve the cold start problem. The predictive maintenance module fuses the physical degradation model and the LSTM prediction results to improve the long-term prediction credibility, and uses the SHAP value to interpret the model to locate abnormal sensors and trace back the fault source;

[0066] The application layer includes a visualization warning interface and an intelligent after-sales optimization system. The visualization warning interface uses web to build an intelligent warning front-end interface. The intelligent after-sales optimization system includes an intelligent dispatching system and spare parts supply chain optimization. The intelligent dispatching system constructs a multi-objective optimization model based on fault level, geographical location, spare parts inventory, and technician skill tags, and uses a reinforcement learning dynamic scheduling algorithm to respond to sudden faults in real time. The spare parts supply chain optimization combines the fault prediction results with the regional device distribution to establish a spatio-temporal demand prediction model to achieve pre-positioned warehousing of spare parts.

[0067] In this embodiment, the core sensor types and parameters of the heterogeneous sensor cluster are specifically as follows:

[0068]

[0069] In this embodiment, the data processing flow of the edge computing node is as follows:

[0070] 1) Preprocessing of the original signal

[0071] Vibration signal: Kalman filter noise reduction + STFT time-frequency analysis;

[0072] Voiceprint signal: MFCC feature extraction, accelerated by FPGA;

[0073] Temperature / current: Moving window mean filtering;

[0074] 2) Lightweight anomaly detection

[0075] Deploy a 1D depthwise separable convolutional network with model parameters < 100KB

[0076] 3) Feature compression and transmission

[0077] For the data confirmed to be abnormal, extract the key feature vectors, vibration peak value and voiceprint resonance frequency;

[0078] 4) Dynamic model update

[0079] When the cloud pushes a new fault mode, update the edge model through knowledge distillation.

[0080] In this embodiment, the multi-source heterogeneous data fusion engine is based on multi-modal feature extraction of deep learning, device mechanism modeling, realizes adaptive fusion through a dynamic weight gating network, constructs a knowledge graph containing more than 3,000 fault cases, uses the TransE algorithm for embedding representation, and the feature selection strategy driven by the graph. The feature extraction technology stack is as follows:

[0081] Data type Feature extraction method Dimension compression technology Vibration signal Wavelet packet energy spectrum (16 - layer decomposition) PCA + T - SNE Voiceprint data Gammatone time - frequency map + CNN Deep auto - encoder Temperature field Graph convolutional network (GCN) Graph pooling layer Current waveform Harmonic distortion analysis (THD calculation) Empirical mode decomposition

[0082] In this embodiment, the digital twin simulation module performs real-time data acquisition and preprocessing through the physical entity interface, uses a multi-scale model library for device mechanism models, data-driven models, and hybrid models, performs real-time solution and visualization through the simulation engine, manages the full life cycle data through the digital thread, and performs real-time control and feedback through the virtual-real interaction interface. The core components are as follows:

[0083]

[0084]

[0085] In this embodiment, the signal preprocessing layer of the fault detection algorithm includes the following technologies:

[0086] Signal type Processing method Technical details Vibration signal Adaptive wavelet packet decomposition 16 - layer decomposition, optimal basis selection Voiceprint signal Mel - spectrum extraction 128 - dimensional MFCC features Temperature signal Sliding window filtering Window size adaptively changes Current signal Harmonic analysis Calculate THD (total harmonic distortion rate)

[0087] In summary, through the dual-engine architecture of physical model and data-driven, flexible sensing technology, and decentralized service network, the present invention realizes the full-chain intelligent reconstruction from fault warning to after-sales service. With the help of advanced wireless communication technology, the analyzed data is timely and stably transmitted back to the after-sales service center or the designated monitoring platform. By deeply analyzing and processing these sensor data, a scientific fault prediction model can be established. Using this model, the possible faults of the equipment can be accurately predicted, and early warnings can be given before the faults occur, enabling users to be aware of potential problems of the equipment in advance and take corresponding measures in a timely manner. When a fault occurs in the equipment, the system can quickly capture abnormal data and transmit the fault information back in the first time, enabling the after-sales team to respond quickly. This can not only effectively reduce the damage degree caused by equipment faults, but also greatly shorten the repair time, improve the quality and efficiency of after-sales service, and provide users with a more high-quality and convenient usage experience, having significant innovative and practical value.

[0088] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0089] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission, including a sensing layer, a transmission layer, a cloud intelligent layer, and an application layer, characterized in that: The sensing layer includes a heterogeneous sensor cluster and edge computing nodes. The heterogeneous sensor cluster deploys multi-dimensional sensors, covering mechanical, electrical, and environmental parameters, and uses self-powered flexible sensor patches, which are directly attached to the device surface. The edge computing nodes are built-in with lightweight AI chips in each device, used for local feature extraction and preliminary anomaly detection; The transmission layer uses a hybrid wireless protocol, introduces a dynamic protocol switching algorithm, and automatically selects the optimal transmission path according to data priority and network status; The cloud intelligent layer includes a multi-source heterogeneous data fusion engine, a digital twin simulation module, a fault detection algorithm, and a predictive maintenance module. The multi-source heterogeneous data fusion engine uses a spatio-temporal attention mechanism + graph neural network to mine the correlation fault patterns between devices. The digital twin simulation module constructs a physical model of the device and a virtual mirror driven by real-time data to realize fault simulation and prediction. The fault detection algorithm is based on the Transformer architecture, fuses time-domain vibration signals, frequency-domain acoustic fingerprint features, and spatial temperature distributions, and adapts to different brand devices through few-shot learning to solve the cold start problem. The predictive maintenance module fuses the physical degradation model and the LSTM prediction results to improve the long-term prediction credibility, and uses SHAP values to interpret the model, locate abnormal sensors, and trace back the fault source; The application layer includes a visual warning interface and an intelligent after-sales optimization system. The visual warning interface uses web to build an intelligent warning front-end interface. The intelligent after-sales optimization system includes an intelligent dispatching system and spare parts supply chain optimization. The intelligent dispatching system constructs a multi-objective optimization model based on fault level, geographical location, spare parts inventory, and technician skill tags, and uses a reinforcement learning dynamic scheduling algorithm to respond to sudden faults in real time. The spare parts supply chain optimization combines the fault prediction results with the regional device distribution to establish a spatio-temporal demand prediction model to realize pre-positioned warehousing of spare parts.

2. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, wherein: The heterogeneous sensor cluster develops a piezoelectric-thermoelectric composite self-powered patch, with a thickness < 1mm and a bending radius < 5mm, realizing three-in-one sensing of vibration ±50g, temperature -20 - 150°C, and acoustic fingerprint 20Hz - 20kHz. The specific steps for the heterogeneous sensor cluster to construct a multi-modal data fusion architecture are as follows: 1) Preprocessing layer: Achieve μs-level synchronization through hardware-level PTP, and establish a sensor space coordinate system through a laser positioning system 2) Feature-level fusion: The vibration signal uses wavelet packet decomposition to extract energy features, the acoustic fingerprint signal uses a Gammatone filter bank to generate a cochleagram, constructs a thermoelectric coupling state equation, and detects hidden faults such as insulation aging through residual analysis; 3) Decision-level fusion: Perform probability synthesis on the diagnostic results of each sensor subsystem, and optimize the confidence weight in combination with the device repair history database.

3. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, characterized in that: The main control chip of the edge computing node adopts the customized AI chip C910 with the RISC-V architecture, supports 1 TOPS computing power, integrates a neural network processing unit and a digital signal processor, meets the multi-modal data processing requirements, and is paired with a field programmable gate array for real-time signal processing to improve the efficiency of vibration and voiceprint analysis; Sensor interface. It supports the access of 12-channel sensors, including: analog input (vibration, temperature), digital interface (I2S for voiceprint acquisition), environmental sensors (humidity, air pressure through SPI), and integrates an energy harvesting module piezoelectric material + solar thin film, supporting wireless power supply for sensor patches; Communication module: Dual-mode wireless transmission: Wi-Fi6 and LoRa are integrated, supporting dynamic switching.

4. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 3, characterized in that: The data processing flow of the edge computing node is specifically as follows: 1) Preprocessing of the original signal Vibration signal: Kalman filter for noise reduction + STFT time-frequency analysis; Voiceprint signal: MFCC feature extraction, accelerated by FPGA; Temperature / current: Moving window mean filtering; 2) Lightweight anomaly detection Deploy a 1D depthwise separable convolutional network, with model parameters < 100KB 3) Feature compression and transmission For the confirmed abnormal data, extract the key feature vectors, vibration peak value, and voiceprint resonance frequency; 4) Dynamic model update When the cloud pushes a new fault mode, update the edge model through knowledge distillation; When the edge computing node device is started for the first time, it automatically calibrates the sensor position deviation through the voiceprint excitation signal, ensures stable operation beside high-power devices through PCB hierarchical layout, reserves SWD / JTAG interfaces for firmware burning, and broadcasts diagnostic information through BLE to avoid disassembly maintenance.

5. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, wherein: The multi-source heterogeneous data fusion engine is based on multi-modal feature extraction of deep learning, device mechanism modeling, realizes adaptive fusion through a dynamic weight gating network, constructs a knowledge graph containing more than 3000 fault cases, uses the TransE algorithm for embedding representation, and a graph-driven feature selection strategy.

6. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, characterized in that: The digital twin simulation module performs real-time data acquisition and preprocessing through the physical entity interface, uses a multi-scale model library for device mechanism models, data-driven models, and hybrid models, performs real-time solution and visualization through the simulation engine, manages full-life cycle data through the digital thread, and performs real-time control and feedback through the virtual-real interaction interface.

7. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, characterized in that: The fault detection algorithms include: Signal preprocessing layer: Data cleaning and feature extraction; Single-modal detection layer: Independent analysis of each sensor; Multi-modal fusion layer: Cross-sensor feature interaction; Decision output layer: Fault classification and location; The signal preprocessing layer processes data specifically as follows: Vibration signal processing method: Adaptive wavelet packet decomposition; Voiceprint signal processing method: Mel spectrum extraction; Temperature signal processing method: Moving window filtering; Current signal processing method: Harmonic analysis; In the single-modal detection layer, based on physics-constrained deep feature learning, the DResNet + attention mechanism framework is used for vibration signal detection, and the CNN + GRU framework is used for voiceprint signal detection The multi-modal fusion layer is based on cross-modal interaction of graph neural networks; The decision output layer is based on the interpretable diagnosis of the attention mechanism and adopts the feature contribution degree analysis of SHAP values.

8. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, characterized in that: The intelligent after-sales optimization system includes the following: Intelligent diagnosis center: Remote fault diagnosis and root cause analysis; Service scheduling engine: Intelligent order assignment and resource optimization; Customer interaction platform: Omni-channel customer service and satisfaction management; Spare parts supply chain: Intelligent prediction and dynamic inventory management; Quality feedback closed-loop: Fault data feedback and product improvement.

9. The intelligent fault warning and after-sales optimization system for household devices based on multi-sensor fusion and wireless transmission according to claim 1, characterized in that: The core modules for optimizing the spare parts supply chain include the following: Demand prediction engine: Spare parts demand prediction based on equipment status; Inventory optimization model: Multi-objective inventory strategy optimization; Logistics scheduling system: Intelligent warehousing and distribution route planning; Digital twin platform: Visualization and simulation of the entire spare parts supply chain process; Blockchain traceability system: Traceability of the entire life cycle of spare parts.

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