Auxiliary household appliance quality inspection system based on tablet computer

Abstract

The invention provides a panel-based auxiliary home appliance quality inspection system, and relates to the technical field of intelligent home appliances, the panel-based auxiliary home appliance quality inspection system comprises a quality inspection processing system, and the quality inspection processing system comprises a mirror image universe simulation module, an ideological flow sensing module, an entropy change tracking module, a super-sensory collaboration module and a self-evolution algorithm module. According to the method, the operation data of the household appliances are collected in real time through the tablet equipment sensor, and the digital twinborn body is constructed in combination with the generative adversarial network, so that abnormal states in operation of the household appliances can be found in time, fault deterioration caused by delayed detection is effectively avoided, the real-time performance and efficiency of quality inspection of the household appliances are improved, and the quality inspection efficiency of the household appliances is improved. A convolutional neural network is adopted to analyze brain wave signals of a user, identify emotional features and adjust detection priorities, entropy change data are deeply analyzed and fault modes are mined through wavelet transform and data mining technologies, the probability of false alarm and missing alarm is remarkably reduced, and a more accurate fault prediction and personalized solution is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home appliances, and in particular to a tablet-assisted home appliance quality inspection system. Background Art

[0002] With the increasing popularity of smart homes and the widespread use of home appliances, the safety and stability of these appliances directly impact the convenience and comfort of family life. Appliance failures can not only cause damage and financial losses, but also pose safety risks such as electrical fires and a degraded user experience. Therefore, accurately and timely monitoring the operating status of home appliances, predicting potential failures, and providing optimized solutions have become important research areas in the smart home field.

[0003] At present, household appliance fault monitoring and quality inspection mainly rely on the following technical means:

[0004] Manual inspection: Traditional home appliance maintenance involves users or professionals regularly checking the operating status of the equipment and observing abnormal sounds, temperatures, or vibrations.

[0005] Sensor monitoring: Some high-end home appliances have built-in temperature, current and other sensors, which determine the device status by monitoring changes in operating parameters.

[0006] Smart diagnostic system: Some home appliance brands have developed smart diagnostic applications that connect to home appliances via mobile phones, collect operating data and provide simple fault prompts.

[0007] Remote monitoring technology: Using IoT technology, home appliance operation data can be uploaded to the cloud, and combined with simple rule analysis to generate fault warnings.

[0008] Although existing technologies have played a certain role in the quality inspection of home appliances, they still have the following shortcomings:

[0009] First, manual inspections are inefficient and have limited coverage, making real-time monitoring impossible. Potential faults are difficult to detect in the early stages, leading to missed opportunities for repairs.

[0010] Secondly, built-in sensors and intelligent diagnostic systems are usually limited to high-end equipment, with low penetration rate and single functions, making it difficult to fully analyze complex failure modes and insufficient early warning accuracy.

[0011] Thirdly, existing remote monitoring technologies mostly rely on simple threshold judgments and lack in-depth data analysis capabilities, such as the application of machine learning or data mining technologies, resulting in frequent false alarms or missed alarms and the inability to provide accurate fault predictions and personalized solutions.

[0012] Finally, the existing system lacks the ability to perceive user emotions and coordinate with the environment. It is unable to adjust detection priorities based on user reactions, and it is difficult to verify failure trends based on environmental factors, which limits the intelligence and adaptability of the system.

[0013] Therefore, a tablet-assisted home appliance quality inspection system is needed to solve the above problems. Summary of the Invention

[0014] Technical problems solved

[0015] In view of the deficiencies in the prior art, the present invention provides a tablet-based assisted home appliance quality inspection system, which solves the problems in the above background technology.

[0016] Technical Solution

[0017] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tablet-assisted home appliance quality inspection system, including a quality inspection processing system, wherein the quality inspection processing system includes a mirror universe simulation module, a stream of consciousness perception module, an entropy change tracking module, a supersensory collaboration module and a self-evolution algorithm module;

[0018] The mirror universe simulation module collects real-time operating data of home appliances through sensors on a tablet device, constructs digital twins of the home appliances using a generative adversarial network, and generates probability distribution data of potential faults. The data is then transmitted to the display unit of the tablet device via a wireless communication protocol for visual presentation. The mirror universe simulation module extracts features from the generated digital twins, establishes a fault signature library, and compares and analyzes the real-time operating data with the fault signature library to quickly identify existing fault modes and generate corresponding warning information, which is then output to the user terminal via pop-up windows and voice prompts.

[0019] The stream of consciousness perception module is configured to collect the user's brain wave signals through an external brain-computer interface, extract subconscious reaction characteristics and transmit them to the mirror universe simulation module to adjust the detection priority;

[0020] The entropy change tracking module is configured to collect energy conversion data of household appliances through sensors of the tablet, generate an entropy change curve and transmit it to the self-evolution algorithm module to predict the critical point of failure;

[0021] The super-sensory collaboration module is configured to collect multi-point signal data of peripheral devices in the environment through a wireless Internet of Things protocol, generate a three-dimensional detection map and transmit it to the entropy change tracking module to verify the entropy change trend;

[0022] The self-evolution algorithm module is configured to receive data from the entropy change tracking module and the mirror universe simulation module, adaptively generate a detection model using a genetic algorithm, and transmit the model to the mirror universe simulation module to optimize simulation accuracy;

[0023] In terms of the control flow design of the mirror universe simulation module, it serves as the initial trigger unit of the system. After activation, it coordinates the operation of the stream of consciousness perception module, entropy change tracking module and supersensory collaboration module in sequence. The self-evolution algorithm module drives the iterative optimization of the system through global scheduling instructions.

[0024] Preferably, the stream of consciousness perception module includes an emotion recognition submodule, which uses a convolutional neural network to identify emotion-related features in brain wave signals. When it is detected that the user has negative emotions due to a home appliance failure, the detection priority of the home appliance is automatically increased, and soothing prompt information is pushed to the user, such as troubleshooting suggestions or maintenance service contact information; the stream of consciousness perception module is activated after receiving a synchronization request from the mirror universe simulation module, and if a valid brain wave signal is detected, the task queue is adjusted through a priority interrupt signal.

[0025] Preferably, the entropy change tracking module uses a wavelet transform algorithm to perform noise reduction and feature enhancement processing on the entropy change curve to improve the accuracy of entropy change trend analysis, and at the same time uses data mining technology to mine potential fault correlation patterns from historical entropy change data; the entropy change tracking module is activated by event triggering after the mirror universe simulation module completes the first simulation, and if the entropy value exceeds a preset threshold, the self-evolution algorithm module is notified through an asynchronous alarm signal.

[0026] Preferably, the supersensory collaboration module has an adaptive signal adjustment function. When it detects that the signal strength of other devices in the environment is unstable and interfered with, it automatically adjusts the signal acquisition frequency and gain, optimizes the parameters of the multi-dimensional signal integration network to generate a reliable three-dimensional detection image, and records the signal adjustment process in a log file; the supersensory collaboration module starts external device collaboration through a broadcast signal during system initialization, and activates the entropy change tracking module through a state switching signal after completing data fusion.

[0027] Preferably, the self-evolution algorithm module considers factors such as the age, brand and model of home appliances, establishes a personalized detection model library, and uses transfer learning technology to migrate existing model knowledge to new home appliance types and usage scenarios to speed up model generation; the self-evolution algorithm module enters an iterative state after receiving a signal from any module, synchronizes other modules through global timing scheduling instructions, and publishes an update event after completing the optimization.

[0028] Preferably, the quality inspection and processing system includes a security protection module, which encrypts the data transmitted between modules by combining symmetric encryption and asymmetric encryption, monitors network traffic and device status in real time through an intrusion detection algorithm, and activates an emergency response mechanism when an attack is detected, such as cutting off the network connection and sending an alert to the administrator; the security protection module continuously monitors when the system is running, and if an abnormality is detected, suspends the operation of other modules through an emergency interrupt signal.

[0029] Preferably, the quality inspection and processing system has a remote upgrade function, which pushes algorithm versions and fault feature library updates through the cloud server. After user confirmation, the upgrade file is automatically downloaded and installed. Important data is backed up during the upgrade process, and the relevant modules are automatically restarted and self-checked after completion.

[0030] Preferably, the quality inspection and processing system includes the following steps during operation: Sp1. Regularly clean and update the fault feature library, delete outdated features and add new fault modes; Sp2. Optimize the emotion recognition submodule and detection priority adjustment strategy based on user feedback; Sp3. Use historical entropy change data and fault records to optimize the wavelet transform algorithm and data mining model; Sp4. Retrain the adaptive signal adjustment parameters and multi-dimensional signal integration network of the supersensory collaborative module according to environmental changes; Sp5. Use transfer learning technology to expand the personalized detection model library and adapt to new types of home appliances.

[0031] Beneficial effects

[0032] The present invention provides a tablet-assisted home appliance quality inspection system. It has the following beneficial effects:

[0033] 1. The present invention uses tablet device sensors to collect home appliance operation data in real time, and combines it with a generative adversarial network to build a digital twin. This can promptly detect abnormal conditions in the operation of home appliances, effectively avoid fault deterioration caused by delayed detection, and improve the real-time performance and efficiency of home appliance quality inspection.

[0034] 2. This invention uses a convolutional neural network to analyze the user's brainwave signals, identify emotional characteristics and adjust detection priorities. At the same time, it uses wavelet transform and data mining technology to deeply analyze entropy change data, mine fault modes, significantly reduce the probability of false alarms and missed alarms, and provide more accurate fault prediction and personalized solutions.

[0035] 3. The present invention not only relies on simple threshold judgment, but also adaptively generates personalized detection models through genetic algorithms and transfer learning technology, and combines historical data and environmental signal trends for multi-dimensional verification, thereby enhancing the accuracy of fault prediction and the intelligence level of the system.

[0036] 4. The present invention introduces a super-sensory collaborative module, uses the wireless Internet of Things protocol to collect environmental signals, generates a three-dimensional detection map to verify the entropy change trend, and has an adaptive signal adjustment function, which can optimize the data collection quality in complex environments and improve the system's adaptability to different types of home appliances and usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a specific flow chart of the present invention;

[0038] Figure 2 It is a system framework diagram of the present invention;

[0039] Figure 3 This is a system simulation diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Specific embodiment one:

[0042] like Figure 1-3 As shown, the tablet-assisted home appliance quality inspection system includes a quality inspection processing system, which includes a mirror universe simulation module, a stream of consciousness perception module, an entropy change tracking module, a supersensory collaboration module and a self-evolution algorithm module;

[0043] The mirrored universe simulation module collects real-time operating data from home appliances through sensors on tablet devices, constructs digital twins of the appliances using a generative adversarial network, and generates probability distribution data for potential faults. This data is then transmitted to the tablet device's display unit via a wireless communication protocol for visual presentation. The module extracts features from the generated digital twins, establishes a fault signature library, and compares and analyzes real-time operating data with the fault signature library to quickly identify past fault modes and generate corresponding warning information, which is then output to the user terminal via pop-up windows and voice prompts.

[0044] The stream of consciousness perception module is configured to collect the user's brain wave signals through an external brain-computer interface, extract subconscious reaction characteristics and transmit them to the mirror universe simulation module to adjust the detection priority;

[0045] The entropy change tracking module is configured to collect energy conversion data of the home appliance through the sensors of the tablet, generate an entropy change curve and transmit it to the self-evolution algorithm module to predict the critical point of failure;

[0046] The super-sensory collaboration module is configured to collect multi-point signal data of peripheral devices in the environment through the wireless Internet of Things protocol, generate a three-dimensional detection map and transmit it to the entropy change tracking module to verify the entropy change trend;

[0047] The self-evolution algorithm module is configured to receive data from the entropy change tracking module and the mirror universe simulation module, adaptively generate a detection model using a genetic algorithm, and transmit the model to the mirror universe simulation module to optimize simulation accuracy;

[0048] In the control flow design of the mirror universe simulation module, as the initial trigger unit of the system, after activation, it coordinates the operation of the stream of consciousness perception module, entropy change tracking module and supersensory collaboration module in sequence. The self-evolution algorithm module drives the iterative optimization of the system through global scheduling instructions.

[0049] The stream of consciousness perception module includes an emotion recognition submodule, which uses a convolutional neural network to identify emotion-related features in brain wave signals. When it detects that the user has negative emotions due to a home appliance failure, it automatically increases the detection priority of the home appliance and pushes soothing prompts to the user, such as troubleshooting suggestions or repair service contact information; the stream of consciousness perception module is activated after receiving a synchronization request from the mirror universe simulation module. If a valid brain wave signal is detected, the task queue is adjusted through a priority interrupt signal.

[0050] The entropy change tracking module uses the wavelet transform algorithm to reduce noise and enhance features of the entropy change curve to improve the accuracy of entropy change trend analysis. At the same time, it uses data mining technology to mine potential fault correlation patterns from historical entropy change data. The entropy change tracking module is activated by event triggering after the mirror universe simulation module completes the first simulation. If the entropy value exceeds the preset threshold, the self-evolution algorithm module is notified through an asynchronous alarm signal.

[0051] The supersensory collaboration module has an adaptive signal adjustment function. When it detects that the signal strength of other devices in the environment is unstable or interfered with, it automatically adjusts the signal acquisition frequency and gain, optimizes the parameters of the multi-dimensional signal integration network to generate a reliable three-dimensional detection image, and records the signal adjustment process in the log file; the supersensory collaboration module starts external device collaboration through a broadcast signal during system initialization, and activates the entropy change tracking module through a state switching signal after completing data fusion.

[0052] The self-evolution algorithm module considers factors such as the age, brand, and model of home appliances to establish a personalized detection model library. It uses transfer learning technology to migrate existing model knowledge to new home appliance types and usage scenarios, thereby speeding up model generation. The self-evolution algorithm module enters the iterative state after receiving a signal from any module, synchronizes other modules through global timing scheduling instructions, and publishes an update event after completing the optimization.

[0053] The quality inspection and processing system includes a security protection module, which encrypts the data transmitted between modules using a combination of symmetric and asymmetric encryption. It monitors network traffic and device status in real time through intrusion detection algorithms. When an attack is detected, it activates an emergency response mechanism, such as cutting off the network connection and sending an alert to the administrator. The security protection module continuously monitors while the system is running, and if an anomaly is detected, it suspends the operation of other modules through an emergency interrupt signal.

[0054] The quality inspection and processing system has a remote upgrade function, which pushes algorithm versions and fault feature library updates through the cloud server. After user confirmation, the upgrade file is automatically downloaded and installed. Important data is backed up during the upgrade process. After completion, the relevant modules are automatically restarted and self-checked.

[0055] The quality inspection and processing system includes the following steps during operation: Sp1. Regularly clean and update the fault feature library, delete outdated features and add new fault modes; Sp2. Optimize the emotion recognition submodule and detection priority adjustment strategy based on user feedback; Sp3. Use historical entropy change data and fault records to optimize the wavelet transform algorithm and data mining model; Sp4. Retrain the adaptive signal adjustment parameters and multi-dimensional signal integration network of the supersensory collaborative module according to environmental changes; Sp5. Use transfer learning technology to expand the personalized detection model library and adapt to new types of home appliances.

[0056] The "Tablet-Assisted Home Appliance Quality Inspection System" is an intelligent inspection tool based on a portable tablet device. Through its quality inspection and processing system, it enables real-time monitoring of appliance operating status, fault prediction, and optimization. The quality inspection and processing system consists of multiple modules, including a mirror universe simulation module, a stream of consciousness perception module, an entropy change tracking module, a supersensory collaboration module, a self-evolutionary algorithm module, a security protection module, and remote upgrade capabilities. The system completes appliance quality inspection tasks through sensor data acquisition, algorithm analysis, inter-module collaboration, and user interaction. All modules run on the tablet's processor and storage unit, relying on wireless communication technology to interact with external devices.

[0057] Specific processing content and overall operation mode of modules and subsystems:

[0058] The mirror universe simulation module is the system's core data processing unit. It uses the tablet's built-in sensors to collect sound signals, temperature values, and vibration frequencies from the appliances during operation. The sensors collect data at a fixed sampling rate of 1,000 times per second, generating a time series dataset that is fed into the module's internal generative adversarial network. The network consists of a generator and a discriminator. The generator constructs a digital twin of the appliance based on the collected data, while the discriminator verifies the twin's similarity to the real data. After 50 training iterations, the module outputs the digital twin. The module then calls a time-accelerated simulation network, using numerical integration to simulate the appliance's operation at 40°C for 1,000 hours. The module then calculates the probability distribution of failures, expressed as a range of 0 to 1. The module extracts features from the digital twin, including vibration peaks and temperature change rates, and stores them in a fault feature library, where features are stored as vectors. Real-time data is compared with the signature database using the Euclidean distance algorithm. If the distance is less than 0.1, it is identified as a known fault and a warning message is generated. This information is transmitted to the display unit via the tablet's Wi-Fi module. The display unit then uses the graphics rendering library to draw the probability curve. The warning message appears in a pop-up window, and a voice prompt is played through the tablet's speakers. This module is activated by the processor's main thread at system startup and, once running, notifies other modules via a semaphore mechanism.

[0059] The stream-of-consciousness perception module is a user interaction analysis unit. It collects user brainwave signals through an external brain-computer interface device. The interface records brainwave voltage values ​​at a frequency of 256 Hz, generating a two-dimensional time-amplitude dataset. This dataset is then fed into the module's internal attention network, which comprises a multilayer perceptron. The softmax function calculates weights for each brainwave segment and extracts subconscious response features. These features are represented as 10-dimensional vectors and transmitted to the Mirror Universe simulation module for detection priority adjustment. The emotion recognition submodule, an auxiliary unit of this module, receives the EEG dataset and invokes a convolutional neural network consisting of three convolutional layers and two pooling layers to extract emotional features, including delta wave intensity and theta wave frequency. If the delta wave intensity exceeds 0.5 millivolts, it identifies a negative emotion. The module prioritizes the appliance detection task and generates a text message containing fault information, which is displayed on the display unit. This module is activated by an interrupt service routine after the Mirror Universe simulation module sends a synchronization signal. Upon completion of processing, the Mirror Universe simulation module is notified via shared memory.

[0060] The entropy change tracking module is a home appliance energy analysis unit. It collects energy conversion data, including current and heat dissipation, through flat-panel sensors. The sensors generate a data set at a sampling rate of 500 times per second. The module internally utilizes a thermodynamic entropy increase model and a wavelet transform algorithm to perform discrete wavelet decomposition on the curve, removing high-frequency noise while retaining low-frequency trends. This feature is then enhanced and stored in local flash memory. Data mining technology utilizes an association rule algorithm to analyze historical entropy change data and discover rules, such as "the probability of compressor failure increases to 80% when the entropy value increases by 0.2." These results are then transmitted to the self-evolutionary algorithm module to predict the critical failure point, which is timestamped. The module is triggered by a timer after the mirror universe simulation module completes its initial simulation. If the entropy value exceeds a preset threshold of 0.3, an alarm signal is sent via the message queue.

[0061] The Supersensory Collaboration Module is a multi-device collaboration unit that uses the ZigBee wireless IoT protocol to collect multi-point signal data from surrounding devices in the environment, including electromagnetic interference intensity and ambient noise decibels. Devices transmit data packets at a sampling rate of 200 times per second. The module's internal multidimensional signal integration network receives the data. This network, consisting of five fully connected layers, fuses the data through matrix multiplication to generate a stereoscopic detection map, which is stored as a three-dimensional grid. The adaptive signal conditioning function monitors signal strength. If the signal strength falls below -70 decibels, the sampling rate is increased to 300 times per second, the gain is adjusted to 1.5 times, and the network weights are optimized. The updated detection map is then encrypted with AES and transmitted to the Entropy Tracking Module to verify the entropy trend. The conditioning process is recorded in a log file, which is stored with a timestamp on the tablet's storage unit. The module establishes a connection via a broadcast frame during system initialization. Once fusion is complete, the Entropy Tracking Module is activated via a semaphore.

[0062] The self-evolutionary algorithm module is the model optimization unit. It receives entropy change curves from the entropy change tracking module and probability distribution data from the mirror universe simulation module. Data is input in CSV format. The module invokes a genetic algorithm, which initializes 50 candidate models and sets a fitness function based on the appliance's age, brand, and model. It iterates through 20 generations of crossover and mutation operations to generate a personalized detection model, which is stored as a weight matrix. Transfer learning techniques load the weights of the existing model, adjust the final network layer to accommodate the new appliance type, and transfer the optimized model to the mirror universe simulation module via a memory-mapped file. Upon receiving a signal, the module is assigned tasks by the thread pool and synchronized with other modules via a global semaphore. Upon completion of optimization, an update signal is issued.

[0063] The security protection module is a data security unit that encrypts data transmission between modules. After receiving data, it uses the AES algorithm to generate a 128-bit encryption key, and then uses the RSA algorithm to encrypt the key. The encrypted data is transmitted via the TCP protocol. The intrusion detection algorithm analyzes network traffic. If the number of data packets per second exceeds 1000, it is determined to be an anomaly. The module disconnects the Wi-Fi connection, isolates the abnormal module, and sends an alert to the administrator's email address via email. The alert includes a timestamp and the type of anomaly. The module runs continuously in an independent thread and suspends other modules through system calls when an anomaly is detected. The remote upgrade function detects updates through the cloud server, pushes the version number and signature library file, and downloads it via the HTTPS protocol after user confirmation. The old file is overwritten after decompression and the data is backed up to the local flash memory. After the upgrade, the module restarts and runs a self-test program to verify version consistency.

[0064] The overall operational approach is centered around iteration. Upon system startup, the Mirror Universe Simulation module collects data to construct a digital twin. The Stream of Consciousness Perception module is triggered to analyze brainwaves and adjust priorities. The Entropy Tracking module calculates entropy curves and verifies them with detection graphs from the Supersensory Collaboration module. The Self-Evolutionary Algorithm module generates model feedback for optimization, outputs warning information to the user terminal, encrypts data and monitors traffic, and remotely updates the system. Optimization steps include cleaning the fault signature library, removing outdated features, and adding new patterns. Emotion recognition weights are adjusted based on user feedback. Historical data is used to optimize the number of wavelet transform decomposition layers and data mining rules. Signal conditioning gains and network weights are adjusted based on environmental changes. Transfer learning is used to expand the model library to accommodate new appliances. Specific embodiment two:

[0066] like Figure 1-3 As shown, the key algorithms mentioned in Example 1 are analyzed in detail below, including their core mathematical formulas and explanations:

[0067] Generative Adversarial Network (GAN): The Mirror Universe Simulation Module uses GAN to build digital twins of home appliances.

[0068] The specific mathematical formula is as follows:

[0069]

[0070] Where: This formula defines the training objective of GAN. min G The goal of the generator G is to minimize the loss so that the generated digital twin is close to the real appliance data; max D The goal of the discriminator D is to maximize the loss and distinguish between real data and generated data. V(D,G) is the loss function, which consists of two parts: Calculate the expected correct and incorrect judgment of real data, x is the appliance operation data, D(x) is the discriminator output probability; Calculate the expected probability of incorrectly classifying the generated data. z represents random noise, G(z) is the digital twin output by the generator, and D(G(z)) is the score given by the discriminator. During training, the generator generates digital twins based on sound, temperature, and vibration data. The discriminator iterates through 50 optimization cycles, ultimately outputting realistic digital twins for fault probability calculation.

[0071] The system generates a virtual model that is highly similar to the real state of home appliances through adversarial training, providing a basis for subsequent fault simulation.

[0072] Convolutional Neural Network: The emotion recognition submodule uses convolutional neural network to identify emotional features in brain wave signals.

[0073] The specific formula is as follows:

[0074]

[0075] Where: This formula describes the convolution operation of the convolutional neural network. i,j is the value of the output feature map at position (i, j), representing the extracted emotional features; x i+m,j+n is the value of the input EEG data at the corresponding position, a two-dimensional time-amplitude dataset; w m,n is the weight of the convolution kernel, of size M×N, where M=3 and N=3 (three convolutional layers); b is the bias term, which adjusts the output range. The calculation process involves sliding convolution of the input data, multiplying and summing the data point by point to extract delta wave intensity and theta wave frequency features. If the delta wave intensity exceeds 0.5 millivolts, it is judged as a negative emotion. The network contains three convolutional layers and two pooling layers. The pooling layers reduce the size of the feature map and retain key information. The system uses this algorithm to extract the spatial characteristics of brain waves, accurately identify user emotions, and assist in adjusting detection priorities.

[0076] Wavelet transform algorithm: The entropy change tracking module uses wavelet transform to reduce noise and enhance features of the entropy change curve.

[0077] The specific mathematical formula is as follows:

[0078]

[0079] Where: W(a,b) is the wavelet transform coefficient, which represents the frequency characteristics of the entropy change curve at scale a and time displacement b; f(t) is the input entropy change curve, a time-amplitude data set; ψ a,b (t) is the wavelet basis function, which is generated by scaling and shifting the mother wavelet ψ(t). a controls the frequency range, and b locates the time point. Normalize the energy. In real time, use discrete wavelet decomposition and select Daubechies wavelet to remove high-frequency noise (frequency > 50 Hz), retain low-frequency trends (< 10 Hz), and enhance fault-related features.

[0080] Genetic algorithm: The self-evolution algorithm module uses genetic algorithm to generate detection models.

[0081] The specific mathematical formula is as follows:

[0082] F(x)=w1·x1+w2·x2+w3·x3

[0083] x′=crossover(x a ,x b )

[0084] Among them; F(x) is the fitness function, which evaluates the performance of the detection model, x1, x2, x3 represent the characteristic values ​​of the age, brand, and model of the home appliance respectively, w1, w2, w3 are weights, which are initially randomly assigned and optimized through iterative optimization. x′ is the new model generated by the crossover operation, x a 、x b is the parent model, and the crossover point is randomly selected, for example, x a The first half is heavy and x b The algorithm initializes 50 candidate models and iterates for 20 generations, updating the model each time through crossover and mutation (with a probability of 0.1). The model with the highest fitness is retained and output as a personalized detection model. This algorithm allows the system to adaptively generate detection models suitable for different appliances, optimizing fault prediction accuracy.

[0085] Transfer learning technology: The self-evolution algorithm module uses transfer learning to transfer model knowledge to new types of home appliances.

[0086] The specific mathematical formula is as follows:

[0087]

[0088] Where: L is the loss function, and the optimization goal is to minimize the prediction error and regularization term; is the mean square error, y i is the real fault label, is the model prediction value, N is the number of new home appliance samples; is a regularization term to prevent overfitting, λ is a regularization coefficient (set to 0.01), w j Where is the model weight, and M is the total number of weights. In real-time, the system loads the existing model weights, freezes the first few layers, adjusts only the final layer, and fine-tunes based on the new appliance data, training for five iterations. This algorithm accelerates the generation of new appliance models, reduces training time, and improves adaptability. Specific embodiment three:

[0090] like Figure 1-3The following is a description of the specific application logic steps of each module and algorithm in the tablet-assisted home appliance quality inspection system:

[0091] 1. Mirror Universe Simulation Module

[0092] Function Overview: Use flat panel sensors to collect home appliance operating data, build digital twins, generate fault probability distributions, extract features to build a fault feature library, identify faults, and output warnings.

[0093] Apply the logical steps:

[0094] Data Collection: The tablet's built-in sensors, including the microphone to collect sound signals, the temperature sensor to collect temperature values, and the accelerometer to collect vibration frequencies, are activated. The sampling rate is 1000 times per second for 10 seconds, generating a time series dataset of 10,000 data points and storing it in a CSV file.

[0095] Building a digital twin: A time series dataset is fed into a generative adversarial network. The network consists of a generator and a discriminator. The generator receives data, initializes random weights, and maps the data into a 128-dimensional vector representing the appliance status using a three-layer fully connected network. The discriminator receives both real and generated data and uses a four-layer convolutional network to determine similarity. Both are trained for 50 iterations with a learning rate of 0.001. The final output is a digital twin, which serves as a virtual mirror image of the appliance's operation.

[0096] Fault probability calculation: The digital twin is input into a time-accelerated simulation network. The simulation conditions are set to a temperature of 40 degrees Celsius and a run time of 1000 hours. State changes are calculated using a numerical integration method with a step size of 0.1 seconds. A fault probability distribution is generated with probability values ​​ranging from 0 to 1, for example, "motor failure probability 0.75," and stored in JSON format.

[0097] Feature extraction and fault feature library creation: Features are extracted from the digital twin, including vibration peak values ​​in Hertz and temperature change rates in degrees Celsius per second, generating a 32-dimensional feature vector. The feature vector is stored in the fault feature library, which is managed using a SQLite database and contains the following fields: feature ID, feature vector, and fault type (e.g., "motor wear").

[0098] Fault pattern recognition: 10 seconds of data is collected in real time to generate a new feature vector. This feature vector is compared with the vectors in the fault feature library and the distance is calculated using the Euclidean distance algorithm. If the distance is less than 0.1, it is determined to be a known fault, such as "motor wear", and a warning message "Motor may be worn, please check" is generated.

[0099] Data transmission and output: Probability distribution and warning information are transmitted to the display unit via the tablet's Wi-Fi module. The display unit uses the OpenGL graphics rendering library to draw the probability curve, with time on the horizontal axis and probability on the vertical axis. The warning information is displayed in a pop-up window, and the tablet speaker API is called to play the voice prompt "Please check the motor."

[0100] Control flow: The module is activated by the processor main thread when the system starts. After completing the processing, it notifies the consciousness flow perception module and entropy change tracking module through the semaphore mechanism.

[0101] 2. Stream of consciousness perception module (including emotion recognition submodule)

[0102] Function Overview: Collect brainwave signals through the brain-computer interface, extract subconscious reaction characteristics to adjust detection priority, identify emotions and push soothing information.

[0103] Apply the logical steps:

[0104] Signal acquisition: The external BCI device collected the user's EEG voltage values ​​at a frequency of 256 Hz for 5 seconds, generating a two-dimensional time-amplitude dataset of 1280 data points, which was stored as a CSV file containing columns for timestamps and voltage values.

[0105] Subconscious feature extraction: The dataset is input into the attention mechanism network, which contains three layers of multilayer perceptrons, with 256 neurons in each layer. The network calculates the weight of each time period and extracts a 10-dimensional subconscious reaction feature vector. For example, "sensitivity to noise 0.8" indicates the user's perception of home appliance anomalies.

[0106] Transmission adjustment priority: The feature vector is transmitted to the mirror universe simulation module through shared memory. The module adjusts the detection queue according to the feature value. If the feature value is greater than 0.5, the home appliance detection task is placed at the top of the queue.

[0107] Emotion Recognition (Submodule): The EEG dataset is fed into a convolutional neural network consisting of three convolutional layers with a kernel size of 3×3 and a stride of 1, and two max-pooling layers with a size of 2×2. These layers extract delta wave intensity and theta wave frequency features. If the delta wave intensity is greater than 0.5 millivolts, it is considered a negative emotion, indicating that the user is dissatisfied with the appliance malfunction.

[0108] Priority adjustment and prompt push: If negative emotions are detected, the home appliance detection task will be placed at the top of the priority queue, and a soothing message will be generated: "An abnormality has been detected. It is recommended to check the motor. Please call XXX for maintenance." The message will be pushed in text form through the display unit, and the speaker API will be called to play the voice version.

[0109] Control flow: After the module receives the synchronization signal from the Mirror Universe Simulation Module, it is activated by the interrupt service routine. After the processing is completed, the adjustment result is notified to the Mirror Universe Simulation Module through the shared memory.

[0110] 3. Entropy change tracking module

[0111] Function Overview: Collect energy conversion data, generate entropy change curves, reduce noise and enhance features, mine fault modes, and predict critical failure points.

[0112] Apply the logical steps:

[0113] Data collection: The flat-panel sensor collects the current value of the household appliance in amperes and the heat loss in joules. It runs at a sampling rate of 500 times per second for 20 seconds, generating a data set of 10,000 data points, which is stored in a CSV file.

[0114] Entropy calculation: Call the thermodynamic entropy increase model, estimate the number of microscopic states based on the collected data, calculate the entropy value sequence, generate a two-dimensional array of time-entropy value, one entropy value point per second, and store it in JSON format.

[0115] Noise reduction and feature enhancement: Apply the wavelet transform algorithm to the entropy value sequence, using the Daubechies D4 wavelet basis function and performing a four-level discrete decomposition to remove high-frequency noise while retaining the low-frequency trend. The enhanced entropy change curve is stored in the local flash memory with the file name "entropy_curve.json".

[0116] Fault pattern mining: Call the association rule algorithm, input historical entropy change data containing 1,000 records, scan the data to calculate the rule frequency, and mine the association relationship, such as "the probability of compressor failure is 80% when the entropy value increases by 0.2", set the support threshold to 0.1 and the confidence threshold to 0.8, and store the results as a rule list.

[0117] Fault prediction and transmission: Analyze the enhanced entropy change curve and detect whether the entropy value exceeds the threshold of 0.3. If so, mark the critical point of the fault with a timestamp, such as "2025-04-09 10:00", and transmit it to the self-evolution algorithm module through the message queue.

[0118] Control flow: The module is triggered by a timer after the mirror universe simulation module completes the first simulation. If the entropy value exceeds the limit, the self-evolution algorithm module is notified through an asynchronous alarm signal.

[0119] 4. Supersensory Collaboration Module

[0120] Function Overview: Collect signals from peripheral devices, generate stereoscopic detection images, adjust signal quality, and verify entropy change trends.

[0121] Apply the logical steps:

[0122] Signal acquisition: The ZigBee protocol is used to collect electromagnetic interference intensity and ambient noise decibels from surrounding devices in the environment, such as smart sockets. The data is run at a sampling rate of 200 times per second for 15 seconds, generating a data packet of 3000 data points and storing it in a binary file.

[0123] Signal conditioning: Monitor the signal strength. If it is below -70 decibels, adjust the sampling frequency to 300 times per second, increase the gain to 1.5 times, update the acquisition parameters, and record the conditioning process in a log file. The file is named with a timestamp, for example, "2025-04-09_10:00_log.txt."

[0124] Data fusion: Data packets are fed into a multidimensional signal integration network. The network consists of five fully connected layers, each with 128 neurons. Data is fused through matrix multiplication to generate a three-dimensional detection map. The three-dimensional grid format includes a time axis, a signal source axis, and an intensity axis, and is stored as a JSON file.

[0125] Transmission and verification: The 3D detection image is encrypted using AES with a 128-bit key length and transmitted to the entropy change tracking module through the cloud channel. The entropy change tracking module compares the detection image with the entropy change curve to verify trend consistency, such as "the increase in noise intensity is synchronized with the entropy value."

[0126] Control flow: The module establishes a connection through a broadcast frame during system initialization. After the fusion is completed, the entropy change tracking module is activated through the semaphore.

[0127] 5. Self-evolution algorithm module

[0128] Function Overview: Receive multi-source data, generate personalized detection models, transfer knowledge to new scenarios, and optimize simulation accuracy.

[0129] Apply the logical steps:

[0130] Data reception: Receives the entropy change curve from the entropy change tracking module and the probability distribution data from the mirror universe simulation module. The data is input in CSV format and contains timestamp, entropy value, and probability value columns.

[0131] Model generation: Call the genetic algorithm to initialize 50 candidate models. Each model is a neural network weight matrix. Set the initial parameters according to the age, brand, and model of the home appliance. Combine the weights through crossover operations. Mutation operations randomly adjust 10% of the weights. Iterate 20 generations and retain the model with the best performance.

[0132] Establishment of personalized model library: The generated models are classified according to the attributes of home appliances, such as "5-year Samsung refrigerator model", and stored in the personalized detection model library. The library is managed in a directory structure and the file format is a binary weight file.

[0133] Knowledge transfer: Call transfer learning technology, load the existing model weights, freeze the first three layers of the network, adjust only the last layer structure, input new home appliance type data, train for 5 iterations, and use a learning rate of 0.0001 to generate an adapted model.

[0134] Transfer optimization: The optimized model is transferred to the Mirror Universe simulation module via a memory-mapped file. The Mirror Universe simulation module loads the model weights to improve the accuracy of the next simulation.

[0135] Control flow: After receiving a signal from any module, the module is assigned a task by the thread pool, synchronized with other modules through the global semaphore, and released an update signal after optimization.

[0136] 6. Security protection module

[0137] Function Overview: Encrypt data between modules, monitor network status, and handle attacks.

[0138] Apply the logical steps:

[0139] Data encryption: When receiving data between modules, the AES algorithm is used to generate a 128-bit key to encrypt the data. The RSA algorithm is then used to generate a 2048-bit public and private key pair to encrypt the AES key. The encrypted data is transmitted via the TCP protocol.

[0140] Network monitoring: Invokes the intrusion detection algorithm to analyze network traffic per second. If the number of data packets exceeds 1,000, it is determined to be abnormal traffic and the abnormal timestamp and type are recorded.

[0141] Emergency response: If an anomaly is detected, the Wi-Fi connection will be disconnected, the abnormal module will be isolated, and an alarm message "Network attack, time 2025-04-09 10:00" will be generated and sent to the administrator's mailbox via email.

[0142] Control flow: Modules are run continuously by independent threads, and other modules are paused through system calls when an exception is detected.

[0143] 7. Remote upgrade function

[0144] Function Overview: Push and install algorithm version and fault signature library updates.

[0145] Apply the logical steps:

[0146] Update detection: Check algorithm version and fault signature library updates through the cloud server, and receive push notifications containing version numbers and file links.

[0147] Download and install: After user confirmation, the update file is downloaded through the HTTPS protocol, unzipped to a temporary directory, and the old file is overwritten.

[0148] Data backup and verification: Back up the current model library and logs to the local flash memory. After the installation is complete, run the self-test program to verify version consistency.

[0149] Control flow: The function is triggered when a notification is detected and restarts the relevant modules after completion. Specific embodiment four:

[0151] like Figure 1-3 As shown, the following is a detailed hardware composition and hardware description of each module in Example 1:

[0152] The mirror universe simulation module collects home appliance operation data through flat-panel sensors, builds digital twins, generates failure probabilities, identifies failure modes and outputs warnings. Its hardware includes: a microphone, using a MEMS model such as the Knowles SPH0645LM4H, with a frequency range of 20Hz-20kHz, a sensitivity of -26dBFS, and a sampling rate of up to 48kHz, which is responsible for collecting household appliance sound signals such as motor noise and generating time series data at a sampling rate of 1000 times per second; a temperature sensor, using a digital model such as the TMP117, with a measurement range of -55℃ to 150℃, an accuracy of ±0.1℃, and a sampling rate of up to 1kHz, which is used to measure the temperature of household appliances such as refrigerator compressor temperature; an accelerometer, using a three-axis model such as the ADXL345, with a measurement range of ±16g, a resolution of 13 bits, and a sampling rate of up to 3.2kHz, which is used to detect vibration frequencies such as washing machine motor vibration; a processor, using an ARM Cortex-A53 quad-core model such as the RK3399, with a main frequency of 1.8GHz and an integrated Mali-T860 GPU, which runs a generative adversarial network and a time-accelerated simulation network, processes 10,000 data points and trains for 50 iterations; a storage unit, using an eMMC flash memory such as the Samsung 32GB The eMMC 5.1 module, with a capacity of 32GB and a read / write speed of 150MB / s, stores fault signatures and real-time data, supporting up to 1000 feature vectors. The Wi-Fi module uses an 802.11ac model such as the Realtek RTL8821, dual-band 2.4GHz / 5GHz with a maximum transmission rate of 433Mbps, and transmits probability distributions and warning information. The display unit uses a 10.1-inch IPS touchscreen with a resolution of 1920×1200 and support for OpenGL rendering, plotting probability curves and displaying pop-up windows. The speaker uses a built-in stereo model with a power of 2W and a frequency range of 100Hz-15kHz, and plays voice prompts such as "Please check the motor." This module is activated by the processor's main thread and notifies other modules via semaphores.

[0153] The stream-of-consciousness perception module (including the emotion recognition submodule) collects brainwave signals, extracts subconscious reaction features, identifies emotions, and adjusts detection priorities. Its hardware includes: a brain-computer interface device (using an external model such as the NeuroSkyMindWave, with a 256Hz sampling rate, 8-channel EEG, and Bluetooth 4.0 connectivity) that collects brainwave voltage values ​​to generate 1280 data points, which are then transmitted to the tablet via Bluetooth; a processor using an ARM Cortex-A53 quad-core model with a main frequency of 1.8GHz and multi-threading support, which runs an attention mechanism network and a convolutional neural network to process brainwave data and extract features; a storage unit using 4GB of LPDDR4 RAM with a bandwidth of 25.6GB / s for temporary storage of brainwave data and feature vectors, interacting via shared memory; a Bluetooth module using a built-in model such as the Nordic nRF52832, with a transmission rate of 1Mbps and a range of 10 meters, which receives brain-computer interface data; a display unit using a 10.1-inch IPS touchscreen to push soothing messages; and a built-in stereo speaker with a power of 2W to play voice soothing messages. After receiving the synchronization signal, the module is activated by the interrupt service routine and notifies the adjustment result through the shared memory.

[0154] The entropy change tracking module collects energy conversion data, generates entropy change curves, reduces noise, enhances features, and discovers fault modes. Its hardware includes: a non-intrusive current sensor, such as the ACS712, with a measurement range of ±5A, 2% accuracy, and a 1kHz sampling rate, collecting appliance current values ​​and generating 10,000 data points; an infrared heat sensor, such as the MLX90614, with a measurement range of -70°C to 380°C, ±0.5°C accuracy, and a 500Hz sampling rate, measuring heat loss; a quad-core ARM Cortex-A53 processor with a 1.8GHz clock speed, running a thermodynamic entropy increase model, wavelet transform, and association rule algorithms; and a 32GB eMMC flash memory unit, storing entropy change curves and fault mode rules, supporting 1,000 historical records. The module is triggered by a timer after the first simulation and provides asynchronous alarm notifications when entropy values ​​exceed the specified limit.

[0155] The super-sensory collaborative module collects signals from surrounding devices, generates a three-dimensional detection map, and adjusts signal quality. Its hardware includes: a ZigBee module, such as the TI CC2530, operating at a 2.4GHz frequency, a 250kbps transmission rate, and a range of 100 meters. It collects electromagnetic interference and noise decibels and generates 3,000 data points. A processor, an ARM Cortex-A53 quad-core model operating at 1.8GHz, runs a multi-dimensional signal integration network to aggregate data. A 32GB eMMC flash memory unit stores adjustment logs and detection maps. A 802.11ac Wi-Fi module, operating at a 433Mbps transmission rate, transmits detection maps via the cloud. During initialization, the module establishes a connection via broadcast frames. After fusion, the entropy change tracking module is activated using signal semaphore.

[0156] The self-evolutionary algorithm module generates personalized detection models and transfers knowledge to new scenarios. Its hardware includes: a processor using an ARM Cortex-A53 quad-core model with a main frequency of 1.8GHz and support for thread pools, which runs genetic algorithms and transfer learning techniques to iterate models; a storage unit using eMMC flash memory with a capacity of 32GB, which stores the model library containing 50 weight files; and 44GB of LPDDR4 RAM with a bandwidth of 25.6GB / s, which stores input data and intermediate results. After receiving signals, the module is assigned tasks by the thread pool, and updates are synchronized and published via global semaphores.

[0157] The security protection module encrypts data, monitors network status, and handles attacks. Its hardware includes: a quad-core ARM Cortex-A53 processor running at 1.8GHz, running AES and RSA encryption algorithms and analyzing traffic; an 802.11ac Wi-Fi module supporting TCP, transmitting encrypted data and terminating connections; and a 32GB eMMC flash memory unit for storing alert logs. The module runs continuously in an independent thread, and system calls are used to suspend other modules in the event of an anomaly.

[0158] The remote update feature pushes and installs updates. Its hardware includes: an 802.11ac Wi-Fi module supporting HTTPS for downloading update files; a 32GB eMMC flash memory unit for backing up data and storing new versions; and a 1.8GHz ARM Cortex-A53 quad-core processor for decompressing, installing updates, and running self-tests. This feature triggers when a notification is detected and reboots the module upon completion. Specific embodiment five:

[0160] like Figure 1-3 As shown, the following are specific use cases of the entire solution:

[0161] Use Case 1: Household Refrigerator Compressor Fault Detection

[0162] Scenario description: A five-year-old Samsung refrigerator in a user's home makes unusual noises during operation, and the user wants to check its status.

[0163] Usage process:

[0164] Data Collection and Digital Twin Construction: The user activates the tablet, activating the mirror universe simulation module. The microphone samples the refrigerator compressor's operating sound at a sampling rate of 1,000 times per second for 10 seconds. The temperature sensor measures the compressor's surface temperature at 38°C, and the accelerometer detects a vibration frequency of 50 Hz, generating 10,000 data points. A generative adversarial network receives this data, trains the generator 50 times, and outputs the refrigerator's digital twin.

[0165] Fault Probability and Pattern Recognition: A time-accelerated simulation network simulated a refrigerator operating at 40°C for 1000 hours. The network calculated the fault probability distribution and displayed a "compressor failure probability of 0.8." The module extracted features from the twin (vibration peak value of 50 Hz, temperature change rate of 0.2°C / s) and compared them with a fault signature database. The Euclidean distance was 0.08, less than 0.1, identifying the fault as "compressor wear."

[0166] User emotion perception: The user wears a brain-computer interface device, the stream of consciousness perception module collects brain waves for 5 seconds, the attention mechanism network extracts the subconscious feature "noise sensitivity 0.7", and the emotion recognition submodule detects the delta wave intensity of 0.6 millivolts and determines it as a negative emotion. The priority queue puts the refrigerator detection at the top.

[0167] Entropy Change Analysis and Verification: The entropy change tracking module collected current values ​​of 2A and heat dissipation of 500 joules to generate an entropy change curve. A wavelet transform was used to remove noise, and the entropy value of 0.35 exceeded the threshold of 0.3. The super-sensory collaboration module collected ambient noise levels of 60dB via ZigBee and generated a 3D detection map to verify that the entropy value was consistent with the noise trend.

[0168] Model Optimization and Output: The self-evolutionary algorithm module receives data, and the genetic algorithm iterates 20 generations to generate a personalized refrigerator detection model. Transfer learning is then used to adjust the final layer to suit Samsung models. The display unit plots the probability curve, a pop-up window displays "Compressor may be worn, please check", and the speaker plays a voice prompt, pushing repair contact information.

[0169] Result: The user contacted maintenance personnel promptly and confirmed that the compressor bearing was worn, and the system's prediction was accurate.

[0170] Use Case 2: Washing Machine Motor Troubleshooting

[0171] Scenario description: A user discovered that a three-year-old Haier washing machine began to vibrate more violently during washing and suspected a motor problem.

[0172] Usage process:

[0173] Data collection and simulation: The mirror universe simulation module is started. The accelerometer samples the washing machine's vibration at 15Hz at a sampling rate of 1000 times per second. The microphone records the motor's running sound. The temperature sensor measures the motor's temperature at 45°C. The generative adversarial network is trained 50 times to build a digital twin of the washing machine.

[0174] Fault identification: After a 1000-hour time-accelerated simulation network run, the fault probability distribution indicated a "motor imbalance probability of 0.65." Feature extraction revealed a vibration peak of 15 Hz and a temperature change rate of 0.15°C / s. Comparison with the fault signature database revealed a distance of 0.09, identifying the fault as "motor imbalance."

[0175] User response adjustments: The brain-computer interface collects the user's brain waves, the stream of consciousness perception module extracts the feature "shake sensitivity 0.6", the convolutional neural network detects the increase in theta wave frequency, determines slight dissatisfaction, and adjusts the washing machine detection priority.

[0176] Energy and environmental verification: The entropy change tracking module collected data on a current of 3A, heat dissipation of 600 joules, and an entropy value of 0.28, which did not exceed the threshold. Wavelet transforms were used to enhance the curve and to mine association rules, such as "vibration of 15Hz and a 70% probability of motor imbalance." The super-sensory collaboration module collected electromagnetic interference intensity data at -65dB. When signal strength was low, the sampling rate was adjusted to 300 times / second to generate a three-dimensional detection image.

[0177] Optimization and Feedback: The self-evolutionary algorithm module generated a Haier washing machine model, using transfer learning to adapt to three years of usage scenarios. The display unit popped up "The motor may be unbalanced. Please check the balance." A voice prompt played simultaneously.

[0178] Result: The user found that the washing machine was not placed level. After adjustment, the shaking disappeared and the system detection was effective.

[0179] Use Case 3: Air Conditioning Refrigeration Efficiency Decline Warning

[0180] Scenario description: A user has been using a two-year-old Gree air conditioner and feels that the cooling effect has deteriorated. The user needs to check the cause.

[0181] Usage process:

[0182] Data collection and analysis: The mirror universe simulation module collects data on the air outlet temperature of the air conditioner, which is 28°C, the compressor vibration is 40Hz, and the sound signal is normal. The generative adversarial network constructs a digital twin, and the time acceleration simulation network simulates high-temperature operation. The failure probability is "cooling efficiency drops by 0.7."

[0183] Feature matching: Extract features (temperature change rate 0.1°C / s, vibration peak 40Hz), compare with the fault feature library, the distance is 0.07, and "refrigerant shortage" is identified.

[0184] Emotion monitoring: The stream of consciousness perception module collects brain waves with the characteristic "temperature sensitivity 0.8". The emotion recognition submodule detects delta waves of 0.55 millivolts, determines negative emotions, and increases priority.

[0185] Entropy change and collaborative verification: The entropy change tracking module measured a current of 1.5A, heat loss of 400 joules, and an entropy value of 0.32, exceeding the threshold. Wavelet transforms enhanced the trend and discovered the rule "entropy value 0.3 and refrigerant shortage probability 85%." The super-sensory collaborative module collected 50dB of ambient noise, and a stereoscopic detection image confirmed the trend.

[0186] Model update and notification: The self-evolutionary algorithm module generates a Gree air conditioner model, and transfer learning is adapted to two years of usage scenarios. The display unit pops up "Refrigerant may be insufficient, please contact maintenance", and voice prompts are synchronized.

[0187] Result: The user contacted maintenance personnel to add refrigerant, the cooling effect was restored, and the system warning was successful.

[0188] These three sets of cases respectively demonstrate the inspection process of refrigerators, washing machines and air conditioners, reflecting the collaborative work of modules and the complete process from data collection to fault identification, user emotion adjustment, model optimization and result output.

[0189] Figure 3 This is the visualization output of the Mirror Universe simulation module results. It contains three sub-plots, respectively showing the sound signal, temperature signal, and fault probability distribution. The first sub-plot (Sound Signal) displays the sound signal collected over a 10-second period. The horizontal axis is time (0 to 10 seconds) and the vertical axis is amplitude (-2 to 2V). The blue line represents the collected signal, which exhibits 50Hz periodic fluctuations superimposed with noise, reflecting the acoustic characteristics of the refrigerator compressor during operation. The second sub-plot (Temperature Signal) displays the compressor surface temperature. The horizontal axis is time (0 to 10 seconds) and the vertical axis is temperature (37 to 40°C). The red line represents the collected signal, showing a slow increase from 38°C at a rate of approximately 0.2°C / s, consistent with the simulation data settings. The third sub-plot (Fault Probability Distribution) is a histogram with probability (0 to 1) on the horizontal axis and frequency on the vertical axis. The green bar graph shows the fault probability distribution calculated by the Monte Carlo algorithm. The probability is concentrated around 0.8. The legend indicates an average probability of 0.82, indicating that "compressor wear" was detected, consistent with the feature matching results.

[0190] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0191] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A tablet-assisted home appliance quality inspection system, including a quality inspection processing system, is characterized by: The quality inspection and processing system includes a mirror universe simulation module, a stream of consciousness perception module, an entropy change tracking module, a supersensory coordination module and a self-evolution algorithm module; The mirror universe simulation module collects real-time operating data of home appliances through sensors on a tablet device, constructs digital twins of the home appliances using a generative adversarial network, and generates probability distribution data of potential faults. The data is then transmitted to the display unit of the tablet device via a wireless communication protocol for visual presentation. The mirror universe simulation module extracts features from the generated digital twins, establishes a fault signature library, and compares and analyzes the real-time operating data with the fault signature library to quickly identify existing fault modes and generate corresponding warning information, which is then output to the user terminal via pop-up windows and voice prompts. The stream of consciousness perception module is configured to collect the user's brain wave signals through an external brain-computer interface, extract subconscious reaction characteristics and transmit them to the mirror universe simulation module to adjust the detection priority; The entropy change tracking module is configured to collect energy conversion data of household appliances through sensors of the tablet, generate an entropy change curve and transmit it to the self-evolution algorithm module to predict the critical point of failure; The super-sensory collaboration module is configured to collect multi-point signal data of peripheral devices in the environment through a wireless Internet of Things protocol, generate a three-dimensional detection map and transmit it to the entropy change tracking module to verify the entropy change trend; The self-evolution algorithm module is configured to receive data from the entropy change tracking module and the mirror universe simulation module, adaptively generate a detection model using a genetic algorithm, and transmit the model to the mirror universe simulation module to optimize simulation accuracy; In terms of the control flow design of the mirror universe simulation module, it serves as the initial trigger unit of the system. After activation, it coordinates the operation of the stream of consciousness perception module, entropy change tracking module and supersensory collaboration module in sequence. The self-evolution algorithm module drives the iterative optimization of the system through global scheduling instructions.

2. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The mirror universe simulation module simulates the operating state of the digital twin under extreme conditions through a time acceleration simulation network and generates the probability distribution data using a Monte Carlo algorithm; In terms of control flow design, the mirror universe simulation module enters the initialization state when the system starts. After detecting the input of home appliance operation data, it is triggered to activate through the internal clock signal. After completing the simulation, it sends a data ready signal to the self-evolution algorithm module and waits for feedback.

3. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The stream of consciousness perception module includes an emotion recognition submodule, which uses a convolutional neural network to identify emotion-related features in brain wave signals. When it detects that the user has negative emotions due to a home appliance failure, it automatically increases the detection priority of the home appliance and pushes soothing prompt information to the user; the stream of consciousness perception module is activated after receiving a synchronization request from the mirror universe simulation module. If a valid brain wave signal is detected, the task queue is adjusted through a priority interrupt signal.

4. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The entropy change tracking module uses a wavelet transform algorithm to reduce noise and enhance features on the entropy change curve, thereby improving the accuracy of entropy change trend analysis, and at the same time uses data mining technology to mine potential fault correlation patterns from historical entropy change data; the entropy change tracking module is activated by event triggering after the mirror universe simulation module completes the first simulation, and if the entropy value exceeds a preset threshold, the self-evolution algorithm module is notified through an asynchronous alarm signal.

5. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The supersensory collaborative module has an adaptive signal adjustment function. When it detects that the signal strength of other devices in the environment is unstable or interfered with, it automatically adjusts the signal acquisition frequency and gain, optimizes the parameters of the multi-dimensional signal integration network to generate a reliable three-dimensional detection image, and records the signal adjustment process in a log file; The supersensory collaboration module starts external device collaboration through a broadcast signal during system initialization, and activates the entropy change tracking module through a state switching signal after completing data fusion.

6. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The self-evolution algorithm module considers factors such as the age, brand and model of home appliances to establish a personalized detection model library, and uses transfer learning technology to migrate existing model knowledge to new home appliance types and usage scenarios to speed up model generation. The self-evolution algorithm module enters an iterative state after receiving a signal from any module, synchronizes other modules through global timing scheduling instructions, and publishes an update event after completing the optimization.

7. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The quality inspection and processing system includes a security protection module, which encrypts the data transmitted between modules using a combination of symmetric encryption and asymmetric encryption, monitors network traffic and device status in real time through an intrusion detection algorithm, and activates an emergency response mechanism when an attack is detected; the security protection module continuously monitors when the system is running, and if an abnormality is detected, it suspends the operation of other modules through an emergency interrupt signal.

8. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The quality inspection and processing system has a remote upgrade function, which pushes algorithm versions and fault feature library updates through the cloud server. After user confirmation, the upgrade file is automatically downloaded and installed. Important data is backed up during the upgrade process. After completion, the relevant modules are automatically restarted and self-checked.

9. The tablet-assisted home appliance quality inspection system according to claim 1, characterized in that: The quality inspection processing system includes the following steps when running: Sp1. Regularly clean and update the fault signature library, delete outdated signatures and add new fault modes; Sp2, optimize the emotion recognition submodule and detection priority adjustment strategy based on user feedback; Sp3. Use historical entropy change data and fault records to optimize the wavelet transform algorithm and data mining model; Sp4. Retrain the adaptive signal adjustment parameters and multi-dimensional signal integration network of the supersensory collaborative module according to environmental changes; Sp5. Use transfer learning technology to expand the personalized detection model library and adapt to new types of home appliances.

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