Tablet computer fault monitoring method and system
Through quantum random multi-source data acquisition, GAN data comparison and DNA algorithm diagnosis, combined with emotional computing models, the misjudgment problem in tablet computer fault diagnosis is solved, and the precise location of faults and efficient maintenance of equipment is achieved.
Patent Information
- Application Number
- CN202510411960.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-02
- Publication Date
- 2025-08-05
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art fails to effectively combine user operating habits with tablet computer failures, resulting in misjudgment of fault diagnosis and affecting user experience and device reliability.
Quantum random multi-source data acquisition, GAN data comparison, DNA algorithm diagnosis and quantum encryption technology are used, combined with emotional computing models, and through adaptive data acquisition and VR visual report generation, the precise location and diagnosis of faults are achieved.
It improves the accuracy of fault diagnosis, optimizes user experience, reduces troubleshooting costs, and improves the reliability and practicality of the equipment.
Smart Images
Figure CN120429192A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of fault monitoring, and in particular to a method and system for monitoring faults of a tablet computer. Background Art
[0002] In today's digital age, tablets have become deeply integrated into every aspect of our lives, whether for work, study, or entertainment. From document processing and meeting participation in mobile office settings to browsing online courses and submitting assignments, and even for leisure and entertainment, tablets have become an indispensable electronic device thanks to their convenience and versatility.
[0003] As tablet computer applications continue to expand, their failure issues are becoming increasingly prominent. Currently, tablet computer failure detection methods primarily focus on routine monitoring of hardware parameters and superficial inspections of software operating status. On the hardware side, temperature sensors monitor the temperatures of key components like the CPU and GPU, while power monitoring modules monitor battery status. On the software side, the system's built-in error reporting mechanism collects information on program crashes, performance freezes, and other issues.
[0004] However, in actual use, this monitoring process completely ignores the potential impact of user habits and emotional factors on faults. Frequent use of certain applications may lead to data anomalies due to specific operating habits. However, existing detection technologies are unable to correlate user emotions with data anomalies, resulting in numerous misjudgments in fault diagnosis and difficulty pinpointing faults caused by user habits. This not only severely impacts the user experience and prevents effective problem resolution, but also hinders software developers from optimizing and upgrading for real-world scenarios, increases the difficulty and cost of troubleshooting for technicians, and reduces the reliability and practicality of applications and devices. Therefore, we propose a tablet computer fault monitoring method and system. Summary of the Invention
[0005] In view of the shortcomings of the existing technology, the present invention provides a tablet computer fault monitoring method and system, which solves the problem in the existing technology that the tablet computer fault problem is not analyzed based on the user's operating habits.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tablet computer fault monitoring method and system, comprising the following steps:
[0007] S1: Preparation for Adaptive Data Collection
[0008] Adaptively adjust hardware monitoring device initialization parameters based on the tablet's current operating mode, such as power saving or high-performance mode. At the same time, an intelligent implantation algorithm prioritizes the implantation of software monitoring code in high-frequency and core applications.
[0009] S2: Quantum random multi-source data acquisition
[0010] Introducing a quantum random number generation mechanism, fine-tuning the hardware parameter collection time interval, and using blockchain technology to encrypt and store software operation data, including application startup time and crash counts, in a distributed ledger.
[0011] S3: GAN data comparison abnormal warning
[0012] Use artificial intelligence to conduct data comparison against the generative adversarial network. The generator simulates the normal data distribution, and the discriminator compares the real-time data. If the difference is too large, an abnormal warning will be triggered.
[0013] S4: DNA algorithm for accurate fault diagnosis
[0014] Adopting a fault diagnosis algorithm based on DNA computing, the fault rules and data are encoded into DNA sequences, simulating biochemical reactions and determining the fault type;
[0015] S5: Fault report generation and notification
[0016] Generate fault reports using virtual reality technology and create 3D models based on the current tablet device model. Abnormal information is marked in the established 3D model in a flashing red highlight.
[0017] S6: Quantum Encryption Data Backup and Maintenance
[0018] Quantum encryption technology is used to encrypt monitoring data into quantum state storage. Machine learning algorithms identify redundant and invalid data, and regular quantum error correction operations are performed to ensure data integrity.
[0019] Preferably, in the preparation for the adaptive data acquisition, an operation mode-parameter mapping table needs to be pre-established for actual computer parameter performance, and the optimal parameter configuration of each hardware monitoring device under different operation modes is adjusted in real time through the operation mode-parameter mapping table.
[0020] Preferably, during the quantum random multi-source data acquisition process, the characteristics of quantum entanglement are used to correlate the acquisition of multiple hardware parameters. By constructing a quantum entangled state, the acquisition of different hardware parameters is synchronized at the quantum level. When one parameter is acquired, the acquisition of other entangled parameters is also triggered.
[0021] Preferably, the quantum random multi-source data acquisition uses a quantum annealing algorithm to optimize the acquisition path. The quantum annealing algorithm simulates the annealing process of the quantum system to find the optimal path for data acquisition and quickly converges to the optimal solution, thereby reducing unnecessary data collection points and reducing the generation of redundant data.
[0022] Preferably, the GAN data comparison anomaly warning step incorporates an emotional computing model to analyze the potential connection between user usage habits and data anomalies. By monitoring user interaction frequency, operation duration, and operation process data between the user and the application, the user's emotional tendencies can be determined. When data anomalies occur due to frequent user use of an application, the system will conduct a more in-depth anomaly assessment based on the user's emotional data to determine whether the failure is caused by the user's specific usage habits.
[0023] Preferably, the DNA algorithm for precise fault diagnosis combines the concept of gene editing technology to dynamically edit and optimize the DNA coding sequence of the fault rules. When faced with new fault types or complex fault scenarios, the system will simulate the gene editing process to cut, reorganize and add new base codes to the existing fault rule DNA sequence to update the fault diagnosis rules.
[0024] Preferably, in the fault report generation and notification, for hardware faults, the location of the faulty component and fault status information will be presented in a three-dimensional model; for software faults, the software running process will be dynamically demonstrated to show the link where the fault occurred.
[0025] A tablet computer fault monitoring system includes the following modules:
[0026] Operation mode perception and parameter adaptation module: Built-in multiple sensors and analysis logic, it can identify the tablet's operation mode in real time, automatically call the preset adaptation strategy to adjust the hardware monitoring device parameters including sensitivity and sampling frequency, and intelligently select and accurately embed software monitoring code into the monitoring application;
[0027] Quantum randomness and blockchain fusion acquisition module: The quantum random number generator is used to give randomness to hardware parameter acquisition, ensuring the comprehensiveness and timeliness of data; the distributed ledger and encryption technology of the blockchain are used to securely store software operation data to prevent data tampering and loss;
[0028] GAN intelligent comparison and warning module: This module integrates a generative adversarial network model to continuously learn the characteristics and patterns of normal data, compares monitoring data in real time, and quickly triggers an early warning mechanism upon detecting abnormal deviations, marking the source and type of abnormal data.
[0029] DNA computing diagnostic engine module: This module uses DNA computing principles to convert fault rules and monitoring data into biological coding information. By simulating biochemical reactions and performing high-speed parallel computing, it can quickly and accurately diagnose the fault type and root cause.
[0030] VR visualization report generation and push module: This module uses virtual reality technology to create a three-dimensional model that corresponds to the physical structure and software architecture of the tablet computer, displays fault information on the model with intuitive visual effects, and automatically generates and pushes reports to designated recipients.
[0031] Quantum encryption data management module: uses quantum key encryption technology to encrypt and store monitoring data, uses machine learning algorithms to regularly clean up redundant data, and performs quantum error correction operations to ensure data security and reliability.
[0032] Preferably, the operation mode perception and parameter adaptation module has self-learning capability, and automatically optimizes the operation mode-parameter mapping table by continuously collecting and analyzing equipment performance data under different operation modes to improve the accuracy and adaptability of parameter adjustment.
[0033] Preferably, the operation mode perception and parameter adaptation module has self-learning capability, and automatically optimizes the operation mode-parameter mapping table by continuously collecting and analyzing equipment performance data under different operation modes to improve the accuracy and adaptability of parameter adjustment.
[0034] The present invention provides a tablet computer fault monitoring method and system, which has the following beneficial effects:
[0035] 1. The affective computing model established in this invention integrates user emotional tendencies with data anomalies, significantly improving fault diagnosis accuracy, avoiding misjudgments, and accurately locating faults caused by user habits. This not only optimizes the user experience and provides solutions to habit-related issues, but also facilitates software upgrades and provides developers with guidance for improvement. Furthermore, this allows technicians to efficiently troubleshoot problems, reduce maintenance costs, and comprehensively improve the reliability and practicality of applications and devices.
[0036] 2. The present invention simulates and models the internal structure of the tablet computer and verifies its coordinate information. It can accurately map the fault information in the actual physical space of the tablet computer to the corresponding position of the three-dimensional model, so that technicians can intuitively see the specific location of the fault in the equipment and quickly locate the problem. In addition, the converted coordinate information can be integrated with other relevant data (such as fault history data, component specification data, etc.), providing a basis for further data analysis and mining, and helping to discover potential fault modes and patterns.
[0037] 3. This invention utilizes a quantum entanglement algorithm and annealing mechanism to accurately capture the complete operational state of a device at the same moment, avoiding data bias caused by acquisition time differences and significantly improving the accuracy of fault diagnosis. Quantum random numbers fine-tune acquisition intervals, imbuing monitoring with randomness and effectively detecting sudden faults. Combined with quantum annealing, this optimized path reduces redundant acquisitions, making monitoring more efficient and providing strong support for rapidly locating fault root causes and providing timely warnings. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 This is a flow chart of the tablet computer fault monitoring method;
[0039] Figure 2 Schematic diagram of abnormality monitoring steps in the affective computing model of the present invention;
[0040] Figure 3 This is the module diagram of the tablet computer fault monitoring system. DETAILED DESCRIPTION
[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0042] Example:
[0043] Please see the attached Figure 1 - Attachment Figure 2 , an embodiment of the present invention provides a tablet computer fault monitoring method, comprising the following steps:
[0044] S1: Preparation for Adaptive Data Collection
[0045] Adaptively adjust hardware monitoring device initialization parameters based on the tablet's current operating mode, such as power saving or high-performance mode. At the same time, an intelligent implantation algorithm prioritizes the implantation of software monitoring code in high-frequency and core applications.
[0046] S2: Quantum random multi-source data acquisition
[0047] Introducing a quantum random number generation mechanism, fine-tuning the hardware parameter collection time interval, and using blockchain technology to encrypt and store software operation data, including application startup time and crash counts, in a distributed ledger.
[0048] S3: GAN data comparison abnormal warning
[0049] Use artificial intelligence to conduct data comparison against the generative adversarial network. The generator simulates the normal data distribution, and the discriminator compares the real-time data. If the difference is too large, an abnormal warning will be triggered.
[0050] S4: DNA algorithm for accurate fault diagnosis
[0051] Adopting a fault diagnosis algorithm based on DNA computing, the fault rules and data are encoded into DNA sequences, simulating biochemical reactions and determining the fault type;
[0052] S5: Fault report generation and notification
[0053] Generate fault reports using virtual reality technology and create 3D models based on the current tablet device model. Abnormal information is marked in the established 3D model in a flashing red highlight.
[0054] S6: Quantum Encryption Data Backup and Maintenance
[0055] Quantum encryption technology is used to encrypt monitoring data into quantum state storage. Machine learning algorithms identify redundant and invalid data, and regular quantum error correction operations are performed to ensure data integrity.
[0056] In the preparation of the adaptive data collection, it is necessary to pre-establish an operating mode-parameter mapping table based on the actual computer parameter performance, and adjust the optimal parameter configuration of each hardware monitoring device under different operating modes in real time through the operating mode-parameter mapping table.
[0057] The quantum random multi-source data acquisition process leverages quantum entanglement to correlate the acquisition of multiple hardware parameters. By creating a quantum entangled state, the acquisition of different hardware parameters is synchronized at the quantum level. When one parameter is acquired, the acquisition of other entangled parameters is also triggered.
[0058] The quantum random multi-source data acquisition adopts the quantum annealing algorithm to optimize the acquisition path. The quantum annealing algorithm simulates the annealing process of the quantum system to find the optimal path for data acquisition and quickly converge to the optimal solution, thereby reducing unnecessary data collection points and reducing the generation of redundant data. In view of the above content, the following algorithm is proposed:
[0059] Determine the set of related parameters: First, the system analyzes the hardware architecture of the tablet and the potential connections between the hardware parameters to determine the set of hardware parameters that need to be collected for quantum entanglement correlation. For example, CPU temperature, memory usage, and battery current are selected as three parameters that have a significant impact on the system's operating status and are interrelated.
[0060] 1. Quantum entangled state construction and hardware parameter setting
[0061] After determining the associated hardware parameters, assign qubits to each parameter. Taking the construction of a two-bit Bell state for correlating CPU temperature and memory usage as an example, first apply a Hadamard gate to the qubit q1 representing the CPU temperature, that is, So that q1 becomes Then, using q1 as the control bit, a CNOT gate is applied to the quantum bit q2 representing the memory usage rate. The CNOT gate matrix is Eventually, an entangled state is formed Realize acquisition synchronization association;
[0062] 2. Fine-tuning of quantum random number generation and collection time intervals
[0063] The quantum random number generator generates random numbers based on quantum uncertainty; assuming that the preset CPU temperature collection interval range is t 1min -t 1max , the memory usage collection interval range is t 2min -t 2max The generated quantum random number sequence is converted to obtain the CPU temperature interval fine-tuning value Δt1 and the memory usage interval fine-tuning value Δt2. The actual collection intervals are t1 = t1base + Δt1, t2 = t 2基础 +Δt2, giving randomness to the collection.
[0064] 3. Quantum annealing algorithm initialization and acquisition path problem modeling
[0065] Set the hardware parameter collection point as the node construction graph, the objective function is:
[0066] E=w1I+w2T+w3R
[0067] Where I is the collection data integrity index, T is the collection data timeliness index, R is the resource consumption index, w1, w2, w3 are weight coefficients. Map the problem to the Ising model, the node spin variable σ i (±1) indicates whether the node data is collected, and the interaction strength between nodes J ij Reflects the degree of correlation, external magnetic field strength h i Represents the acquisition preference, and the objective function corresponds to the model energy function:
[0068]
[0069] 4. Quantum Annealing Process Execution and Optimal Path Solution
[0070] Initial Hamiltonian H0 = -∑ i h i σ i , target Hamiltonian H1=-∑ i,j J ij σi σ j -∑ i h i σ i , the system Hamiltonian H(s)=(1-s)H0+sH1 evolves with s(0 to 1). The system updates the spin variable σ by quantum tunneling effect i state, multiple iterations converge to the σ corresponding to the global minimum of the objective function i Configuration, determine the optimal collection path, and reduce redundant collection points.
[0071] In the GAN data comparison anomaly warning step, an emotional computing model is introduced to analyze the potential connection between user usage habits and data anomalies. By monitoring the user's interaction frequency, operation duration, and operation process data between the user and the application, the user's emotional tendency is judged. When a data anomaly occurs when a user frequently uses an application, the system will combine the user's emotional data to conduct a more in-depth anomaly assessment to determine whether the fault is caused by the user's specific usage habits. Based on the user's emotional data and the abnormality of the application data, it is determined whether the fault is caused by the user's specific usage habits. The following anomaly judgment algorithm is proposed:
[0072] Abnormal evaluation calculation:
[0073] 1. Data collection and preprocessing: Comprehensively collect user-application interaction data (interaction frequency, operation duration, operation process data) and application data anomaly information. Utilize data cleaning techniques to remove noise and outliers, and standardize and normalize the data to an appropriate range, laying the foundation for subsequent analysis.
[0074] 2. Feature extraction and quantification: Extract representative features and quantify them. Interaction frequency is measured as the number of interactions per unit time, operation duration is the average of each or the total duration, and the operation process is converted into a numerical range. Data anomalies are quantified based on severity, such as setting quantitative values based on error type and impact range.
[0075] 3. Sentiment tendency calculation:
[0076] Linear combination method: The emotional tendency value E is calculated using the formula E = w1f + w2t + w3p. Where f is the interaction frequency, t is the operation duration, p is the operation process, w1, w2, and w3 are weights, and w1 + w2 + w3 = 1. The weights are set according to the importance of the data.
[0077] Neural network method: Construct a multi-layer perceptron (MLP). The input layer receives the pre-processed interaction data, and after the hidden layer nonlinear transformation (such as ReLU activation function: f(x) = max(0,x)), the output layer obtains the sentiment tendency value. Through the back propagation algorithm (based on gradient descent, adjust the weight W and bias b: α is the learning rate, L is the loss function) and continuously optimizes the model;
[0078] The above two methods are calculated synchronously, and the weighted average parameter between the two is taken as the reference parameter of emotional tendency;
[0079] 4. Threshold setting and comparison: Set the sentiment threshold T and the minimum data anomaly threshold A based on historical data and business experience. min , compare the calculated E with T, and the quantified A with A m in comparison.
[0080] 5. Comprehensive judgment and decision-making: If E>T and A>A min , it is determined that the fault may be caused by the user's specific habits, and the user's usage patterns and historical data are deeply analyzed to determine the cause; if the conditions are not met, it is judged that the fault is caused by software vulnerabilities, hardware failures, etc., and other diagnostic methods are used.
[0081] The DNA algorithm's precise fault diagnosis combines the concept of gene editing technology to dynamically edit and optimize the DNA coding sequence of fault rules. When faced with new fault types or complex fault scenarios, the system will simulate the gene editing process to cut, reorganize and add new base codes to the existing fault rule DNA sequence to update the fault diagnosis rules.
[0082] In the fault report generation and notification, for hardware faults, the location of the faulty component and fault status information will be presented in a three-dimensional model; for software faults, the software operation process will be dynamically demonstrated to show the link where the fault occurred.
[0083] Please see the attached Figure 3 , a tablet computer fault monitoring system, including the following modules:
[0084] Operation mode perception and parameter adaptation module: Built-in multiple sensors and analysis logic, it can identify the tablet's operation mode in real time, automatically call the preset adaptation strategy to adjust the hardware monitoring device parameters including sensitivity and sampling frequency, and intelligently select and accurately embed software monitoring code into the monitoring application;
[0085] Quantum randomness and blockchain fusion acquisition module: The quantum random number generator is used to give randomness to hardware parameter acquisition, ensuring the comprehensiveness and timeliness of data; the distributed ledger and encryption technology of the blockchain are used to securely store software operation data to prevent data tampering and loss;
[0086] GAN intelligent comparison and warning module: This module integrates a generative adversarial network model to continuously learn the characteristics and patterns of normal data, compares monitoring data in real time, and quickly triggers an early warning mechanism upon detecting abnormal deviations, marking the source and type of abnormal data.
[0087] DNA computing diagnostic engine module: This module uses DNA computing principles to convert fault rules and monitoring data into biological coding information. By simulating biochemical reactions and performing high-speed parallel computing, it can quickly and accurately diagnose the fault type and root cause.
[0088] VR visualization report generation and push module: This module uses virtual reality technology to create a three-dimensional model that corresponds to the physical structure and software architecture of the tablet computer, displays fault information on the model with intuitive visual effects, and automatically generates and pushes reports to designated recipients.
[0089] Quantum encryption data management module: uses quantum key encryption technology to encrypt and store monitoring data, uses machine learning algorithms to regularly clean up redundant data, and performs quantum error correction operations to ensure data security and reliability.
[0090] The operating mode perception and parameter adaptation module has self-learning capabilities. By continuously collecting and analyzing equipment performance data under different operating modes, it automatically optimizes the operating mode-parameter mapping table to improve the accuracy and adaptability of parameter adjustment.
[0091] The VR visualization report generation and push module allows users to trace faults within the 3D model. By clicking on a marked anomaly, the system displays other components and modules that may be affected by the anomaly, as well as the path and related historical data of the fault. The following algorithm is used to calculate and calibrate the coordinates of the established 3D model:
[0092] Establishment of position coordinates and model coordinate system
[0093] The physical space of the tablet corresponds to the world coordinate system, and the location of the fault information is represented by the world coordinate (x w ,y w ,z w ) indicates that it is determined based on the actual length, width, and height of the tablet. For example, with the lower left corner of the tablet as the coordinate origin, the x, y, and z axes are established along the length, width, and height directions respectively;
[0094] The three-dimensional model used for display is constructed in the model coordinate system, and the coordinates are expressed as (x m ,y m ,z m The model coordinate system is set according to the modeling software or VR development framework. The model center may be the origin, and the coordinate axis direction is related to the spatial orientation of the model design.
[0095] Translation Transformation
[0096] In order to make the coordinates correspond to the model coordinates, a translation operation is required. Let the translation vector be (t x,t y ,t z ), the translation formula is:
[0097]
[0098] For example, if the tablet's fault point in world coordinates is (10, 20, 30), and measurement reveals that the model needs to be translated by 5, -3, and 2 units in the x, y, and z directions, respectively, to align the fault point with the model, then the position of this point in model coordinates is (10 + 5, 20 - 3, 30 + 2) = (15, 17, 32).
[0099] Rotation Transformation
[0100] Considering the difference between the coordinate axis direction of the coordinate system and the model coordinate system, a rotation operation is required. Assume that the rotation angles around the x, y, and z axes are α, β, and γ respectively, and the rotation matrix R x (α), R y (β), R z (γ) implementation.
[0101] Rotation matrix R around the x-axis x (α) is:
[0102]
[0103] Rotation matrix R around the y-axis y (β) is:
[0104]
[0105] Rotation matrix R around the z axis z (γ) is:
[0106]
[0107] The comprehensive rotation matrix R = R z (γ)R y (β)R x (α), the translated coordinate (x m ,y m ,z m ) is converted to the rotated coordinate (x′ m ,y′ m ,z′ m ), the formula is:
[0108]
[0109] Scaling Transform
[0110] Since the physical size in the world coordinate system may be different from the size ratio in the model coordinate system, scaling operation is required. Suppose the scaling factors along the x, y, and z axes are s respectively. x 、s y 、s z , the scaling matrix S is:
[0111]
[0112] The final transformed model coordinates (x f ,y f ,z f ) formula is:
[0113]
[0114] The algorithm changes described above ensure that the built 3D model matches the tablet's actual physical dimensions and spatial layout, accurately mapping the corresponding anomaly information to the 3D model's coordinate system. Furthermore, in a VR environment, the coordinate transformation algorithm ensures that users can accurately view the tablet's structure and fault information regardless of their viewing angle. Users can view the model from different angles and distances without distortion or misalignment of fault information, enabling technicians to comprehensively and meticulously observe and analyze faults, leading to a more accurate assessment of their nature and impact.
[0115] 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 computer fault monitoring method, characterized in that: The following steps are involved: S1: Adaptive Data Collection Preparation Adaptively adjust hardware monitoring device initialization parameters based on the tablet's current operating mode, such as power saving or high-performance mode. At the same time, an intelligent implantation algorithm prioritizes the implantation of software monitoring code in high-frequency and core applications. S2: Quantum random multi-source data acquisition Introducing a quantum random number generation mechanism, fine-tuning the hardware parameter collection time interval, and using blockchain technology to encrypt and store software operation data, including application startup time and crash counts, in a distributed ledger. S3: GAN data comparison abnormal warning Use artificial intelligence to conduct data comparison against the generative adversarial network. The generator simulates the normal data distribution, and the discriminator compares the real-time data. If the difference is too large, an abnormal warning will be triggered. S4: DNA algorithm for accurate fault diagnosis Adopting a DNA computing-based fault diagnosis algorithm, the fault rules and data are encoded into DNA sequences, simulating biochemical reactions, and determining the fault type; S5: Fault report generation and notification Generate fault reports using virtual reality technology and create 3D models based on the current tablet device model. Abnormal information is marked in the established 3D model in a flashing red highlight. S6: Quantum Encryption Data Backup and Maintenance Quantum encryption technology is used to encrypt monitoring data into quantum state storage. Machine learning algorithms identify redundant and invalid data, and regular quantum error correction operations are performed to ensure data integrity.
2. A tablet computer fault monitoring method according to claim 1, characterized in that: In the preparation of the adaptive data collection, it is necessary to pre-establish an operation mode-parameter mapping table according to the actual computer parameter performance, and adjust the optimal parameter configuration of each hardware monitoring device under different operation modes in real time through the operation mode-parameter mapping table.
3. A tablet computer fault monitoring method according to claim 1, characterized in that: The quantum random multi-source data acquisition process leverages quantum entanglement to correlate the acquisition of multiple hardware parameters. By creating a quantum entangled state, the acquisition of different hardware parameters is synchronized at the quantum level. When one parameter is acquired, the acquisition of other entangled parameters is also triggered.
4. A tablet computer fault monitoring method according to claim 3, characterized in that: The quantum random multi-source data acquisition adopts the quantum annealing algorithm to optimize the acquisition path. The quantum annealing algorithm simulates the annealing process of the quantum system to find the optimal path for data acquisition and quickly converge to the optimal solution, thereby reducing unnecessary data collection points and reducing the generation of redundant data.
5. The tablet computer fault monitoring method according to claim 1, characterized in that: The GAN data comparison anomaly warning step incorporates an emotional computing model to analyze the potential connection between user usage habits and data anomalies. By monitoring user interaction frequency, operation duration, and process data between the user and the application, the user's emotional tendencies are determined. When data anomalies occur due to frequent user use of an application, the system combines the user's emotional data for a more in-depth anomaly assessment to determine whether the problem is caused by the user's specific usage habits.
6. A tablet computer fault monitoring method according to claim 1, characterized in that: The DNA algorithm's precise fault diagnosis combines the concept of gene editing technology to dynamically edit and optimize the DNA coding sequence of fault rules. When faced with new fault types or complex fault scenarios, the system will simulate the gene editing process to cut, reorganize and add new base codes to the existing fault rule DNA sequence to update the fault diagnosis rules.
7. A tablet computer fault monitoring method according to claim 1, characterized in that: In the fault report generation and notification, for hardware faults, a 3D model will be presented including the location of the faulty component and fault status information; For software failures, the software operation process is dynamically demonstrated to show the link where the failure occurs.
8. The tablet computer fault monitoring system according to claim 1, characterized in that: Includes the following modules: Operation mode perception and parameter adaptation module: Built-in multiple sensors and analysis logic, it can identify the tablet's operation mode in real time, automatically call the preset adaptation strategy to adjust the hardware monitoring device parameters including sensitivity and sampling frequency, and intelligently select and accurately embed software monitoring code into the monitoring application; Quantum randomness and blockchain fusion acquisition module: The quantum random number generator is used to give randomness to hardware parameter acquisition, ensuring the comprehensiveness and timeliness of data; the distributed ledger and encryption technology of the blockchain are used to securely store software operation data to prevent data tampering and loss; GAN intelligent comparison and warning module: This module integrates a generative adversarial network model to continuously learn the characteristics and patterns of normal data, compares monitoring data in real time, and quickly triggers an early warning mechanism upon detecting abnormal deviations, marking the source and type of abnormal data. DNA computing diagnostic engine module: Utilizing DNA computing principles, it converts fault rules and monitoring data into biologically encoded information. By simulating biochemical reactions and performing high-speed parallel computing, it can quickly and accurately diagnose the type and root cause of faults. VR visualization report generation and push module: This module uses virtual reality technology to create a three-dimensional model that corresponds to the physical structure and software architecture of the tablet computer, displays fault information on the model with intuitive visual effects, and automatically generates and pushes reports to designated recipients. Quantum encryption data management module: uses quantum key encryption technology to encrypt and store monitoring data, uses machine learning algorithms to regularly clean up redundant data, and performs quantum error correction operations to ensure data security and reliability.
9. The tablet computer fault monitoring system according to claim 8, characterized in that: The operating mode perception and parameter adaptation module has self-learning capabilities. By continuously collecting and analyzing equipment performance data under different operating modes, it automatically optimizes the operating mode-parameter mapping table to improve the accuracy and adaptability of parameter adjustment.
10. The tablet computer fault monitoring system according to claim 8, characterized in that: The VR visualization report generation and push module allows users to perform fault tracing operations in the three-dimensional model. By clicking on the marked abnormal information, the system can display other components and modules that may be affected by the abnormality, as well as the path of the fault and related historical data.