Touch screen multi-mode interaction fault prediction and maintenance system based on intelligent diagnosis

Abstract

The invention discloses a touch screen multi-modal interaction fault prediction and maintenance system based on intelligent diagnosis, and the system employs a multi-modal perception fusion unit to fuse the multi-dimensional perception data of a touch screen in quantum touch interaction, neural gesture recognition, electroencephalogram voice cooperation and iris eye movement tracking interaction modes, overcomes the limitation of single-modal data, and improves the accuracy of the multi-modal interaction fault prediction and maintenance of the touch screen. Quantum Fourier transform and a quantum principal component analysis algorithm are used for carrying out quantum state feature extraction on fused data, the feature extraction efficiency and accuracy are improved, the uniqueness of fault features is enhanced through quantum entanglement, different fault modes can be distinguished, and then the fault prediction accuracy is improved. A fault prediction value is calculated based on a quantum fault prediction formula, a weight coefficient is dynamically adjusted to adapt to different fault modes and data changes, a maintenance strategy comprehensively considering a plurality of target factors is generated by applying a quantum multi-objective optimization algorithm according to the prediction value and a preset threshold value, and optimal configuration of maintenance resources is realized.

Description

Technical Field

[0001] The present invention relates to the field of touch screen fault prediction and maintenance technology, and in particular to a touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis. Background Art

[0002] With the rapid development of science and technology, touch screens, as an important interface for human-computer interaction, are evolving towards multimodality. Emerging interaction modes such as quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking are gradually being applied to the touch screen field, bringing users a more natural, efficient, and personalized interaction experience.

[0003] Traditional touch screen fault prediction and maintenance methods are mainly based on classic signal processing and machine learning algorithms. These methods have achieved certain results when processing single-modal perception data, but their limitations gradually become apparent when faced with multimodal fusion data. On the one hand, traditional methods have difficulty handling the complex relationships and high-dimensional features between multimodal data. Multimodal data contains rich information, but it also brings problems of data redundancy and noise interference. Traditional methods often cannot fully explore the potential connections between these data, resulting in inaccurate fault feature extraction and low prediction accuracy. On the other hand, traditional methods have poor adaptability. In actual applications, the working environment and usage conditions of touch screens will continue to change, and the failure mode will also change accordingly. Traditional methods usually require manual adjustment of model parameters and feature selection, which is difficult to adapt to such dynamic changes, resulting in insufficient timeliness and effectiveness of maintenance strategies. Summary of the Invention

[0004] In view of this, the present invention proposes a touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis, which can effectively solve the defects of the existing technology, such as the difficulty in processing the complex relationships and high-dimensional features between multimodal data, resulting in inaccurate fault feature extraction, and the poor adaptability, resulting in insufficient timeliness and effectiveness of maintenance strategies.

[0005] The technical solution of the present invention is achieved as follows:

[0006] A multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis, including:

[0007] Multimodal perception fusion unit, used to fuse the multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes;

[0008] The fault feature quantization extraction unit is used to extract quantum state features from the fused multimodal sensing data using quantum Fourier transform and quantum principal component analysis algorithms, mapping the classical features to quantum Hilbert space to obtain a fault feature vector with quantum entanglement characteristics;

[0009] A dynamic weight adaptive fault prediction unit is used to calculate the fault feature vector with quantum entanglement characteristics based on the quantum fault prediction formula to obtain a fault prediction value;

[0010] A multi-objective optimization maintenance decision-making unit is used to generate a maintenance strategy based on the quantum fault prediction value and the preset quantum fault level threshold using a quantum multi-objective optimization algorithm. The maintenance strategy includes a quantum maintenance time window, a quantum maintenance operation sequence, a quantum maintenance resource allocation, and a quantum spare parts scheduling plan.

[0011] As a further optional solution of the touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis, the multimodal perception fusion unit includes:

[0012] Quantum touch sensor array, which uses quantum tunneling effect to collect pressure distribution and position information of quantum touch interaction;

[0013] Neural electrical signal acquisition electrodes, which collect neural electrical signals during neural gesture recognition through implantable or surface electrodes;

[0014] The EEG and speech collaborative acquisition device combines an EEG acquisition cap and a microphone array to synchronously collect EEG and speech signals and analyze their collaborative features;

[0015] The iris eye tracking quantum camera uses quantum imaging technology to capture changes in iris texture and eye movement trajectories.

[0016] As a further optional solution for the touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis, the quantum Fourier transform and quantum principal component analysis algorithm are used to extract quantum state features from the fused multimodal perception data, specifically including:

[0017] Perform quantum state encoding on the fused multimodal perception data and map it to quantum bits;

[0018] Use quantum gate circuits to realize quantum Fourier transform and convert classical features into frequency domain quantum states;

[0019] Quantum principal component analysis is implemented through quantum phase estimation and quantum singular value decomposition to extract the eigenvector with the largest quantum variance.

[0020] As a further optional solution to the touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis, the quantum fault prediction formula is specifically as follows:

[0021]

[0022] Among them, ψ q is the quantum characteristic state vector, a q is the quantum characteristic weight coefficient, E q (t) is the quantum environment interference energy function, t q0 and t q1 is the quantum time interval, b q is the quantum environment interference weight coefficient, p q-fault is the quantum failure probability p q-normal is the quantum normal probability, c q is the quantum probability weight coefficient, F q-pred is the predicted value of quantum failure.

[0023] As a further optional solution for the multimodal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis, the system also includes a holographic visualization interaction unit for displaying the changing trend of quantum fault prediction values, the specific content of quantum maintenance strategies, the quantum real-time status information of the touch screen, and quantum historical fault records, specifically:

[0024] Quantum holographic projection technology is used to display the changing trend of quantum fault prediction values over time, and different quantum fault levels are distinguished by the flashing frequencies of different colors and quantum states;

[0025] The specific contents of the quantum maintenance strategy are displayed in detail in the form of quantum tables, including quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation and quantum spare parts scheduling plan;

[0026] The touch screen's real-time quantum status is displayed using a combination of quantum status indicators and quantum text prompts. The quantum status indicators include quantum green for normal quantum operation, quantum yellow for quantum warning, and quantum red for quantum failure. The quantum text prompts display specific quantum failure information or quantum warning content.

[0027] The quantum histogram displays the historical quantum failure records, including the time when the quantum failure occurred, the type of quantum failure, and the time it took to handle the quantum failure.

[0028] As a further optional solution to the multimodal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis, the system also includes a distributed elastic storage unit for storing the collected multimodal perception data, quantum fault feature vectors, quantum fault prediction values, quantum maintenance strategies, and quantum full life cycle operation records of the touch screen on quantum storage nodes using a quantum distributed storage protocol.

[0029] As a further optional solution for the touch screen multimodal interactive fault prediction and maintenance system based on intelligent diagnosis, the system also includes a quantum secure communication remote monitoring unit, which transmits the touch screen's quantum fault prediction value, quantum maintenance strategy, quantum real-time status information, and quantum anomaly alarm information in real time to a remote quantum monitoring center through quantum key distribution and quantum teleportation technology.

[0030] A multimodal interactive fault prediction and maintenance method for touch screens based on intelligent diagnosis, specifically including:

[0031] Collect multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes;

[0032] Quantum Fourier transform and quantum principal component analysis algorithms are used to extract quantum state features from the fused multimodal sensing data, mapping the classical features to the quantum Hilbert space to obtain fault feature vectors with quantum entanglement characteristics.

[0033] Based on the quantum fault prediction formula, the fault feature vector with quantum entanglement characteristics is calculated to obtain the fault prediction value;

[0034] Determining the quantum fault level based on the quantum fault prediction value and a preset quantum fault level threshold;

[0035] A quantum multi-objective function is constructed by comprehensively considering the multi-objective factors of quantum state stability of the touch screen, quantum information security, quantum computing resource utilization, and quantum communication reliability;

[0036] A quantum multi-objective optimization algorithm is used to optimize and solve the quantum multi-objective function to obtain the optimal maintenance strategy. The maintenance strategy includes a quantum maintenance time window, a quantum maintenance operation sequence, a quantum maintenance resource allocation, and a quantum spare parts scheduling plan.

[0037] The beneficial effects of the present invention are: by integrating the multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG voice collaboration and iris eye tracking interaction modes through a multimodal perception fusion unit, the limitations of single modal data can be avoided, and the accuracy and reliability of fault detection can be improved. The quantum Fourier transform and quantum principal component analysis algorithms are used to extract quantum state features of the fused multimodal perception data, and the classical features can be mapped to the quantum Hilbert space to obtain fault feature vectors with quantum entanglement characteristics. The quantum algorithm has powerful parallel computing capabilities and can process large amounts of data in a short time to extract more representative fault features. Compared with traditional feature extraction methods, the quantum algorithm can explore the potential relationships between data and improve the efficiency and accuracy of feature extraction. The fault feature vectors with quantum entanglement characteristics can better reflect the differences between different fault modes. Quantum entanglement creates a special correlation between feature vectors. This correlation can enhance the uniqueness of the fault features, making different faults easier to distinguish in the feature space, which helps to improve the accuracy of fault prediction and reduce In order to reduce the number of misjudgments and missed judgments, the fault feature vector with quantum entanglement characteristics is calculated based on the quantum fault prediction formula to obtain an accurate fault prediction value. The quantum fault prediction formula comprehensively considers multiple factors and can adapt to different fault modes and data changes by dynamically adjusting the weight coefficient, thereby improving the accuracy and stability of the prediction. Based on the quantum fault prediction value and the preset quantum fault level threshold, the quantum multi-objective optimization algorithm is used to generate a maintenance strategy, which can comprehensively consider multiple target factors such as quantum state stability, quantum information security, quantum computing resource utilization, and quantum communication reliability. The quantum multi-objective optimization algorithm can find a balance point between multiple goals and generate the optimal maintenance strategy, avoiding the limitations brought by single-target optimization. The generated maintenance strategy includes quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation, and quantum spare parts scheduling plan, which can achieve optimal configuration of maintenance resources. By reasonably arranging maintenance time and operation sequence, the impact on the normal use of the touch screen can be reduced. Accurate resource allocation and spare parts scheduling can reduce maintenance costs and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 Schematic diagram of the components of the multimodal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to the present invention;

[0040] Figure 2 Schematic diagram of the flow of the touch screen multimodal interactive fault prediction and maintenance method based on intelligent diagnosis of the present invention. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. 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.

[0042] refer to Figures 1 to 2 , a multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis, including:

[0043] Multimodal perception fusion unit, used to fuse the multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes;

[0044] The fault feature quantization extraction unit is used to extract quantum state features from the fused multimodal sensing data using quantum Fourier transform and quantum principal component analysis algorithms, mapping the classical features to quantum Hilbert space to obtain a fault feature vector with quantum entanglement characteristics;

[0045] A dynamic weight adaptive fault prediction unit is used to calculate the fault feature vector with quantum entanglement characteristics based on the quantum fault prediction formula to obtain a fault prediction value;

[0046] A multi-objective optimization maintenance decision-making unit is used to generate a maintenance strategy based on the quantum fault prediction value and the preset quantum fault level threshold using a quantum multi-objective optimization algorithm. The maintenance strategy includes a quantum maintenance time window, a quantum maintenance operation sequence, a quantum maintenance resource allocation, and a quantum spare parts scheduling plan.

[0047] In this embodiment, the multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG voice collaboration and iris eye tracking interaction modes are integrated through a multi-modal perception fusion unit, which can avoid the limitations of single modal data and improve the accuracy and reliability of fault detection. The quantum Fourier transform and quantum principal component analysis algorithms are used to extract quantum state features of the fused multi-modal perception data, and the classical features can be mapped to the quantum Hilbert space to obtain a fault feature vector with quantum entanglement characteristics. The quantum algorithm has a powerful parallel computing capability and can process a large amount of data in a short time to extract more representative fault features. Compared with traditional feature extraction methods, the quantum algorithm can explore the potential relationship between data and improve the efficiency and accuracy of feature extraction. The fault feature vector with quantum entanglement characteristics can better reflect the differences between different fault modes. Quantum entanglement creates a special correlation between feature vectors. This correlation can enhance the uniqueness of the fault feature, making different faults easier to distinguish in the feature space, which helps to improve the accuracy of fault prediction and reduce In the case of misjudgment and missed judgment, the fault feature vector with quantum entanglement characteristics is calculated based on the quantum fault prediction formula to obtain accurate fault prediction values. The quantum fault prediction formula comprehensively considers multiple factors and can adapt to different fault modes and data changes by dynamically adjusting the weight coefficient, thereby improving the accuracy and stability of the prediction. Based on the quantum fault prediction value and the preset quantum fault level threshold, the quantum multi-objective optimization algorithm is used to generate a maintenance strategy, which can comprehensively consider multiple target factors such as quantum state stability, quantum information security, quantum computing resource utilization, and quantum communication reliability. The quantum multi-objective optimization algorithm can find a balance point between multiple goals and generate the optimal maintenance strategy, avoiding the limitations brought by single-target optimization. The generated maintenance strategy includes quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation, and quantum spare parts scheduling plan, which can achieve optimal configuration of maintenance resources. By reasonably arranging maintenance time and operation sequence, the impact on the normal use of the touch screen can be reduced. Accurate resource allocation and spare parts scheduling can reduce maintenance costs and improve resource utilization efficiency.

[0048] Preferably, the multimodal perception fusion unit includes:

[0049] Quantum touch sensor array, which uses quantum tunneling effect to collect pressure distribution and position information of quantum touch interaction;

[0050] Neural electrical signal acquisition electrodes, which collect neural electrical signals during neural gesture recognition through implantable or surface electrodes;

[0051] The EEG and speech collaborative acquisition device combines an EEG acquisition cap and a microphone array to synchronously collect EEG and speech signals and analyze their collaborative features;

[0052] The iris eye tracking quantum camera uses quantum imaging technology to capture changes in iris texture and eye movement trajectories.

[0053] In this embodiment, the multi-dimensional perception data collected by the quantum touch sensor array, neural electrical signal acquisition electrodes, EEG speech collaborative acquisition equipment and iris eye tracking quantum camera are integrated, which can fully utilize the complementarity between different modal information and provide more comprehensive and accurate user interaction information. This multimodal fusion method can overcome the limitations of single modal information and improve the accuracy and reliability of human-computer interaction. The rich perception data collected by the multimodal perception fusion unit provides sufficient data basis for the fault feature quantization extraction unit, the dynamic weight adaptive fault prediction unit and the multi-objective optimization maintenance decision unit. By analyzing and processing these data, the fault of the touch screen can be predicted more accurately, a more scientific and reasonable maintenance strategy can be formulated, and the reliability and stability of the touch screen can be improved.

[0054] Preferably, the method of extracting quantum state features from the fused multimodal perception data using quantum Fourier transform and quantum principal component analysis algorithms specifically includes:

[0055] Perform quantum state encoding on the fused multimodal perception data and map it to quantum bits;

[0056] Use quantum gate circuits to realize quantum Fourier transform and convert classical features into frequency domain quantum states;

[0057] Quantum principal component analysis is implemented through quantum phase estimation and quantum singular value decomposition to extract the eigenvector with the largest quantum variance.

[0058] In this embodiment, the fused multimodal sensing data is quantum-encoded and mapped to quantum bits, which can utilize the superposition characteristics of quantum bits to represent more data states in an exponential manner. Unlike traditional classical bits that can only represent two states, 0 or 1, quantum bits can be in a superposition of multiple states at the same time, which greatly improves the efficiency of data storage and representation. Quantum state encoding can better capture the complex correlations between multimodal perception data. Multimodal data often contains information from different sources, and there are nonlinear and implicit correlations between them. The entanglement characteristics of quantum states allow different quantum bits to be correlated with each other, which can more naturally represent this complex relationship between multimodal data; quantum Fourier transform is implemented through quantum gate circuits, which can convert classical features into frequency domain quantum states. In the frequency domain, the characteristics of the data are often more obvious. Some laws and patterns that are difficult to find in the time domain can be clearly displayed in the frequency domain. For example, for fault signals with periodic or frequency characteristics, after being converted to the frequency domain through quantum Fourier transform, their frequency components will be more prominent, which is convenient for subsequent feature extraction and fault identification. Quantum gate circuits implement quantum Fourier transform with powerful parallel computing capabilities. Compared with traditional Fourier transform algorithms, quantum Fourier transform can process large amounts of data in a shorter time. Quantum principal component analysis can process a large amount of data and perform transformation operations on multiple data points simultaneously, greatly improving computational efficiency and enabling rapid acquisition of frequency domain features in fault prediction scenarios with high real-time requirements. Quantum principal component analysis, through quantum phase estimation and quantum singular value decomposition, can extract eigenvectors with the largest quantum variance. These eigenvectors represent the most important information in the data and remove redundancy and noise components. Multimodal perception data often contains a large amount of irrelevant information and noise. Quantum principal component analysis can focus on key features and improve the accuracy of fault feature extraction. For example, in multimodal data from touch screens, there may be only a few eigenvectors that are closely related to the occurrence of faults. Quantum principal component analysis can accurately extract these key features. While extracting key eigenvectors, quantum principal component analysis also reduces data dimensionality. High-dimensional data often leads to increased computational complexity and the "curse of dimensionality" problem. Dimensionality reduction can reduce the amount of computation and improve algorithm efficiency. At the same time, the reduced-dimensional data is easier to visualize and analyze, helping to better understand the inherent structure of the data and fault modes.

[0059] Preferably, the quantum failure prediction formula is specifically:

[0060]

[0061] Among them, ψ q is the quantum characteristic state vector, a q is the quantum characteristic weight coefficient, E q (t) is the quantum environment interference energy function, tq0 and t q1 is the quantum time interval, b q is the quantum environment interference weight coefficient, p q-fault is the quantum failure probability p q-normal is the quantum normal probability, c q is the quantum probability weight coefficient, F q-pred is the predicted value of quantum failure.

[0062] In this embodiment, by introducing the quantum feature weight vector, fault prediction can be performed based on the characteristics of a specific system, thereby improving the targeted nature of the prediction. By integrating the environmental interference energy over a period of time, the impact of the environment on the system can be quantified, making the prediction more comprehensive. By integrating the environmental interference energy over a period of time, historical data can be fully utilized to explore the potential relationship between environmental interference and faults, thereby improving the accuracy of the prediction.

[0063] It should be noted that the quantum weight coefficient a q 、b q 、c q Determined by:

[0064] Construct a quantum training dataset containing historical quantum failure data, corresponding quantum multimodal perception data, and the actual time of quantum failure occurrence;

[0065] The quantum variational algorithm is used to optimize the quantum weight coefficients, with minimizing the quantum prediction error as the objective function.

[0066] Preferably, the system further includes a holographic visualization interactive unit for displaying the changing trend of quantum fault prediction values, the specific content of quantum maintenance strategies, the quantum real-time status information of the touch screen, and the quantum historical fault records, specifically:

[0067] Quantum holographic projection technology is used to display the changing trend of quantum fault prediction values over time, and different quantum fault levels are distinguished by the flashing frequencies of different colors and quantum states;

[0068] The specific contents of the quantum maintenance strategy are displayed in detail in the form of quantum tables, including quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation and quantum spare parts scheduling plan;

[0069] The touch screen's real-time quantum status is displayed using a combination of quantum status indicators and quantum text prompts. The quantum status indicators include quantum green for normal quantum operation, quantum yellow for quantum warning, and quantum red for quantum failure. The quantum text prompts display specific quantum failure information or quantum warning content.

[0070] The quantum histogram displays the historical quantum failure records, including the time when the quantum failure occurred, the type of quantum failure, and the time it took to handle the quantum failure.

[0071] In this embodiment, quantum holographic projection technology is used to display the changing trend of quantum fault prediction values over time, so that complex data is presented in a three-dimensional, dynamic form. Different colors and quantum state flashing frequencies distinguish different quantum fault levels. This intuitive visual expression allows users to quickly understand the changes in fault prediction without in-depth interpretation of complex numerical data. By observing the changing trend, users can discover potential fault risks in advance and take preventive measures in time to avoid the occurrence of faults or mitigate the impact of faults. For example, when the predicted value begins to rise and the flashing frequency accelerates, it may indicate that a fault is about to occur, and the user can arrange maintenance work in advance; the specific content of the quantum maintenance strategy is displayed in detail in the form of a quantum table, including the quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation, and quantum spare parts scheduling plan. This structured display method makes the various information of the maintenance strategy clear at a glance, convenient for users to view and understand. The detailed maintenance strategy display helps relevant personnel to accurately perform maintenance operations according to plan, reasonably allocate resources, and ensure the smooth progress of maintenance work. For example, maintenance personnel can perform operations step by step according to the operation sequence, and managers can allocate resources according to resource allocation. The system can allocate human and material resources according to the situation; the quantum real-time status of the touch screen is displayed in a combination of quantum status indicator lights and quantum text prompts, which can provide instant feedback on the system's operating status. The quantum status indicator lights use different colors (green, yellow, and red) to intuitively indicate normal, warning, and fault states, and the quantum text prompts display specific fault information or warning content, allowing users to quickly understand the current status of the system. Real-time status feedback helps users to promptly discover and handle abnormal situations, thereby improving the system's response speed and reliability. For example, when the indicator light turns yellow, users can immediately view the text prompt to understand the cause of the warning and take corresponding measures; the quantum historical fault records are displayed through quantum histograms, including the time of quantum fault occurrence, quantum fault type, and quantum fault processing time. The histogram format makes historical fault data more intuitive, facilitating data analysis and comparison by users. The visual display of historical fault records helps users summarize the patterns and causes of fault occurrence, providing a reference for subsequent fault prediction and maintenance work. For example, by analyzing the distribution of fault occurrence time in the histogram, it can be found that the frequency of faults in certain time periods is higher, thereby strengthening monitoring and maintenance in that time period.

[0072] Preferably, the system further includes a distributed elastic storage unit for storing the collected multimodal perception data, quantum fault feature vectors, quantum fault prediction values, quantum maintenance strategies, and quantum full life cycle operation records of the touch screen on the quantum storage node using a quantum distributed storage protocol.

[0073] In this embodiment, the quantum distributed storage protocol usually stores data in a dispersed manner on multiple quantum storage nodes. This distributed storage method provides data redundancy backup. Even if one or some nodes fail, complete data copies are still stored on other nodes, thereby ensuring high data reliability and avoiding data loss due to single point failure. As the system runs, the amount of collected multimodal perception data, quantum fault prediction values, and other data will continue to increase. The distributed elastic storage unit can dynamically increase or decrease quantum storage nodes according to the growth of data volume, achieving elastic scaling to meet the system's demand for data storage capacity. When the system's business scale expands or shrinks, the demand for storage resources will also change accordingly. The elastic scaling capability of the distributed storage architecture enables the system to flexibly respond to business changes. There is no need for large-scale hardware upgrades or redeployment; quantum distributed storage allows multiple users or applications to access different copies of data at the same time, improving data access efficiency. For example, when performing fault prediction analysis, multiple analysis modules can read the required data from different storage nodes at the same time, speeding up the analysis. Through quantum properties such as quantum entanglement, quantum distributed storage protocols may achieve more efficient data transmission. When data needs to be migrated or synchronized between different nodes, the advantages of quantum communication can be utilized to reduce the time and bandwidth consumption of data transmission; distributed storage systems can set up strict access control mechanisms, and only authorized users or applications can access specific data, which helps protect sensitive data, such as quantum fault characteristic vectors, quantum maintenance strategies, etc., to prevent data leakage.

[0074] Preferably, the system also includes a quantum secure communication remote monitoring unit, which transmits the touch screen's quantum fault prediction value, quantum maintenance strategy, quantum real-time status information, and quantum anomaly alarm information in real time to a remote quantum monitoring center through quantum key distribution and quantum teleportation technology.

[0075] In this embodiment, quantum key distribution is based on the principles of quantum mechanics and can generate unconditionally secure keys. When transmitting sensitive information such as the quantum fault prediction value of the touch screen and the quantum maintenance strategy, quantum keys are used for encryption to ensure that the information is not stolen or cracked during transmission. Even if the attacker has powerful computing power, he cannot crack the information encrypted by the quantum key, thereby ensuring the security of the data. Quantum teleportation technology can transmit quantum state information to a remote location without directly transmitting the quantum state itself. This transmission method avoids the risk of information being intercepted and copied during transmission, further enhancing the security of information transmission; through quantum communication technology, real-time transmission of data such as the quantum real-time status information of the touch screen and quantum anomaly alarm information can be achieved, which enables the remote quantum monitoring center to obtain the latest status of the system in a timely manner and respond quickly to abnormal situations. The remote monitoring center can centrally monitor the operating status of multiple touch screen systems and obtain information such as the quantum fault prediction value and quantum maintenance strategy of each system in real time. This centralized monitoring method improves management efficiency and facilitates unified management and scheduling of multiple systems. The quantum secure communication remote monitoring unit enables technicians and managers in different locations to share information in real time and collaborate remotely. For example, when a touch screen system fails, remote experts can perform remote diagnosis and provide repair guidance through real-time transmitted information.

[0076] A multimodal interactive fault prediction and maintenance method for touch screens based on intelligent diagnosis, specifically including:

[0077] Collect multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes;

[0078] Quantum Fourier transform and quantum principal component analysis algorithms are used to extract quantum state features from the fused multimodal sensing data, mapping the classical features to the quantum Hilbert space to obtain fault feature vectors with quantum entanglement characteristics.

[0079] Based on the quantum fault prediction formula, the fault feature vector with quantum entanglement characteristics is calculated to obtain the fault prediction value;

[0080] Determining the quantum fault level based on the quantum fault prediction value and a preset quantum fault level threshold;

[0081] Considering multiple objective factors such as the quantum state stability, quantum information security, quantum computing resource utilization rate, and quantum communication reliability of the touch screen, a quantum multi-objective function is constructed;

[0082] The quantum multi-objective function is optimized and solved using a quantum multi-objective optimization algorithm to obtain an optimal maintenance strategy, which includes a quantum maintenance time window, a quantum maintenance operation sequence, quantum maintenance resource allocation, and a quantum spare part scheduling plan.

[0083] In this embodiment, based on the quantum fault prediction value F q-pred and a preset quantum fault level threshold, the quantum fault level is determined, specifically:

[0084] Set a first quantum preset threshold Tq1 and a second quantum preset threshold Tq2, and Tq1 < Tq2;

[0085] If F q-pred < Tq1, it is determined as a low quantum fault level;

[0086] If Tq1 ≤ F q-pred < Tq2, it is determined as a medium quantum fault level;

[0087] If F q-pred ≥ Tq2, it is determined as a high quantum fault level.

[0088] Considering multiple objective factors such as the quantum state stability, quantum information security, quantum computing resource utilization rate, and quantum communication reliability of the touch screen, a quantum multi-objective function is constructed, specifically:

[0089] Quantum state stability objective function: Using the fluctuation degree of the quantum state, the error rate of quantum bits, etc. as quantization indexes, a function is constructed to maximize the quantum state stability;

[0090] Quantum information security objective function: Considering the decryption probability of quantum keys, the leakage risk of quantum information, etc., a function is constructed to maximize the quantum information security;

[0091] Quantum computing resource utilization rate objective function: Using the completion time of quantum computing tasks, the idle rate of quantum computing resources, etc. as indexes, a function is constructed to maximize the quantum computing resource utilization rate;

[0092] Quantum communication reliability objective function: According to the bit error rate of quantum communication, the stability of quantum channels, etc., a function is constructed to maximize the quantum communication reliability;

[0093] The above four objective functions are integrated into a quantum multi-objective function.

[0094] The quantum multi-objective function is optimized and solved using a quantum multi-objective optimization algorithm to obtain an optimal maintenance strategy, specifically:

[0095] A quantum optimization algorithm combining quantum variational algorithm and quantum annealing algorithm is used to initialize the quantum bit state and encode the maintenance strategy variables onto the quantum bits.

[0096] Use quantum gate circuits to operate quantum bits, simulate quantum variation processes, and continuously adjust the quantum bit state to search for the optimal solution;

[0097] The quantum annealing mechanism is introduced to gradually lower the "temperature" of the quantum system so that the quantum bit state gradually converges to the optimal solution region of the quantum multi-objective function.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis, characterized in that: include: Multimodal perception fusion unit, used to fuse the multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes; The fault feature quantization extraction unit is used to extract quantum state features from the fused multimodal sensing data using quantum Fourier transform and quantum principal component analysis algorithms, mapping the classical features to quantum Hilbert space to obtain a fault feature vector with quantum entanglement characteristics; A dynamic weight adaptive fault prediction unit is used to calculate the fault feature vector with quantum entanglement characteristics based on the quantum fault prediction formula to obtain a fault prediction value; A multi-objective optimization maintenance decision-making unit is used to generate a maintenance strategy based on the quantum fault prediction value and the preset quantum fault level threshold using a quantum multi-objective optimization algorithm. The maintenance strategy includes a quantum maintenance time window, a quantum maintenance operation sequence, a quantum maintenance resource allocation, and a quantum spare parts scheduling plan.

2. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 1 is characterized in that: The multimodal perception fusion unit includes: Quantum touch sensor array, which uses quantum tunneling effect to collect pressure distribution and position information of quantum touch interaction; Neural electrical signal acquisition electrodes, which collect neural electrical signals during neural gesture recognition through implantable or surface electrodes; The EEG and speech collaborative acquisition device combines an EEG acquisition cap and a microphone array to synchronously acquire EEG and speech signals and analyze their collaborative features; The iris eye tracking quantum camera uses quantum imaging technology to capture changes in iris texture and eye movement trajectories.

3. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 2 is characterized in that: The quantum Fourier transform and quantum principal component analysis algorithm are used to extract quantum state features from the fused multimodal perception data, specifically including: Perform quantum state encoding on the fused multimodal perception data and map it to quantum bits; Use quantum gate circuits to realize quantum Fourier transform and convert classical features into frequency domain quantum states; Quantum principal component analysis is implemented through quantum phase estimation and quantum singular value decomposition to extract the eigenvector with the largest quantum variance.

4. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 3 is characterized in that: The quantum failure prediction formula is specifically: Among them, ψq is the quantum characteristic state vector, aq is the quantum characteristic weight coefficient, Eq(t) is the quantum environment interference energy function, tq0 and tq1 are quantum time intervals, bq is the quantum environment interference weight coefficient, pq-fault is the quantum fault probability, pq-normal is the quantum normal probability, cq is the quantum probability weight coefficient, and Fq-pred is the quantum fault prediction value.

5. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 4 is characterized in that: The system also includes a holographic visualization interactive unit for displaying the changing trend of quantum fault prediction values, the specific content of the quantum maintenance strategy, the quantum real-time status information on the touch screen, and the quantum historical fault records, specifically: Quantum holographic projection technology is used to display the changing trend of quantum fault prediction values over time, and different quantum fault levels are distinguished by different colors and quantum state flashing frequencies; The specific contents of the quantum maintenance strategy are displayed in detail in the form of quantum tables, including quantum maintenance time window, quantum maintenance operation sequence, quantum maintenance resource allocation and quantum spare parts scheduling plan; The touch screen's real-time quantum status is displayed using a combination of quantum status indicators and quantum text prompts. The quantum status indicators include quantum green for normal quantum operation, quantum yellow for quantum warning, and quantum red for quantum failure. The quantum text prompts display specific quantum failure information or quantum warning content. The quantum histogram displays the historical quantum failure records, including the time when the quantum failure occurred, the type of quantum failure, and the time it took to handle the quantum failure.

6. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 5 is characterized in that: The system also includes a distributed elastic storage unit for storing collected multimodal perception data, quantum fault feature vectors, quantum fault prediction values, quantum maintenance strategies, and quantum full life cycle operation records of the touch screen on quantum storage nodes using a quantum distributed storage protocol.

7. The multi-modal interactive fault prediction and maintenance system for touch screens based on intelligent diagnosis according to claim 6 is characterized in that: The system also includes a quantum secure communication remote monitoring unit, which transmits the touch screen's quantum fault prediction value, quantum maintenance strategy, quantum real-time status information, and quantum anomaly alarm information in real time to a remote quantum monitoring center through quantum key distribution and quantum teleportation technology.

8. A multi-modal interactive fault prediction and maintenance method for touch screens based on intelligent diagnosis, characterized in that: Specifically include: Collect multi-dimensional perception data of the touch screen in quantum touch interaction, neural gesture recognition, EEG speech collaboration, and iris eye tracking interaction modes; Quantum Fourier transform and quantum principal component analysis algorithms are used to extract quantum state features from the fused multimodal sensing data, mapping the classical features to the quantum Hilbert space to obtain fault feature vectors with quantum entanglement characteristics. Based on the quantum fault prediction formula, the fault feature vector with quantum entanglement characteristics is calculated to obtain the fault prediction value; Determining the quantum fault level based on the quantum fault prediction value and a preset quantum fault level threshold; A quantum multi-objective function is constructed by comprehensively considering the multi-objective factors of quantum state stability of the touch screen, quantum information security, quantum computing resource utilization, and quantum communication reliability; A quantum multi-objective optimization algorithm is used to optimize and solve the quantum multi-objective function to obtain the optimal maintenance strategy. The maintenance strategy includes a quantum maintenance time window, a quantum maintenance operation sequence, a quantum maintenance resource allocation, and a quantum spare parts scheduling plan.

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