Equipment operation and maintenance health state prediction and fault diagnosis system based on Internet of Things

Abstract

The invention provides an equipment operation and maintenance health state prediction and fault diagnosis system based on the Internet of Things, and relates to the technical field of Internet of Things prediction and diagnosis. The system comprises a digital twinning technology and virtual operation environment fusion module, an equipment data flow real-time distribution and processing module under a hybrid cloud architecture, a self-adaptive multi-modal sensor and fusion sensing module, an enhanced intelligent sensing technology and physical fault detection combination module, a virtual operation environment fusion module, a virtual operation environment fusion module and an equipment data flow real-time distribution and processing module under the hybrid cloud architecture. The intelligent multi-level fault diagnosis and active repair mechanism module and the quantum computing-assisted efficient fault diagnosis module can synchronize the running state and the health condition of the physical equipment in real time, construct a virtual equipment model and dynamically match the state of the physical equipment, and process data through a Kalman filtering algorithm and a particle swarm optimization algorithm, so that the fault diagnosis efficiency of the physical equipment is improved. According to the method, the mapping precision of the virtual model and the physical equipment is enhanced, the health condition prediction of the equipment is more accurate, and potential fault risks can be identified in advance through the fault prediction and early warning function based on the virtual model, so that accidental shutdown is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of Internet of Things prediction and diagnosis, specifically to an equipment operation and maintenance health state prediction and fault diagnosis system based on Internet of Things. BACKGROUND

[0002] According to the energy equipment fault prediction method, device and electronic equipment based on Internet of Things disclosed in Chinese Publication No. "CN116975741A", the energy equipment fault prediction method based on Internet of Things provided by the present application monitors and collects the current running state and current running parameters of the energy equipment in real time through Internet of Things, then extracts features from the collected current running data based on the ReliefF algorithm, then trains the classifier to obtain the current prediction result corresponding to the current running state and running data of the energy equipment, collects the current running data through Internet of Things so that the data is real-time and effective, and the fault prediction model obtained by training is used for fault prediction based on the current running data, thereby realizing automatic equipment fault monitoring and improving the accuracy and efficiency of equipment fault monitoring.

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

[0004] Problem one, in the traditional Internet of Things equipment health prediction and fault diagnosis, the prediction of equipment failure usually depends on static historical data or rule-driven methods, which often cannot reflect the current health status of the equipment in real time, resulting in low accuracy of fault prediction, in addition, the prediction of equipment failure is often delayed, which cannot timely alarm before the occurrence of failure, increasing the downtime and maintenance cost of the equipment;

[0005] Problem two, the system usually relies on a single cloud computing platform to process all data, resulting in insufficient cloud computing capacity, high delay, and inability to respond to equipment state changes in real time, thereby affecting the timeliness of fault diagnosis;

[0006] Problem three, in the above file and prior art, the sensors are often affected by environmental changes, equipment load and other factors, resulting in noise in the collected data, reducing the accuracy of equipment health assessment and fault diagnosis, and unable to dynamically adjust the data collection frequency according to the actual running state of the equipment, resulting in inaccurate and redundant data collection, and different types of sensors may provide mutually contradictory data, resulting in poor accuracy of data fusion;

[0007] Problem four, traditional fault diagnosis and data processing algorithms often have slow computing speed and insufficient processing capacity when facing large-scale, high-dimensional equipment data, especially when real-time fault diagnosis of complex systems is required, traditional algorithms often cannot meet the demand. SUMMARY

[0008] Technical problems solved

[0009] In view of the deficiencies of the prior art, the present application provides an equipment operation and maintenance health state prediction and fault diagnosis system based on the Internet of Things, which solves the following problems:

[0010] 1. The problem of low accuracy and slow response of equipment fault prediction;

[0011] 2. The problem of high data processing delay and insufficient computing power of equipment;

[0012] 3. The problem of difficult sensor data fusion, large sensor data noise and poor accuracy;

[0013] 4. The problem that traditional algorithms cannot cope with large-scale high-dimensional data.

[0014] Technical solutions

[0015] To achieve the above purpose, the present application is implemented by the following technical solutions: an equipment operation and maintenance health state prediction and fault diagnosis system based on the Internet of Things, which comprises a digital twin technology and virtual operation environment fusion module, a device data stream real-time distribution and processing module under a hybrid cloud architecture, a self-adaptive multi-modal sensor and fusion perception module, an enhanced intelligent sensing technology and physical fault detection combination module, an intelligent multi-level fault diagnosis and active repair mechanism module, and a quantum computing assisted efficient fault diagnosis module, wherein:

[0016] The digital twin technology and virtual operation environment fusion module: through digital twin technology, a virtual model of the equipment is constructed, which can synchronize the running state and health state of the physical equipment in real time. The mapping relationship between the virtual model and the physical equipment is constantly updated, and accurate equipment health prediction and fault diagnosis are performed;

[0017] The device data stream real-time distribution and processing module under the hybrid cloud architecture: a hybrid cloud architecture is adopted, and real-time processing and large-scale computing of device data are distributed between private cloud and public cloud. The private cloud focuses on core data processing and real-time decision-making of the equipment, while the public cloud provides higher computing power and storage capacity to support complex data analysis and large-scale prediction;

[0018] The self-adaptive multi-modal sensor and fusion perception module: multi-modal sensors are used, including temperature, pressure, vibration, infrared, for all-round monitoring of the equipment state. The adaptive sensor dynamically adjusts the sampling frequency and data acquisition mode according to the change of equipment operation to obtain more accurate equipment state data. Adaptive algorithm is used to adjust the sampling frequency of the sensor to optimize the perception effect;

[0019] The enhanced intelligent sensing technology combined with physical fault detection module: combined with enhanced intelligent sensors including infrared imaging, ultrasonic detection and traditional fault detection technology including temperature, pressure monitoring, efficient fault detection of equipment;

[0020] The intelligent multi-level fault diagnosis and active repair mechanism module: using intelligent multi-level fault diagnosis mechanism, the device is monitored in all directions and multiple levels, which can find faults in real time and provide repair solutions. The active repair mechanism adjusts or repairs in real time when the device fault is diagnosed, reduces human intervention and reduces downtime;

[0021] The quantum computing assisted high efficiency fault diagnosis module: using the high efficiency parallel computing ability of quantum computing, the efficiency of fault diagnosis and prediction is improved. Quantum computing is particularly suitable for processing high-dimensional and complex data sets, and can solve problems that traditional computing methods cannot handle in a very short time.

[0022] Preferably, the digital twin technology and virtual operation environment integration module internally processes data through Kalman filtering algorithm and particle swarm optimization algorithm, and the physical data of the device is uploaded to the edge computing node through the sensor to generate a virtual device model and synchronize the physical device state. The virtual environment can simulate various operating states of the device and predict potential fault risks. The digital twin technology and virtual operation environment integration module further includes the following contents:

[0023] Real-time virtual model updating unit: through real-time data feedback and algorithm optimization, dynamically update the parameters of the virtual device model to keep it consistent with the operating state of the physical device, ensuring that the virtual device accurately reflects the health status of the physical device at any time;

[0024] Fault prediction and early warning unit: based on the running data of the virtual model, use prediction algorithms to predict fault trends, and combine historical data with real-time data to generate early warning signals for possible device failures.

[0025] Preferably, the device data stream real-time distribution and processing module under the hybrid cloud architecture internally processes data through A search algorithm and decision tree algorithm. Device data is transmitted in real time to the private cloud for preliminary analysis through the edge computing node, and important data is uploaded to the public cloud for deep processing. The hybrid cloud architecture ensures low latency and high scalability of the system. The device data stream real-time distribution and processing module under the hybrid cloud architecture further includes the following contents:

[0026] Data distribution and routing control unit: intelligently select the transmission path of device data according to data importance and timeliness, preferentially transmit critical data under low latency, and use decision tree algorithm to ensure that high priority data is processed first;

[0027] Real-time data analysis unit: real-time analysis of device data using cloud computing resources, combined with the preliminary analysis results of edge computing nodes, to provide more accurate fault prediction and performance evaluation.

[0028] Preferably, the adaptive multi-modal sensor and fusion perception module use fuzzy logic algorithms and weighted average methods for data processing and fusion inside. Each sensor monitors different parameters of the device, and through edge computing nodes, data fusion is performed, combined with historical data and operating environment for state evaluation. The adaptive multi-modal sensor and fusion perception module further includes:

[0029] Sensor data filtering unit: the input data of multi-modal sensors is filtered by Kalman filtering algorithm to remove noise and improve data accuracy;

[0030] Dynamic adjustment unit: adaptive sensors can dynamically adjust the sampling frequency and data acquisition method of sensors based on device load, environmental changes and other factors, so as to obtain more accurate state data, and data fusion is performed according to the correlation between sensors to optimize the sensing effect.

[0031] Preferably, the enhanced intelligent sensing technology and physical fault detection combination module uses support vector machines and K-means clustering algorithms for data analysis inside. The enhanced intelligent sensing technology and physical fault detection combination module further includes:

[0032] Multi-sensor cooperative detection unit: combining multiple data sources of infrared thermal imaging, ultrasonic sensors, vibration sensors and temperature sensors, using weighted average algorithm for data fusion to improve the accuracy and reliability of fault detection;

[0033] Fault mode recognition unit: using support vector machines (SVM) and K-means clustering algorithm to classify different device operating states, accurately identifying device fault modes to ensure fast and accurate fault detection and early warning.

[0034] Preferably, the intelligent multi-level fault diagnosis and active repair mechanism module uses Bayesian networks and genetic algorithms for fault diagnosis and optimization inside. Through multi-level fault diagnosis, it can comprehensively evaluate from the physical state, functional state to the system level. The active repair mechanism automatically adjusts device parameters or starts repair mode according to fault type. The intelligent multi-level fault diagnosis and active repair mechanism module further includes:

[0035] Hierarchical diagnosis unit: through multi-level fault diagnosis mechanism, from the preliminary state monitoring of the equipment to the accurate fault mode analysis, each level makes decisions through different algorithms, thereby comprehensively diagnosing the equipment fault;

[0036] Active repair strategy unit: according to the fault diagnosis result, the system automatically generates the optimal repair scheme, and adjusts the equipment operation parameters or enables the standby system in real time through the adaptive control mechanism, so as to reduce the equipment downtime and improve the operation and maintenance efficiency.

[0037] Preferably, the quantum computing assisted efficient fault diagnosis module internally evaluates and calculates through quantum support vector machine and quantum Monte Carlo method, quickly processes and analyzes large-scale equipment data through quantum computing, and quantum algorithms can provide more efficient calculation and more accurate fault prediction than traditional algorithms when facing high-dimensional data, and the quantum computing assisted efficient fault diagnosis module further comprises:

[0038] Quantum data processing unit: utilizing the parallel computing characteristics of quantum computing, the quantum data processing unit efficiently analyzes large-scale historical data and real-time monitoring data of the equipment, and quickly identifies fault trends and potential fault modes,

[0039] Quantum optimization algorithm unit: the quantum optimization algorithm unit optimizes the fault prediction model using quantum optimization algorithms, improving the accuracy and calculation speed of fault diagnosis, and providing more efficient solutions especially when facing high-dimensional data.

[0040] Preferably, the system further comprises a data security and privacy protection module and a fault emergency response module, wherein:

[0041] Data security and privacy protection module: through encryption technology and access control mechanism, the data security and privacy protection module ensures the security of equipment data during cloud storage and transmission, and guarantees that the equipment privacy is not leaked, and the module adopts a data storage and tracking method based on blockchain technology, further enhancing data security and transparency;

[0042] Fault emergency response module: when the equipment has a serious fault, the system automatically starts the emergency response mechanism, immediately notifies the maintenance personnel, and takes corresponding emergency measures according to the preset emergency scheme to quickly restore the equipment operation.

[0043] Beneficial effects

[0044] The present application provides an equipment operation and maintenance health state prediction and fault diagnosis system based on Internet of Things. The following beneficial effects are provided:

[0045] 1、The system can improve the accuracy and timeliness of equipment failure prediction, by fusing digital twin technology and virtual operation environment, the system can real-time synchronize the running state and health condition of physical equipment, construct virtual equipment model and dynamically match with the state of physical equipment, adopt Kalman filtering algorithm and particle swarm optimization algorithm for data processing, enhance the mapping precision of virtual model and physical equipment, make the health condition prediction of equipment more accurate, in addition, through the fault prediction and early warning function based on virtual model, potential fault risk can be identified in advance, greatly improve the accuracy and timeliness of fault prediction, help to early warning before fault occurs, thereby avoiding the major damage and unexpected downtime of equipment.

[0046] 2、The system can optimize data processing and computing efficiency, improve system response speed, the system adopts hybrid cloud architecture, real-time distributes and processes equipment data stream, private cloud processes core data and provides real-time decision, and public cloud is responsible for processing large-scale data analysis and complex prediction, this architecture design ensures the efficiency and low delay of data processing, can quickly respond to the change of equipment state, especially in the scene of large data volume or high computing demand, ensures the real-time of equipment state monitoring and fault diagnosis, at the same time, adopts A search algorithm and decision tree algorithm for data processing, enhances the scalability and data processing capacity of the system, improves the processing speed and response ability of complex data.

[0047] 3、The system combines multi-modal sensor and intelligent fault diagnosis, enhances fault detection capability, self-adapts multi-modal sensor and enhances intelligent sensing technology, the system can comprehensively monitor multiple key parameters (such as temperature, pressure, vibration, infrared) of equipment, and combines fuzzy logic algorithm and weighted average method for data fusion, so as to obtain more accurate equipment health data, in addition, with the help of support vector machine and K-means clustering algorithm, the system can accurately identify the fault mode of equipment, especially in the complex environment of multiple faults coexisting, through the combination of multi-sensor cooperative detection and fault mode recognition, the accuracy of fault detection is greatly improved, the false positive rate is reduced, and the efficient operation of equipment is ensured.

[0048] 4、The system adopts quantum computing to assist efficient fault diagnosis, processes high-dimensional data and complex problems, the introduction of quantum computing provides the system with powerful computing power, especially in the processing of high-dimensional data, quantum computing can provide efficiency and accuracy that traditional algorithms cannot match, through quantum support vector machine and quantum Monte Carlo method for fault diagnosis, the system can quickly extract key information from large-scale equipment data, and perform rapid fault prediction and analysis, the quantum optimization algorithm further improves the precision and calculation speed of the fault prediction model, when facing complex data sets that traditional computing methods cannot handle, the response time of fault prediction can be greatly reduced, the fault prediction capability of the system is improved, and the intelligent level of overall operation and maintenance is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is a system architecture diagram of the present application;

[0050] Figure 2 It is a system running step diagram of the present application;

[0051] Figure 3 It is a system data flow chart of the present application;

[0052] Figure 4 It is a data flow chart of the present application;

[0053] Figure 5 It is a comparison chart of the Kalman filter estimated value and the real state of the present application;

[0054] Figure 6 It is a simulated noisy observation data chart of the present application;

[0055] Figure 7 It is a support vector machine device state classification prediction chart of the present application;

[0056] Figure 8 It is a sensor data classification scatter plot of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Specific embodiment one:

[0059] As Figures 1-8As shown, the equipment operation and maintenance health state prediction and fault diagnosis system based on Internet of Things includes a digital twin technology and virtual operation environment fusion module, a device data stream real-time distribution and processing module under a hybrid cloud architecture, a self-adaptive multi-modal sensor and fusion perception module, an enhanced intelligent sensing technology and physical fault detection combination module, an intelligent multi-level fault diagnosis and active repair mechanism module, a quantum computing assisted efficient fault diagnosis module, a data security and privacy protection module, and a fault emergency response module, wherein:

[0060] The digital twin technology and virtual operation environment fusion module: through digital twin technology, a virtual model of the equipment is constructed, which can synchronize the running state and health state of the physical device in real time. The mapping relationship between the virtual model and the physical device is constantly updated for accurate equipment health prediction and fault diagnosis.

[0061] The device data stream real-time distribution and processing module under the hybrid cloud architecture: using hybrid cloud architecture, real-time processing and large-scale computing of device data are distributed between private cloud and public cloud. The private cloud focuses on core data processing and real-time decision-making of the equipment, while the public cloud provides higher computing power and storage capacity to support complex data analysis and large-scale prediction.

[0062] The self-adaptive multi-modal sensor and fusion perception module: multi-modal sensors are used, including temperature, pressure, vibration, and infrared, to monitor the equipment state from all directions. The adaptive sensor dynamically adjusts the sampling frequency and data acquisition method according to the changes in equipment operation to obtain more accurate equipment state data. Adaptive algorithms are used to adjust the sampling frequency of the sensor to optimize the perception effect.

[0063] The enhanced intelligent sensing technology and physical fault detection combination module: combining enhanced intelligent sensors including infrared imaging, ultrasonic detection, and traditional fault detection technologies including temperature and pressure monitoring, efficient fault detection of the equipment is achieved.

[0064] The intelligent multi-level fault diagnosis and active repair mechanism module: using intelligent multi-level fault diagnosis mechanism, the equipment is monitored comprehensively and multi-level, which can discover faults in real time and provide repair solutions. The active repair mechanism adjusts or repairs in real time when the equipment fault is diagnosed, reducing human intervention and downtime.

[0065] The quantum computing assisted efficient fault diagnosis module: using the high parallel computing power of quantum computing, the efficiency of fault diagnosis and prediction is improved. Quantum computing is particularly suitable for handling high-dimensional and complex data sets, which can solve problems that traditional computing methods cannot handle in a very short time.

[0066] Data security and privacy protection module: Through encryption technology and access control mechanisms, it ensures the security of device data during cloud storage and transmission, while guaranteeing that device privacy is not leaked. This module adopts a data storage and tracking method based on blockchain technology to further enhance data security and transparency.

[0067] Fault Emergency Response Module: When a serious equipment failure occurs, the system automatically activates the emergency response mechanism, immediately notifies maintenance personnel, and takes corresponding emergency measures according to the preset emergency plan to quickly restore equipment operation;

[0068] The system integrates multiple advanced technologies and algorithms, including digital twins, hybrid cloud architecture, adaptive multimodal sensors, enhanced intelligent sensors, fault diagnosis, and quantum computing, to provide comprehensive equipment health monitoring and maintenance decision support. Each technology module employs a combination of two or more algorithms to improve the system's diagnostic accuracy and predictive capabilities. The system integrates multiple advanced technologies to provide efficient, accurate, and intelligent equipment management and fault prediction. Specific Implementation Example 2:

[0070] like Figures 1-8 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0071] The digital twin technology and virtual operating environment fusion module internally processes data using Kalman filtering and particle swarm optimization algorithms. Physical data from the device is uploaded to edge computing nodes via sensors to generate a virtual device model, which is then updated synchronously with the physical device's status. This virtual environment can simulate various operating states of the device and predict potential failure risks. The digital twin technology and virtual operating environment fusion module further includes the following:

[0072] Virtual model real-time update unit: Through real-time data feedback and algorithm optimization, the parameters of the virtual device model are dynamically updated to keep them consistent with the operating status of the physical device, ensuring that the virtual device can accurately reflect the health status of the physical device at any time;

[0073] Fault prediction and early warning unit: Based on the operating data of the virtual model, it uses prediction algorithms to predict fault trends and generates early warning signals for possible equipment failures by combining historical and real-time data.

[0074] The system fuses digital twin technology with a virtual operation environment through a fusion module, and can synchronize the running state and health condition of the physical device in real time, construct a virtual device model and dynamically match it with the state of the physical device, process data using Kalman filtering algorithm and particle swarm optimization algorithm, enhance the mapping accuracy of the virtual model and the physical device, and make the health condition prediction of the device more accurate. In addition, through the fault prediction and early warning function based on the virtual model, potential fault risks can be identified in advance, greatly improving the accuracy and timeliness of fault prediction, which helps to warn before the fault occurs, thereby avoiding major damage and unexpected downtime of the device.

[0075] The device data stream real-time distribution and processing module under the hybrid cloud architecture processes data internally through A search algorithm and decision tree algorithm, and the device data is transmitted to the private cloud in real time through edge computing nodes for preliminary analysis, and important data is uploaded to the public cloud for deep processing. The hybrid cloud architecture ensures low latency and high scalability of the system. The device data stream real-time distribution and processing module under the hybrid cloud architecture further includes the following:

[0076] Data distribution and routing control unit: intelligently selects the transmission path of device data according to data importance and timeliness, prioritizes the transmission of critical data under low latency, and uses decision tree algorithm to ensure that high-priority data is processed first;

[0077] Real-time data analysis unit: uses cloud computing resources to analyze device data in real time, combines the preliminary analysis results of edge computing nodes, and provides more accurate fault prediction and performance evaluation.

[0078] The system can optimize data processing and computing efficiency, and improve system response speed through the device data stream real-time distribution and processing module under the hybrid cloud architecture. The system uses a hybrid cloud architecture to distribute and process device data streams in real time, with private clouds processing core data and providing real-time decisions, and public clouds handling large-scale data analysis and complex predictions. This architecture design ensures efficient and low-latency data processing, enabling rapid response to changes in device status, especially in scenarios with large data volumes or high computing demands, ensuring real-time device state monitoring and fault diagnosis. In addition, A search algorithm and decision tree algorithm are used for data processing, enhancing the system's scalability and data processing capabilities, and improving the speed and responsiveness of complex data processing.

[0079] The adaptive multi-modal sensor and fusion perception module internally uses fuzzy logic algorithm and weighted average method for data processing and fusion. Each sensor monitors different parameters of the device, and the edge computing node is used for data fusion, combined with historical data and operating environment of the device for state evaluation. The adaptive multi-modal sensor and fusion perception module further includes:

[0080] Sensor data filtering unit: filter the input data of multi-modal sensors through Kalman filtering algorithm, remove noise and improve the accuracy of data;

[0081] Dynamic adjustment unit: adaptive sensors can dynamically adjust the sampling frequency and data acquisition method of sensors based on device load, environmental changes and other factors, so as to obtain more accurate state data, and perform data fusion according to the correlation between sensors, and optimize the sensing effect.

[0082] Enhanced intelligent sensing technology and physical fault detection combination module: through support vector machine and K-means clustering algorithm for data analysis, enhance the real-time monitoring of key components of the device by sensors, combine with traditional monitoring data for multi-dimensional analysis, the data of sensors are transmitted to the cloud platform through edge computing nodes for fault diagnosis, the enhanced intelligent sensing technology and physical fault detection combination module further includes:

[0083] Multi-sensor cooperative detection unit: combine multiple data sources of infrared thermal imaging, ultrasonic sensor, vibration sensor and temperature sensor, use weighted average algorithm for data fusion, improve the accuracy and reliability of fault detection,

[0084] Fault mode recognition unit: use support vector machine (SVM) and K-means clustering algorithm to classify different device operating states, accurately identify device fault modes, ensure fast and accurate fault detection and early warning,

[0085] The system collects and fuses sensor data by adaptive multi-modal sensors, fusion perception module and enhanced intelligent sensing technology and physical fault detection combination module, combines multi-modal sensors with intelligent fault diagnosis, enhances fault detection capability, combines adaptive multi-modal sensors with enhanced intelligent sensing technology, the system can comprehensively monitor multiple key parameters of the device (such as temperature, pressure, vibration, infrared), and combine fuzzy logic algorithm with weighted average method for data fusion, so as to obtain more accurate device health data, in addition, with the help of support vector machine and K-means clustering algorithm, the system can accurately identify the fault mode of the device, especially in complex environment with multiple faults coexisting, through the combination of multi-sensor cooperative detection and fault mode recognition, the accuracy of fault detection is greatly improved, the false positive rate is reduced, and the efficient operation of the device is ensured.

[0086] The intelligent multi-level fault diagnosis and active repair mechanism module internally performs fault diagnosis and optimization through Bayesian networks and genetic algorithms. Through multi-level fault diagnosis, it can comprehensively evaluate from the physical state, functional state to the system level. The active repair mechanism automatically adjusts device parameters or starts repair mode according to fault types. The intelligent multi-level fault diagnosis and active repair mechanism module further includes:

[0087] Hierarchical diagnosis unit: Through a multi-level fault diagnosis mechanism, from preliminary state monitoring to accurate fault mode analysis, each level makes decisions through different algorithms, thereby comprehensively diagnosing device faults.

[0088] Active repair strategy unit: According to fault diagnosis results, the system automatically generates the optimal repair scheme and adjusts device operating parameters or enables standby systems in real time through adaptive control mechanisms to reduce device downtime and improve operation and maintenance efficiency.

[0089] Quantum computing assisted efficient fault diagnosis module internally performs evaluation and calculation through quantum support vector machines and quantum Monte Carlo methods. Through quantum computing, large-scale device data is quickly processed and analyzed. Quantum algorithms can provide more efficient computation and more accurate fault prediction than traditional algorithms when facing high-dimensional data. For example, when there is a slight fault (such as high temperature), the system can repair by adjusting device load; when there is a serious fault (such as mechanical component damage), the system issues a maintenance warning and provides specific repair solutions. The active repair mechanism greatly reduces device downtime, improves device operation efficiency, avoids delayed repair due to human intervention, not only can diagnose device faults, but also can perform real-time adjustment or repair through the active repair mechanism, reducing human intervention. The quantum computing assisted efficient fault diagnosis module further includes:

[0090] Quantum data processing unit: Utilizing the parallel computing characteristics of quantum computing, it efficiently analyzes large-scale historical data and real-time monitoring data of devices, quickly identifies fault trends and potential fault modes,

[0091] Quantum optimization algorithm unit: Quantum optimization algorithms are used to optimize fault prediction models, improving the accuracy and speed of fault diagnosis, especially providing more efficient solutions when facing high-dimensional data.

[0092] The system adopts quantum computing to assist efficient fault diagnosis, processes high-dimensional data and complex problems, and the introduction of quantum computing provides the system with powerful computing power, especially in the processing of high-dimensional data. Quantum computing can provide efficiency and accuracy that traditional algorithms cannot match. Through quantum support vector machines and quantum Monte Carlo methods for fault diagnosis, the system can quickly extract key information from large-scale equipment data for rapid fault prediction and analysis. Quantum optimization algorithms further improve the accuracy and computing speed of the fault prediction model. In the face of complex data sets that traditional computing methods cannot handle, the response time of fault prediction can be greatly reduced, improving the fault prediction capability of the system and enhancing the intelligent level of overall operation and maintenance. Embodiment Three:

[0094] As Figures 1-8 shown, according to the content in the above embodiments, the following content is further disclosed:

[0095] Each module of the entire step system works in coordination to ensure the smooth operation of the device's health prediction, fault diagnosis, early warning and repair functions. Each step is processed in a modular manner, from data collection to fault repair. The system as a whole realizes high integration and intelligent management. The operation steps of the entire system are as follows:

[0096] Sp1: Data collection and transmission

[0097] Device sensor data collection: Each adaptive multi-modal sensor (such as temperature, pressure, vibration, infrared, etc.) begins to monitor the device status in real time. The sensor automatically adjusts the sampling frequency and data collection method through a dynamic adjustment unit according to factors such as device load and environmental changes to obtain more accurate device status data.

[0098] Data transmission to edge computing nodes: The data collected by each sensor is preliminarily processed by the edge computing nodes, such as filtering, data fusion, and noise suppression. The sensor data filtering unit processes the data through the Kalman filtering algorithm to ensure the accuracy and reliability of the data.

[0099] Sp2: Data stream distribution and preliminary analysis

[0100] Preliminary analysis of edge computing nodes: The preliminarily analyzed data (such as device operating status and sensor health data) are transmitted to the private cloud through the data distribution and routing control unit. Key data are transmitted first according to timeliness and importance, and intelligent routing is performed through A search algorithm.

[0101] Real-time data analysis in private cloud: In the private cloud, data are analyzed in real time, combining historical data and current status of the device for fault prediction and performance evaluation.

[0102] Sp3: Virtual device model synchronization and update

[0103] Virtual model generation and synchronization: Through digital twin technology, a virtual model of the device is generated and updated in synchronization with the physical device state. The virtual model real-time updating unit continuously receives data from the edge computing node, updates it through Kalman filtering algorithm and particle swarm optimization algorithm, ensuring that the virtual device accurately reflects the health status of the physical device at any time;

[0104] Fault prediction and early warning generation: Using virtual device models and historical data, the system generates fault prediction information and early warning signals through the fault prediction and early warning unit, identifying potential faults in advance;

[0105] Sp4: Deep analysis in hybrid cloud architecture

[0106] Data transmission to public cloud for deep analysis: Important data is transmitted to the public cloud for deep data analysis after preliminary analysis by the private cloud. The real-time data analysis unit uses the powerful computing power of the cloud and the preliminary results of the edge computing node to perform more accurate fault prediction and performance evaluation on device data;

[0107] Sp5: Fault diagnosis and repair

[0108] Fault diagnosis: Through the intelligent multi-level fault diagnosis and active repair mechanism module, the system performs multi-level fault diagnosis, from the physical state, functional state of the device to the system level for comprehensive analysis. The hierarchical diagnosis unit uses different algorithms (such as Bayesian networks, genetic algorithms, etc.) to make decisions layer by layer, accurately determining device faults;

[0109] Active repair mechanism starts: Based on the fault diagnosis results, the system automatically generates the optimal repair scheme. The active repair strategy unit adjusts the device operating parameters or starts the standby system through the adaptive control mechanism, reducing downtime and improving operational efficiency;

[0110] Sp6: Fault mode identification and optimization

[0111] Fault mode identification: Using enhanced intelligent sensing technology and physical fault detection combination module, combining infrared imaging, ultrasonic detection, vibration sensors and traditional temperature, pressure monitoring data, the system performs data fusion through weighted average algorithm. The fault mode identification unit uses support vector machine (SVM) and K-means clustering algorithm to classify device operating status, accurately identifying fault modes;

[0112] Real-time fault early warning and optimization: Once a fault pattern is identified, the system notifies the operation and maintenance personnel through the fault early warning mechanism and provides relevant repair suggestions. The quantum computing-assisted efficient fault diagnosis module uses quantum support vector machines and quantum Monte Carlo methods to further optimize and predict faults, improving the accuracy and speed of fault prediction.

[0113] Sp7: Quantum computing-assisted fault diagnosis

[0114] Quantum data processing: Utilizing the parallel computing characteristics of quantum computing, the quantum computing module efficiently analyzes large-scale historical data and real-time monitoring data of the equipment, quickly identifying fault trends and potential fault patterns.

[0115] Quantum optimization and decision support: The quantum optimization algorithm unit optimizes the fault prediction model through quantum computing, improving the accuracy and speed of fault diagnosis, especially when dealing with complex and high-dimensional data, providing more efficient computing results.

[0116] Sp8: Fault emergency response and handling

[0117] Emergency response initiation: When the system detects a serious fault or potential risk, the fault emergency response module immediately initiates, automatically notifying maintenance personnel and taking emergency measures according to the pre-set emergency plan.

[0118] Emergency measures implementation: According to the fault type and current equipment status, the system initiates corresponding emergency handling measures (such as adjusting equipment operating parameters, starting backup equipment, etc.) through automated means, quickly restoring equipment operation.

[0119] Sp9: Data security and privacy protection

[0120] Data security assurance: The data security and privacy protection module uses encryption technology and access control mechanisms to ensure the security of equipment data during cloud storage and transmission. The system uses blockchain technology to track and store data, enhancing data security and transparency, and ensuring that device privacy is not leaked.

[0121] The data processing direction steps for each module corresponding to the above system steps are as follows:

[0122] Sp1: Data collection and preliminary processing

[0123] Data collection: Various sensors (such as temperature, pressure, vibration, infrared, etc.) monitor equipment status in real time, collecting equipment operation data. The collected data includes physical environment data (such as temperature, humidity, etc.) and equipment health status data (such as vibration, temperature, pressure, etc.). Each sensor has adaptive characteristics, capable of dynamically adjusting sampling frequency and data collection methods according to equipment load or environmental changes, to ensure the acquisition of the most accurate state data.

[0124] Preliminary data processing: The data collected by the sensors is transmitted to the edge computing node for preliminary data processing, including noise filtering, data fusion, etc. Kalman filtering algorithm is used for data filtering to remove noise, smooth data, make it more accurate and reliable. The results of data processing are transmitted to the cloud or local server for further analysis.

[0125] Sp2: Data transmission and distribution

[0126] Data transmission to private cloud: After preliminary processing, the device data is transmitted to the private cloud through the network. In the private cloud, data will be intelligently routed according to factors such as timeliness and importance, ensuring low latency and prioritizing high-priority data. The data distribution and routing control unit uses A* search algorithm to select the data transmission path, ensuring that data can be quickly and effectively transmitted to the destination.

[0127] Private cloud data analysis: In the private cloud, the preliminary analysis results of the edge computing node are used to conduct real-time data analysis and predictive model construction. The private cloud uses low-latency computing resources to analyze and process data in real time, combining historical data of the device to support fault prediction and performance evaluation.

[0128] Sp3: Data analysis and virtual model synchronization

[0129] Virtual device model construction and synchronization: The digital twin technology and virtual operating environment fusion module generates a virtual model of the device and synchronizes the actual device status with the virtual model. Physical data of the device is uploaded to the edge computing node through sensors, and through real-time data feedback and algorithm optimization, a virtual device model is generated and kept consistent with the physical device status. The virtual model real-time update unit uses Kalman filtering algorithm and particle swarm optimization algorithm to update the parameters of the virtual model, ensuring that the virtual model accurately reflects the health status of the device.

[0130] Fault prediction and early warning: The running data provided by the virtual model is used to predict the fault trend of the device. Based on the running data of the virtual device, the algorithm predicts the possible faults of the device and generates early warning signals. These early warning signals are transmitted to the operation and maintenance platform to remind the operator to perform necessary maintenance in advance.

[0131] Sp4: Cloud deep analysis and optimization

[0132] Data transmission to public cloud for deep analysis: After the preliminary analysis of device data by the private cloud, important data is transmitted to the public cloud for deeper processing and analysis. In the public cloud, more powerful computing resources are used for deep analysis of data, training of optimization models, and large-scale prediction. The real-time data analysis unit further improves the accuracy of fault diagnosis and device performance prediction by combining edge computing and the preliminary analysis results of the private cloud.

[0133] Advanced data processing and optimization: Deep learning and machine learning algorithms in the public cloud can handle complex multi-dimensional data, identify potential fault patterns, and comprehensively evaluate device operation. The efficient fault diagnosis module assisted by quantum computing improves the processing efficiency of high-dimensional data sets and accurately identifies potential fault patterns through the high-efficiency parallel computing capability of quantum computing.

[0134] Sp5: Fault diagnosis and active repair

[0135] Fault diagnosis and classification: The enhanced intelligent sensing technology and physical fault detection combination module uses various sensors (such as infrared thermal imaging, ultrasonic waves, vibration sensors, etc.) to perform multi-dimensional real-time monitoring of devices. The collected sensor data is fused through weighted average algorithm and other data processing algorithms to identify device fault patterns. Support vector machines (SVM) and K-means clustering algorithms are used to classify different device fault patterns and accurately identify device fault types.

[0136] Fault repair scheme generation: Once the fault pattern is identified, the system automatically generates a repair scheme based on the diagnosis results. The intelligent multi-level fault diagnosis and active repair mechanism module starts the active repair mechanism, automatically adjusts device parameters, starts backup systems, or provides corresponding repair suggestions to operators based on the type of fault and the current state of the device. Fault repair data feedback is transmitted back to the system for verification to ensure that the state of the repaired device meets expectations.

[0137] Sp6: Quantum computing supported diagnosis and optimization

[0138] Quantum computing for large-scale data processing: The efficient fault diagnosis module assisted by quantum computing uses quantum support vector machines (QSVM) and quantum Monte Carlo methods to efficiently process device historical data and real-time monitoring data. Quantum computing can handle high-dimensional complex data, improving the accuracy and speed of fault prediction, especially when dealing with large-scale device data. Its parallel computing characteristics can significantly improve efficiency.

[0139] Quantum optimization algorithm: The quantum optimization algorithm unit optimizes the fault prediction model through quantum computing when processing device data, thereby improving diagnostic accuracy and calculation speed. In the face of complex, multi-dimensional data, quantum algorithms can provide more efficient solutions than traditional computing methods, reducing data processing time and improving overall operational efficiency.

[0140] Sp7: Data security and privacy protection

[0141] Data security assurance: The data security and privacy protection module ensures the security of device data during cloud storage and transmission through encryption technology and access control mechanisms, preventing data leakage or tampering. The system uses a storage and tracking method based on blockchain technology to enhance data security, transparency, and credibility.

[0142] Privacy protection mechanism: Blockchain technology ensures that each data access record is traceable and tamper-proof, preventing device privacy from being leaked. Data is anonymized to ensure that sensitive information about users or devices is not leaked during data transmission and storage.

[0143] Summary of data processing trends

[0144] Data collection and transmission: Collect data from sensors and transmit it to the cloud after preliminary processing by edge computing nodes.

[0145] Data storage and analysis: Distributively store and deeply analyze data in private and public clouds for fault prediction, performance evaluation, and trend analysis.

[0146] Virtual model synchronization: Update virtual device models in real time and perform fault prediction through digital twinning technology.

[0147] Fault diagnosis and repair: Diagnose device faults based on intelligent algorithms, generate repair plans, and perform real-time adjustments through active repair mechanisms.

[0148] Quantum computing optimization: Use quantum computing to accelerate data processing and improve fault prediction accuracy, especially when dealing with high-dimensional and complex data.

[0149] Data security and privacy protection: Ensure data transmission and storage security and privacy protection through encryption and blockchain technology.

[0150] Data in this system forms a complete cycle from collection to processing, analysis, diagnosis, and repair. Each step ensures efficient data flow, accurate diagnosis, and security assurance. Specific embodiment four:

[0152] As Figures 1-8Further disclosed are the following according to the content in the above specific embodiments.

[0153] The specific content and operation logic of the algorithm of the entire system are as follows:

[0154] Digital twin technology and virtual operation environment fusion module

[0155] Algorithm: Kalman filter algorithm KalmanFilter and particle swarm optimization algorithm PSO

[0156] Kalman filter algorithm

[0157] Prediction step:

[0158]

[0159] wherein, is the predicted state estimate, A is the state transition matrix, B is the control input matrix, u k Control input, is the predicted covariance matrix, Q is the process noise covariance matrix;

[0160] Update step:

[0161]

[0162] wherein, K k is the Kalman gain, H is the observation matrix, y k is the observation value, R is the observation noise covariance matrix, P k is the updated error covariance matrix;

[0163] The core idea of the Kalman filter is to estimate the state of the system in a recursive manner and optimize the state prediction by combining noise estimation. In the digital twin system, the Kalman filter is used to filter the noise in the device sensor data, accurately estimate the current state of the device, and update the virtual device model in real time, so that the virtual environment can accurately reflect the health status of the physical device.

[0164] Particle swarm optimization algorithm (PSO)

[0165] Mathematical formula:

[0166] Particle position update:

[0167]

[0168] Particle velocity update:

[0169]

[0170] wherein, x i is the particle position, vi is the velocity of the particle, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, is the historical optimal position of the particle, g k global optimal position;

[0171] The particle swarm optimization algorithm is used to optimize the mapping relationship between the virtual model and the physical device, ensuring that the virtual device model can track the health status of the device in real time during operation. By optimizing the parameters in the virtual environment through PSO, the prediction accuracy and response speed of digital twinning are improved.

[0172] For the device data stream real-time distribution and processing module under the hybrid cloud architecture, it includes A search algorithm and decision tree algorithm, the specific content is as follows:

[0173] A search algorithm

[0174] Heuristic function:

[0175] f(n)=g(n)+h(n)

[0176] Where f(n) is the evaluation function of node n, g(n) is the path cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the target node.

[0177] The A search algorithm is used to select the optimal path for data transmission. In the device data stream real-time distribution and processing module, the A search algorithm dynamically adjusts the routing of data streams according to the importance and timeliness of device data, optimizes the data transmission path, and ensures that critical data can be quickly transmitted, reducing delay.

[0178] Decision tree algorithm

[0179] The basic split criterion of the decision tree algorithm:

[0180]

[0181] Where D is the data set, p i is the probability of class i.

[0182] The decision tree algorithm is used in the data distribution and routing control unit. By classifying the priority of data, the system makes decisions based on the importance and timeliness of data, and classifies device data using the decision tree algorithm to determine the key information that needs to be transmitted first.

[0183] The adaptive multi-modal sensor and fusion perception module includes fuzzy logic algorithm and weighted average method

[0184] Fuzzy logic algorithm

[0185] Fuzzy rule output

[0186]

[0187] where μ i (x) is a membership function, f i (x) is a rule function, and y is the output result.

[0188] The weighted average method formula is:

[0189]

[0190] where w i is the weight coefficient, x i is the data point, and y is the weighted average value.

[0191] The weighted average method is used to fuse data from different sensors. In the multi-modal sensor module, the system combines data from different sensors through the weighted average method to obtain the comprehensive state of the device.

[0192] The enhanced intelligent sensing technology and physical fault detection combination module includes support vector machines (SVM) and K-means clustering algorithm (K-means), and further includes the following:

[0193] Support vector machines (SVM)

[0194] SVM classification decision function:

[0195] f(x) = w T x + b

[0196] where w is the weight vector, b is the bias term, and x is the input feature.

[0197] SVM is used in the enhanced intelligent sensing technology and physical fault detection combination module to classify different device fault patterns. The system classifies the device operation data collected by the sensor through SVM to identify the device fault type.

[0198] K-means clustering algorithm

[0199] Loss function:

[0200]

[0201] where μ k is the cluster center, and ∥x i - μ k ∥ 2 is the distance between the data point and the cluster center.

[0202] K-means clustering is used to enhance the data analysis of the sensor. Through clustering, the system can identify abnormal patterns in device operation, group device faults according to features, and accurately diagnose faults.

[0203] The intelligent multi-level fault diagnosis and proactive repair mechanism module includes Bayesian networks and genetic algorithms, and further includes the following:

[0204] Bayesian networks

[0205] Conditional probability distribution:

[0206]

[0207] Among them, X i Let Pa(X) be a random variable. i ) is X i The set of parent nodes;

[0208] Bayesian networks are used to infer the probability of equipment failure in multi-level fault diagnosis. The system constructs conditional dependencies based on historical data of the equipment and sensor feedback, and diagnoses the cause of the failure based on Bayesian inference methods.

[0209] Genetic Algorithm

[0210] Objective function:

[0211]

[0212] Where, x i For gene representation, c i This represents the fitness value of a gene;

[0213] Genetic algorithms are used in proactive repair mechanisms. By optimizing and selecting possible repair strategies, the most suitable repair solution is found and executed automatically to ensure that the device returns to normal operation.

[0214] The quantum computing-assisted high-efficiency fault diagnosis module incorporates a quantum support vector machine. The objective function of the quantum SVM is similar to that of the classical SVM, but it accelerates high-dimensional data processing through quantum inner product calculation.

[0215] Quantum support vector machines are used for rapid classification of large-scale, high-dimensional device data. Combined with the parallelism of quantum computing, they provide efficient fault diagnosis support. In high-dimensional data processing, quantum SVMs can significantly improve computation speed and accuracy. Specific Implementation Example 5:

[0217] like Figures 1-8 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0218] The hardware architecture of each module in the entire system is shown below:

[0219] Digital twin technology and virtual operating environment integration module

[0220] Hardware requirements:

[0221] Edge computing nodes: These are used to collect and process physical data from devices in real time, and then upload the processed data to the cloud. Edge computing nodes require high-performance processing capabilities to support real-time data transmission and preliminary analysis. It is recommended to use single-board computers with high computing power, such as NVIDIA Jetson series or Intel NUC.

[0222] High-precision sensors: such as temperature, pressure, vibration, and infrared sensors. These sensors require high accuracy and high reliability, and can continuously monitor the status of equipment in real time. They can be used in the following ways:

[0223] Temperature sensors: such as PT100, NTC thermistors;

[0224] Pressure sensor: pressure transmitter;

[0225] Vibration sensors: MEMS accelerometers, piezoelectric sensors;

[0226] Infrared sensors: used for monitoring the surface temperature distribution of equipment, such as infrared thermal imaging cameras (FLIR, etc.).

[0227] GPU accelerator cards: The construction and synchronization of virtual models for digital twins require strong computing power. Therefore, equipping them with GPU accelerator cards (such as the NVIDIA Tesla series) helps to accelerate the mapping between physical data and virtual models.

[0228] Real-time data processing unit: For example, using FPGA (Field Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit) to optimize the needs of real-time data processing and high-frequency data synchronization.

[0229] Real-time distribution and processing module of device data stream under hybrid cloud architecture

[0230] Hardware requirements:

[0231] Private cloud servers: The servers required in a private cloud will process local data and ensure low latency. High-performance servers, such as HPE ProLiant and Dell PowerEdge, are recommended, as they have sufficient computing, storage, and network processing capabilities.

[0232] Public cloud resources: rely on the computing power of cloud service platforms (such as AWS, Azure, Google Cloud) for large-scale data analysis and deep learning modeling, and hardware resources are expanded on demand through cloud services;

[0233] Network switching equipment: network switches, routers, etc. need to ensure that device data can be transmitted efficiently and with low latency to different cloud and local data storage units;

[0234] Edge computing gateway: used for preliminary processing, transmission and protocol conversion of data, usually uses edge gateway devices (such as NVIDIA Jetson, Cisco IoT Gateway) for real-time data flow management.

[0235] Adaptive multi-modal sensor and fusion perception module

[0236] Hardware requirements:

[0237] Multi-modal sensor array includes the following:

[0238] Temperature sensor: NTC thermistor, PT100, etc.

[0239] Pressure sensor: high-precision sensor, such as piezoelectric sensor, strain gauge sensor

[0240] Vibration sensor: such as MEMS accelerometer, piezoelectric acceleration sensor

[0241] Infrared sensor: for thermal imaging and surface temperature monitoring, such as FLIR or ThermoWorks infrared imaging equipment

[0242] Data fusion platform: data fusion hardware is usually based on high-performance embedded computing platforms such as Raspberry Pi, Intel NUC, combined with data sensors, and through adaptive algorithms such as Kalman filtering and weighted average method for real-time data fusion processing

[0243] Real-time monitoring transmission unit: uses low-power, low-latency wireless communication modules (such as ZigBee, LoRa, NB-IoT) to ensure stable and reliable transmission of sensor data.

[0244] Enhanced intelligent sensing technology and physical fault detection combination module

[0245] Hardware requirements:

[0246] Enhanced intelligent sensor includes the following:

[0247] Infrared thermal imager: for non-contact detection of temperature distribution, such as FLIR's infrared imaging equipment

[0248] Ultrasonic sensor: for detecting surface cracks or material defects of equipment, such as ultrasonic detector

[0249] Vibration sensor: such as piezoelectric sensor, MEMS accelerometer, for monitoring the vibration state of the equipment during operation

[0250] Temperature / Pressure Sensor: Detects possible temperature and pressure abnormalities during the device's operation;

[0251] Fault Mode Recognition Hardware Platform: To perform efficient fault detection and pattern recognition, the system requires high-performance data processing hardware, such as using embedded hardware platforms like NVIDIA Jetson or Intel NUC, combined with specialized deep learning accelerator cards (e.g., NVIDIA Tesla) for complex data analysis.

[0252] Intelligent Multi-Level Fault Diagnosis and Active Repair Mechanism Module

[0253] Hardware Requirements:

[0254] Fault Diagnosis Unit: Multi-Level Sensors: Deploy various sensors (e.g., temperature, vibration, infrared, etc.) for multi-level fault diagnosis. Fault Diagnosis Computing Unit: Includes GPU-accelerated deep learning modules (e.g., NVIDIA Tesla) to accelerate the multi-level fault diagnosis process.

[0255] Active Repair Unit: Actuators / Execution Devices: Used to repair the device by automatically adjusting device parameters or enabling backup systems when a fault occurs. For example, using servo motors, electric valves, etc., to adjust the device's working state.

[0256] System Control Unit: Collects diagnostic data at various levels, generates repair strategies based on algorithms, and performs automated control. The control unit needs to be reliable and efficient, often using embedded control systems (e.g., ARM architecture development boards) for control.

[0257] Quantum Computing Assisted Efficient Fault Diagnosis Module

[0258] Hardware Requirements:

[0259] Quantum Computing Hardware: Quantum computing modules require specialized quantum processors for large-scale parallel data computation. Quantum computer hardware platforms can use IBM Quantum, D-Wave quantum computers, or specialized hardware based on quantum algorithms (e.g., quantum accelerators).

[0260] Quantum Optimization Hardware Accelerator: Integrates quantum optimization hardware accelerators such as Quantum Support Vector Machines (QSVM) and Quantum Monte Carlo methods, specifically designed to improve fault prediction accuracy and computing speed.

[0261] Data Security and Privacy Protection Module

[0262] Hardware Requirements:

[0263] Encryption hardware module: Use hardware encryption modules (such as TPM, HSM) to encrypt data in transmission, ensure the security and privacy of data;

[0264] Blockchain storage node: Use distributed storage nodes to ensure data tamper-proof and reliability, support device data storage and tracking based on blockchain technology;

[0265] Firewall and security gateway: Firewall and security gateway are used to prevent external malicious attacks and protect the security of cloud data;

[0266] Fault emergency response module

[0267] Hardware requirements:

[0268] Emergency response system: Emergency response hardware is needed to support the rapid recovery of devices, including:

[0269] Communication equipment (such as wireless communication module, transmission equipment) is used to send fault alarm to maintenance personnel,

[0270] Remote control unit, used for remote operation of equipment and start of emergency mode (for example, start of backup system);

[0271] Automated maintenance equipment: such as automated mechanical arm, robot, etc., used to automatically complete device repair or maintenance operation when fault occurs;

[0272] The hardware equipment corresponding to each module helps to ensure the efficient and accurate operation of data collection, transmission, processing, diagnosis and security, etc. Through the use of high-performance computing hardware, intelligent sensors, real-time data processing units and other comprehensive hardware systems, the overall performance and reliability of the system can be improved, and the accuracy of device health state prediction and fault diagnosis can be ensured.

[0273] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a reference structure" does not exclude the presence of other identical elements in the process, method, article or equipment including the element.

[0274] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. An equipment operation and maintenance health status prediction and fault diagnosis system based on the Internet of Things, characterized in that: The system includes a digital twin technology and virtual operating environment fusion module, a real-time distribution and processing module for device data streams under a hybrid cloud architecture, an adaptive multimodal sensor and fusion sensing module, an enhanced intelligent sensing technology and physical fault detection combination module, an intelligent multi-level fault diagnosis and proactive repair mechanism module, and a quantum computing-assisted high-efficiency fault diagnosis module, wherein: The digital twin technology and virtual operating environment integration module: Through digital twin technology, a virtual model of the equipment is constructed, which can synchronize the operating status and health status of the physical equipment in real time. The mapping relationship between the virtual model and the physical equipment is constantly updated, enabling accurate equipment health prediction and fault diagnosis. The real-time distribution and processing module for device data streams under the hybrid cloud architecture: adopts a hybrid cloud architecture, distributing the real-time processing and large-scale computing of device data between private clouds and public clouds. The private cloud focuses on the core data processing and real-time decision-making of the device, while the public cloud provides higher computing power and storage capacity to support complex data analysis and large-scale prediction. The adaptive multimodal sensor and fusion sensing module employs multimodal sensors, including temperature, pressure, vibration, and infrared sensors, to comprehensively monitor the equipment status. The adaptive sensor dynamically adjusts the sampling frequency and data acquisition method according to changes in equipment operation to obtain more accurate equipment status data. An adaptive algorithm is used to adjust the sensor's sampling frequency to optimize the sensing effect. The enhanced intelligent sensing technology and physical fault detection combination module combines enhanced intelligent sensors, including infrared imaging and ultrasonic detection, with traditional fault detection technologies, including temperature and pressure monitoring, to perform efficient fault detection on equipment. The intelligent multi-level fault diagnosis and active repair mechanism module utilizes an intelligent multi-level fault diagnosis mechanism to perform comprehensive and multi-level status monitoring of the equipment. It can detect faults in real time and provide repair solutions. While diagnosing equipment faults, the active repair mechanism makes real-time adjustments or repairs, reducing human intervention and downtime. The quantum computing-assisted high-efficiency fault diagnosis module utilizes the high-efficiency parallel computing capabilities of quantum computing to improve the efficiency of fault diagnosis and prediction. Quantum computing is particularly suitable for processing high-dimensional and complex datasets and can solve problems that are difficult to handle by traditional computing methods in a very short time.

2. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The digital twin technology and virtual operating environment fusion module internally processes data using Kalman filtering and particle swarm optimization algorithms. Physical data from the device is uploaded to edge computing nodes via sensors to generate a virtual device model, which is then updated synchronously with the physical device's status. This virtual environment can simulate various operating states of the device and predict potential failure risks. The digital twin technology and virtual operating environment fusion module further includes the following: Virtual model real-time update unit: Through real-time data feedback and algorithm optimization, the parameters of the virtual device model are dynamically updated to keep them consistent with the operating status of the physical device, ensuring that the virtual device can accurately reflect the health status of the physical device at any time; Fault prediction and early warning unit: Based on the operating data of the virtual model, it uses prediction algorithms to predict fault trends and generates early warning signals for possible equipment failures by combining historical and real-time data.

3. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The real-time distribution and processing module for device data streams under the hybrid cloud architecture internally processes data using A search algorithm and decision tree algorithm. Device data is transmitted in real time to the private cloud for preliminary analysis via edge computing nodes, and important data is then uploaded to the public cloud for in-depth processing. The hybrid cloud architecture ensures low latency and high scalability of the system. The real-time distribution and processing module for device data streams under the hybrid cloud architecture further includes the following: Data distribution and routing control unit: Based on the importance and timeliness of the data, it intelligently selects the transmission path of the device data, prioritizes the transmission of key data under low latency conditions, and uses a decision tree algorithm to ensure that high-priority data is processed first. Real-time data analysis unit: Utilizes cloud computing resources to perform real-time analysis of device data, and combines the preliminary analysis results from edge computing nodes to provide more accurate fault prediction and performance evaluation.

4. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The adaptive multimodal sensor and fusion sensing module internally employs fuzzy logic algorithms and weighted average methods for data processing and fusion. Each sensor monitors different parameters of the device, and data fusion is performed through edge computing nodes. The system combines historical data and operating environment data for status assessment. The adaptive multimodal sensor and fusion sensing module further includes: Sensor data filtering unit: The input data of the multimodal sensor is filtered using the Kalman filter algorithm to remove noise and improve the accuracy of the data; Dynamic adjustment unit: The adaptive sensor can dynamically adjust the sampling frequency and data acquisition method of the sensor based on factors such as equipment load and environmental changes, thereby obtaining more accurate status data and performing data fusion based on the correlation between sensors to optimize the sensing effect.

5. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The enhanced intelligent sensing technology and physical fault detection integration module internally performs data analysis using support vector machines and K-means clustering algorithms, enhancing the real-time monitoring of key components of the device by the enhanced sensors. It combines traditional monitoring data for multi-dimensional analysis, and the sensor data is transmitted to the cloud platform via edge computing nodes for fault diagnosis. The enhanced intelligent sensing technology and physical fault detection integration module further includes: Multi-sensor collaborative detection unit: Combining multiple data sources such as infrared thermal imaging, ultrasonic sensors, vibration sensors and temperature sensors, it uses a weighted average algorithm to fuse data, thereby improving the accuracy and reliability of fault detection; Fault Mode Recognition Unit: Utilizes support vector machine and K-means clustering algorithm to classify different equipment operating states, accurately identify equipment fault modes, and ensure rapid and accurate fault detection and early warning.

6. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The intelligent multi-level fault diagnosis and proactive repair mechanism module internally uses Bayesian networks and genetic algorithms for fault diagnosis and optimization. Through multi-level fault diagnosis, it can comprehensively evaluate the physical and functional status of the equipment to the system level. The proactive repair mechanism automatically adjusts equipment parameters or initiates repair mode based on the fault type. The intelligent multi-level fault diagnosis and proactive repair mechanism module further includes: Hierarchical diagnostic unit: Through a multi-level fault diagnosis mechanism, from the initial status monitoring of the equipment to the precise fault mode analysis, each level makes decisions through different algorithms, thereby comprehensively diagnosing equipment faults; Active Repair Strategy Unit: Based on the fault diagnosis results, the system automatically generates the optimal repair plan and adjusts the equipment operating parameters or activates the backup system in real time through an adaptive control mechanism to reduce equipment downtime and improve operation and maintenance efficiency.

7. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The quantum computing-assisted high-efficiency fault diagnosis module internally uses quantum support vector machines and quantum Monte Carlo methods for evaluation and calculation. It leverages quantum computing to rapidly process and analyze large-scale equipment data. When dealing with high-dimensional data, quantum algorithms provide more efficient computation and more accurate fault prediction than traditional algorithms. The quantum computing-assisted high-efficiency fault diagnosis module further includes: Quantum data processing unit: Utilizing the parallel computing capabilities of quantum computing, it efficiently analyzes large-scale historical and real-time monitoring data from the equipment, quickly identifying fault trends and potential fault modes. Quantum optimization algorithm unit: The quantum optimization algorithm is used to optimize the fault prediction model, improve the accuracy and calculation speed of fault diagnosis, and provide a more efficient solution, especially when dealing with high-dimensional data.

8. The IoT-based equipment operation and maintenance health status prediction and fault diagnosis system according to claim 1, characterized in that: The system further includes a data security and privacy protection module and a fault emergency response module, wherein: Data security and privacy protection module: Through encryption technology and access control mechanisms, it ensures the security of device data during cloud storage and transmission, while guaranteeing that device privacy is not leaked. This module adopts a data storage and tracking method based on blockchain technology to further enhance data security and transparency. Fault Emergency Response Module: When a serious equipment failure occurs, the system automatically activates the emergency response mechanism, immediately notifies maintenance personnel, and takes corresponding emergency measures according to the preset emergency plan to quickly restore equipment operation.

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