Quantum-meta learning fused special environment wind and light power station predictive maintenance method and system

Abstract

The invention discloses a quantum-meta learning fused special environment wind and light power station predictive maintenance method and system. The system is composed of four layers which are an application decision layer, an algorithm engine layer, a data processing layer and a hardware sensing layer from top to bottom. The hardware sensing layer is a layer of system for collecting data through various devices and sensors; the data processing layer comprises a quantum feature extraction module, a multi-modal data fusion module, a spatio-temporal data alignment module and a data quality verification module which cooperate with one another and are used for processing complex data tasks; the algorithm engine layer is a core part in the data processing and analysis system, is responsible for executing various complex calculation tasks, and comprises a meta-learning fault diagnosis module, a physical constraint modeling module, a risk assessment prediction module and a Xinjiang Uygur autonomous region feature algorithm module; and the application decision-making layer realizes efficient operation and optimization of the system and comprises a multi-objective optimization decision-making module, a resource scheduling module, a Uighur-Chinese bilingual interaction module and an execution effect evaluation module.

Description

TECHNICAL FIELD

[0001] The application belongs to the application field of intelligent cyber-physical systems, and relates to an invention scheme and implementation of a predictive maintenance system for a wind-solar power station in a special environment, which can be applied to the application fields of a wind-solar power station system, a photovoltaic power station system, an intelligent manufacturing system, and a robot system. The proposed wind-solar power station application scheme specifically implements quantum-meta learning technology in wind turbines and photovoltaic power stations, and realizes full-chain intelligent operation and maintenance from device-level monitoring to station-level optimization through dedicated hardware deployment and customized software processes. The application scheme is optimized and adapted for special environments such as the Xinjiang Uygur Autonomous Region. BACKGROUND

[0002] A wind-solar power station system is the main production form of the new energy industry, and its operation is an important routine work, and its operation safety is also important. However, such a system operation and maintenance system has some inherent problems. The core pain points of wind-solar operation and maintenance are that fault data is scarce, and actual fault samples are few; due to system complexity, faults are diverse; the environment is harsh, with large temperature differences, sand weather, and high temperature and dryness. Therefore, comprehensive and early fault detection is both important and innovative. Therefore, the invention scheme is proposed to realize "small data and large information" through quantum enhancement, to realize "instant recognition" of adverse conditions through meta learning, and to ensure "robust reasoning" through physical constraints to enable the system to operate normally. The characteristics of the application lie in deeply integrating the information processing advantage of quantum computing and the small sample self-adaptive ability of meta learning, and considering extreme environmental conditions to construct an intelligent diagnosis system that can realize "super-early precise early warning" from "trace fault data" and is resistant to harsh environments. SUMMARY

[0003] The application discloses a quantum-meta learning fusion special environment wind and light power station predictive maintenance method and system, which comprises four levels from top to bottom, namely, an application decision layer, an algorithm engine layer, a data processing layer and a hardware sensing layer; the hardware sensing layer is a system for collecting data through various devices and sensors, and comprises a wind turbine blade monitoring module, a wind turbine transmission chain monitoring module, a wind turbine tower monitoring module, a photovoltaic component monitoring module, a photovoltaic support monitoring module and an environmental meteorological monitoring module; the wind turbine blade monitoring module is used for monitoring the state of the wind turbine blade to ensure normal operation; the wind turbine transmission chain monitoring module is responsible for monitoring the working condition of the internal transmission system of the wind turbine; the wind turbine tower monitoring module checks the structural health of the wind turbine tower to prevent accidents caused by damage; the photovoltaic component monitoring module is used for detecting the performance and state of the solar cell panel (photovoltaic panel); the photovoltaic support monitoring module pays attention to whether the support for the photovoltaic panel is stable; the environmental meteorological monitoring module collects environmental information such as weather, temperature and wind speed to provide a reference for equipment operation; the data processing layer comprises a quantum feature extraction module, a multi-modal data fusion module, a space-time data alignment module and a data quality verification module; the modules are jointly used for processing complex data tasks; the quantum feature extraction module is responsible for extracting quantum-related characteristics from data for analyzing quantum computing or quantum physics field data; the multi-modal data fusion module integrates data from different sources or forms (such as images, sounds and texts) together to facilitate unified analysis; the space-time data alignment module is used for processing data associated with time and space to ensure that the data are consistent in the time axis and spatial coordinates; the data quality verification module checks the accuracy and integrity of the data to ensure the reliability of subsequent analysis results; quantum features refer to characteristics or parameters related to quantum mechanics, such as quantum states and superposition states; in data processing, extraction of these features may help to solve scientific problems in specific fields; multi-modal data refer to data from different modes or types, such as images, audio, video and texts; fusion of the data can improve the comprehensiveness and accuracy of analysis; space-time data refer to data related to time and space, such as geographical position information changing with time; alignment of the data is to ensure the synchronization of the data in the time axis and spatial coordinates; data quality refers to the accuracy, integrity and consistency of the data; verification of the data quality is an important step in data processing, which can avoid analysis deviation caused by data errors; the algorithm engine layer is a core part of the data processing and analysis system, and is responsible for performing various complex calculation tasks, including a meta learning fault diagnosis module, a physical constraint modeling module, a risk assessment prediction module and a Xinjiang Uygur autonomous region characteristic algorithm module; the meta learning fault diagnosis module is a fault detection and diagnosis tool based on meta learning technology; meta learning is a machine learning method aiming to learn general knowledge from multiple tasks to quickly adapt to new tasks; the physical constraint modeling module is a method for integrating physical laws or prior knowledge into a mathematical model to ensure that the output of the model meets the constraint conditions of the physical world;The risk assessment and prediction module is a tool used to assess potential risks and predict future events, typically combining statistical analysis and machine learning techniques. The Xinjiang Uygur Autonomous Region-specific algorithm module is designed for the specific needs or characteristics of the Xinjiang Uygur Autonomous Region, potentially involving factors such as geography, climate, and culture. The application decision layer enables efficient system operation and optimization, including a multi-objective optimization decision module, a resource scheduling module, a Uygur-Chinese bilingual interaction module, and an execution effect evaluation module. The multi-objective optimization decision module finds the optimal balance among multiple objectives, helping the system make optimal decisions. The resource scheduling module allocates and manages resources, ensuring their rational utilization. The Uygur-Chinese bilingual interaction module supports interaction between Uygur and Chinese, facilitating system use for users of different languages. The execution effect evaluation module analyzes and evaluates the system's execution results to improve subsequent decision-making.

[0004] The functions of each layer and module are now represented by mathematical formulas. Table 1 shows the overall formula hierarchy for each layer, and Table 2 shows the relationships between the formulas. Table 1 illustrates the four layers of the system and the functions, inputs, and outputs of each layer; Table 2 further explains the variable transfer relationships between different formulas. Overall, this system forms a complete process from raw data acquisition to final decision execution by processing data layer by layer. The hardware sensing layer is responsible for acquiring physical signals and converting them into quantum state data. The data processing layer performs feature extraction and standardization on this data. The algorithm engine layer uses feature vectors and environmental data for intelligent diagnosis and prediction. The application decision layer generates specific maintenance plans and scheduling instructions based on the diagnostic results. Table 2 details the variable transfer paths between different formulas, such as blade data streams and vibration data streams, showing how the modules in the system work together. The hardware sensing layer is the first layer of the system. Its main function is to acquire physical signals through sensors and convert these signals into quantum state data or measured values, providing a foundation for subsequent data processing. In the data processing layer, this process refers to extracting useful information from the raw data and converting it into standardized feature vectors for subsequent analysis and modeling. The algorithm engine layer calculates the probability and risk level of failures by analyzing feature vectors and environmental data, thus providing a basis for decision-making. The variable transfer path represents how information is transferred between different formulas through variables. Quantum state data is a special form of data, typically used to describe the mathematical expression of a system's state; here, it is derived from physical signals collected by sensors.

[0005] Table 1. Formula Attribution for Each Layer

[0006]

[0007]

[0008] Table 2 Relationships between formulas

[0009]

[0010] The mathematical description of the modules in the hardware perception layer is as follows.

[0011] Wind turbine blade monitoring module: Quantum encoding formula for blade lidar point cloud (formula F1), the symbols of which are shown in Table 3.

[0012]

[0013] Table 3. Explanation of Variables in the Quantum Encoding Formula for Blade LiDAR Point Cloud

[0014]

[0015] Wind turbine drive chain monitoring module: The formula for acquiring vibration signals of the drive chain (formula F2) is shown in Table 4.

[0016] v(t) = [v x (t), v y (t), v z (t)] T +n vibration (t) (F2)

[0017] Table 4. Explanation of Variables in the Formula for Acquiring Vibration Signals of the Transmission Chain

[0018]

[0019] Fan drive chain monitoring module: Gearbox temperature field monitoring formula (formula F3), its symbols are shown in Table 5.

[0020] T gearbox (x, t) = T sensor (x i ,t)+δT interp (x, t) (F3)

[0021] Table 5. Explanation of Variables in the Formula for Gearbox Temperature Field Monitoring

[0022] Wind turbine tower monitoring module: Tower deformation monitoring formula (formula F4), its symbols are shown in Table 6.

[0023] p tower (t)=[x top (t), y top (t), z top (t)] T -p base (F4)

[0024] Table 6. Explanation of Variables in the Formula for Tower Deformation Monitoring Using Beidou Navigation

[0025]

[0026] Photovoltaic module monitoring module: Infrared thermal imaging quantum processing (formula F5), its symbol is shown in Table 7.

[0027]

[0028] Table 7 Explanation of Quantum Processing Variables in Infrared Thermal Imaging

[0029]

[0030] Photovoltaic module monitoring module: String current-voltage (Ⅳ) characteristic curve monitoring (formula F6), its symbols are shown in Table 8.

[0031]

[0032] Table 8. Explanation of Variables Monitored by String Current-Voltage (IV) Curves

[0033]

[0034] Photovoltaic support monitoring module: Visual deformation monitoring of the support (formula F7), its symbol is shown in Table 9.

[0035]

[0036] Table 9. Explanation of Variables for Visual Deformation Monitoring of the Bracket

[0037]

[0038] Environmental meteorological monitoring module: Sand and dust concentration multi-sensor fusion (formula F8), its symbols are shown in Table 10.

[0039]

[0040] Table 10 Explanation of Multi-Sensor Fusion Variables for Dust Concentration

[0041]

[0042] Environmental meteorological monitoring module: meteorological parameter monitoring (formula F9), its symbols are shown in Table 11.

[0043] E(t) = [v wind (t),θ wind (t), I solar (t),T ambient (t), RH(t)] T (F9)

[0044] Table 11 Explanation of Meteorological Parameter Monitoring Variables

[0045]

[0046] The mathematical descriptions of the modules in the data processing layer are as follows (Formulas F10-F14).

[0047] Quantum feature extraction module: multimodal quantum feature fusion (formula F10), its symbol is shown in Table 12.

[0048]

[0049] Table 12 Explanation of Multimodal Quantum Feature Fusion Variables

[0050]

[0051]

[0052] Multimodal data fusion module: vibration spectrum feature extraction (formula F11), the symbols of which are shown in Table 13.

[0053]

[0054] Table 13 Explanation of Variables Extracted from Vibration Spectrum Features

[0055]

[0056] Multimodal data fusion module: thermal image temporal feature extraction (formula F12), its symbols are shown in Table 14.

[0057]

[0058] Table 14 Explanation of Variables for Extracting Temporal Features from Thermal Imaging

[0059]

[0060] Spatiotemporal data alignment module: multi-scale spatiotemporal alignment (formula F13), its symbols are shown in Table 15.

[0061]

[0062] Table 15 Explanation of Multi-Scale Spatiotemporal Alignment Variables

[0063]

[0064] Data quality verification module: Data quality assessment (formula F14), its symbols are shown in Table 16.

[0065]

[0066] Table 16 Explanation of Data Quality Assessment Variables

[0067]

[0068] The algorithm engine layer formulas (F15-F26) are as follows.

[0069] Meta-learning fault diagnosis module: Meta-learning optimization objective (formula F15), its symbol is shown in Table 17.

[0070]

[0071] Table 17 Explanation of Objective Variables for Meta-Learning Optimization

[0072]

[0073]

[0074] Meta-learning fault diagnosis module: small sample rapid adaptation (formula F16), its symbol is shown in Table 18.

[0075]

[0076] Table 18 Explanation of Variables for Rapid Adaptation in Small Samples

[0077]

[0078] Meta-learning fault diagnosis module: Fault probability prediction (Formula F17), its symbols are shown in Table 19.

[0079] P(fault|X) = softmax(W·f) θ′ (X)+b) (F17)

[0080] Table 19 Explanation of Fault Probability Prediction Variables

[0081]

[0082] Physical constraint modeling module: physical model of blade crack propagation (formula F18), its symbols are shown in Table 20.

[0083]

[0084] Table 20: Variable Description for the Physical Model of Blade Crack Propagation

[0085]

[0086]

[0087] Physical constraint modeling module: Gearbox thermal-vibration coupling model (formula F19), its symbols are shown in Table 21.

[0088]

[0089] Table 21 Variable Description Table for Gearbox Thermal-Vibration Coupling Model

[0090]

[0091] Physical constraint modeling module: Photovoltaic heat conduction equation formula (F20), its symbols are shown in Table 22.

[0092]

[0093] Table 22 Explanation of Variables in Photovoltaic Heat Conduction Equation Formula

[0094]

[0095]

[0096] Risk assessment and prediction module: Comprehensive crack risk assessment (formula F21), its symbols are shown in Table 23.

[0097]

[0098] Table 23 Explanation of Variables for Comprehensive Crack Risk Assessment

[0099]

[0100] Risk assessment and prediction module: tower safety factor (formula F22), its symbol is shown in Table 24.

[0101]

[0102] Table 24 Explanation of Tower Safety Factor Variables

[0103]

[0104] Photovoltaic hot spot risk prediction (formula F23), its symbols are shown in Table 25.

[0105]

[0106] Table 25 Explanation of Variables for Predicting Photovoltaic Hot Spot Risk

[0107]

[0108] The Xinjiang Uygur Autonomous Region's unique algorithm module: Sandstorm Impact Prediction (Formula F24), its symbols are shown in Table 26.

[0109]

[0110] Table 26 Explanation of Variables Predicting the Impact of Dust Storms

[0111]

[0112] The Xinjiang Uygur Autonomous Region's unique algorithm module: Extreme Temperature Equipment Protection (Formula F25), its symbols are shown in Table 27.

[0113]

[0114] Table 27 Explanation of Protective Variables for Extreme Temperature Equipment

[0115]

[0116]

[0117] Clean optimization for arid regions (Formula F26), its symbols are shown in Table 28.

[0118]

[0119] Table 28 Explanation of Clean Energy Optimization Variables in Arid Regions

[0120]

[0121] The decision-making formula (F27-F32) is applied as follows.

[0122] Multi-objective optimization decision: Maintenance scheduling multi-objective optimization (Formula F27), its symbols are shown in Table 29.

[0123]

[0124] Table 29 Explanation of Multi-Objective Optimization Variables for Maintenance Scheduling

[0125]

[0126]

[0127] Multi-objective optimization decision-making: resource-constrained optimization (Formula F28), its symbols are shown in Table 30.

[0128]

[0129] Table 30 Explanation of Resource Constraint Optimization Variables

[0130]

[0131] The Uyghur-Chinese bilingual interactive module is a Uyghur-Chinese machine translation module (Formula F29), whose symbols are shown in Table 31.

[0132]

[0133] Table 31 Explanation of Variables in Uyghur-Chinese Machine Translation

[0134]

[0135] Multimodal instruction generation (formula F30), its symbols are shown in Table 32.

[0136]

[0137] Table 32 Description of Variables Generated by Multimodal Commands

[0138]

[0139]

[0140] Execution effect evaluation module: Maintenance effect quantification (formula F31), its symbols are shown in Table 33.

[0141]

[0142] Table 33 Explanation of Quantitative Variables for Maintenance Effectiveness

[0143]

[0144] Execution effect evaluation module: System performance tracking (Formula F32), its symbols are shown in Table 34.

[0145] P system (t)=αP availability (t)+βP efficiency (t)+γP reliability (t) (F32)

[0146] Table 34 System Performance Tracking Variables Explanation Table

[0147]

[0148]

[0149] The predictive maintenance method and system for wind and solar power plants in special environments, based on quantum-meta-learning fusion, operates as follows: After the hardware perception layer is activated, all sensor networks are initialized first, followed by the parallel activation of six monitoring systems. Wind turbine blade monitoring uses quantum lidar to scan the blades and encode quantum states; transmission chain monitoring collects vibration and temperature data; tower monitoring acquires deformation data through the BeiDou system; photovoltaic module monitoring collects infrared thermal images and string current-voltage characteristic curves; support monitoring performs visual deformation detection; and environmental monitoring collects dust concentration and meteorological parameters. After quality checks, qualified data is packaged and transmitted to the data processing layer, while unqualified data triggers a re-acquisition process. Upon receiving data from the hardware layer, the data processing layer categorizes and routes data according to data type: quantum data enters the quantum feature extraction module for quantum principal component analysis and dimensionality reduction; traditional sensor data enters the multimodal fusion module to integrate vibration and thermal image features; and spatiotemporal data enters the alignment module for timestamp synchronization and coordinate registration. The output data from each module is uniformly entered into the quality verification module to evaluate noise and synchronization errors. Based on the quality grading results, high-quality data is directly fused and output, while low-quality data undergoes a repair process and is re-evaluated. Finally, a standardized feature matrix is ​​generated and transmitted to the algorithm engine layer. After receiving feature data, the algorithm engine layer executes four analysis modules in parallel: the meta-learning diagnostic module quickly adapts and calculates the failure probability using a small sample support set; the physical constraint modeling module runs crack propagation, thermal-vibration coupling, and heat conduction simulations; the risk assessment module comprehensively calculates the risk levels of cracks, towers, and hot spots; and the Xinjiang Uygur Autonomous Region-specific algorithm module handles sandstorm prediction, temperature protection, and cleaning optimization. The analysis results from each module are then aggregated and transmitted to the application decision layer, providing a complete basis for operation and maintenance decisions. After receiving the algorithm layer results, the application decision layer uses a multi-objective optimization module to establish cost, safety, and efficiency objective functions, solving for the optimal maintenance plan under constraints; a resource scheduling module analyzes manpower, spare parts, and equipment resources, optimizing time window arrangements; a Uyghur-Chinese bilingual module translates technical instructions into multimodal operation guidance; and an execution effect module collects on-site feedback data, quantifies maintenance effectiveness and system performance, updates the knowledge base or re-decides decisions based on the evaluation results, forming a closed-loop optimization process.

[0150] The fundamental breakthrough of this system lies not in piling up advanced technologies, but in weaving together, for the first time, three seemingly discrete fields—quantum perception, meta-learning, and physical constraints—into an interconnected, positively cyclical intelligent core. The core methodology's interconnected structure is a "triangular closed loop": quantum perception provides meta-learning with high-energy data whose information concentration far exceeds that of traditional data; meta-learning, in turn, uses this high-energy data to refine accurate diagnoses with a very small number of fault samples; and physical constraints act as anchors, fixing this data-driven judgment within the framework of engineering principles. These three elements are not simply relayed in an assembly line, but rather constitute a mutually verifying, continuously evolving intelligent symbiosis.

[0151] This core intelligent methodology is physically implemented through a four-layer architecture. The hardware perception layer is the sensory system, with quantum lidar and quantum infrared sensors deployed throughout the site, acting as specialized organs that transform the physical world into quantum state data. The data processing layer is its unique neural network, with quantum principal component analysis as the core algorithm, specifically responsible for refining and fusing this raw quantum information to form standardized features suitable for advanced cognitive processing. The algorithm engine layer is the system's brain, where a meta-learning rapid diagnosis module and a physical mechanism model run in parallel. The former handles agile insight, while the latter handles rational verification; their outputs converge in the risk assessment module to form the final judgment. Finally, the application decision layer is the system's "decision and execution center," transforming the brain's judgment into the optimal maintenance strategy and seamlessly embedding it into local workflows through a Uyghur-Chinese bilingual interactive interface module.

[0152] The innovative approach to predictive maintenance of wind and solar power plants in special environments, which integrates quantum and meta-learning, lies in its vertically integrated architecture consisting of a quantum sensing layer, a meta-learning diagnostic layer, and a physical constraint optimization layer. It employs quantum lidar and quantum infrared sensors to acquire multimodal data, performs feature fusion through quantum principal component analysis, and establishes a meta-learning optimization objective function based on physical constraints. Combined with a wide-temperature-range quantum computing node hardware structure and a dust-resistant sensor network, it forms a complete system encompassing quantum-enhanced stereo perception, a small-sample fault diagnosis engine, and a multi-objective optimization decision-making module. The quantum-classical hybrid computing platform enables the collaborative execution of quantum feature extraction and classical meta-learning algorithms, while the Uyghur-Chinese bilingual intelligent interactive interface generates bilingual operation and maintenance commands through a dedicated machine translation model. Attached Figure Description

[0153] Appendix Figure 1 This is a schematic diagram of the structure of the predictive maintenance method and system for wind and solar power plants in special environments, which incorporates quantum-meta-learning fusion as described in this invention.

[0154] Appendix Figure 2 This is a flowchart illustrating the hardware perception layer of the predictive maintenance method and system for wind and solar power plants in special environments, which incorporates quantum-meta-learning fusion as described in this invention.

[0155] Appendix Figure 3 This is a flowchart illustrating the data processing layer of the predictive maintenance method and system for wind and solar power plants in special environments, which incorporates quantum-meta-learning fusion as described in this invention.

[0156] Appendix Figure 4 This is a flowchart of the algorithm engine layer of the predictive maintenance method and system for wind and solar power plants in special environments, which incorporates quantum-meta-learning fusion as described in this invention.

[0157] Appendix Figure 5This is a flowchart illustrating the application decision layer of the predictive maintenance method and system for wind and solar power plants in special environments, which incorporates quantum-meta-learning fusion as described in this invention. Detailed Implementation Specific Implementation Method 1

[0159] Based on the aforementioned predictive maintenance method and system for wind and solar power plants in special environments that integrates quantum-meta-learning, the implementation method is as follows: A medium-sized wind and solar power plant consisting of 50 3MW wind turbine generators and a 100MW photovoltaic array is deployed with a quantum-meta-learning intelligent operation and maintenance system. The system hardware includes: 5 sets of quantum lidar UAV inspection systems (1 set per 10 wind turbines), 2000 quantum-enhanced sensor nodes (400 for wind turbine drive trains and 1600 for photovoltaic strings), 150 sets of BeiDou deformation monitoring terminals (3 sets per wind turbine), 10 edge quantum computing nodes (operating at -40℃ to 60℃), 1 central quantum-classical hybrid computing platform, 2 sets of anti-dust infrared thermal imaging UAV systems, and 1 Uyghur-Chinese bilingual interactive terminal network. A quantum laser monitoring system for the blades is deployed on 50 wind turbines, each equipped with 8 vibration-temperature composite sensors and 3 BeiDou deformation monitoring points. In the photovoltaic area, 2000 intelligent optimizers are deployed for component-level monitoring, with 40 visual deformation monitoring points. Twenty meteorological and dust monitoring stations are deployed throughout the site. The sensor network utilizes a hybrid 5G and fiber optic network, achieving data transmission latency of <100ms. A central computer room is built at the booster station, deploying one 50-qubit superconducting quantum computing system and one GPU cluster (capable of performing 50 quadrillion floating-point operations per second). Ten edge quantum computing nodes are deployed in the wind turbine cluster and photovoltaic area, each with a computing power of 256 quadrillion operations per second. A data center storage system with a capacity of 5PB is constructed, supporting hybrid storage of time-series data and quantum state data.

[0160] The software system architecture consists of a hardware perception layer deploying a quantum data acquisition and preprocessing system, a data processing layer running feature extraction and fusion algorithms, an algorithm engine layer integrating meta-learning diagnostics and physical constraint optimization modules, and an application decision layer realizing multi-objective operation and maintenance optimization and bilingual interaction. The software platform adopts a microservice architecture, comprising six core modules: equipment management, status monitoring, fault diagnosis, predictive maintenance, resource scheduling, and performance analysis. The core algorithm system implements a quantum principal component analysis feature fusion algorithm to convert multimodal sensor data into a 32-dimensional unified feature space; deploys a model-agnostic meta-learning framework to achieve rapid fault identification with 3-5 small samples; establishes a physical constraint optimization objective function, embedding equipment operation physical laws such as wind turbine blade crack propagation models, gearbox thermal-vibration coupling equations, and photovoltaic heat conduction equations; and employs a multi-objective Pareto optimization algorithm for maintenance decisions.

[0161] The backend framework adopts the Spring Cloud microservice architecture; real-time computing uses the Apache Flink stream processing engine. Data storage uses the TimescaleDB time-series database plus the MongoDB document database. Quantum computing utilizes the Qiskit quantum computing framework and a custom quantum algorithm library. Message queues use the Apache Kafka distributed messaging system. Containerization is handled by Kubernetes cluster management; the frontend framework is Vue.js + Three.js for 3D visualization.

Claims

1. The predictive maintenance method and system for wind and solar power plants in special environments, which integrates quantum sensing, meta-learning, and physical constraints, is based on the fact that these three seemingly discrete fields are woven into an interlocking and positively cyclical intelligent core. The core method has a triangular closed loop structure: quantum sensing provides meta-learning with high-energy data with a much higher information concentration than traditional data; meta-learning, in turn, uses this high-energy data to refine accurate diagnoses with very few fault samples; and physical constraints act as anchors, fixing this data-driven judgment within the framework of engineering laws. The three are not simply relayed in an assembly line, but constitute a mutually verifying and continuously evolving intelligent symbiosis. This core intelligent methodology is physically implemented through a four-layer architecture. The hardware perception layer is the sensory system, with quantum lidar and quantum infrared sensors deployed throughout the site, serving as specialized organs that transform the physical world into quantum state data. The data processing layer is its unique neural network, with quantum principal component analysis as the core algorithm, specifically responsible for refining and fusing this primordial quantum information to form standardized features that can be processed by advanced cognition. The algorithm engine layer is the brain of the system, running in parallel a meta-learning rapid diagnosis module and a physical mechanism model. The former is responsible for agile insight, while the latter is responsible for rational verification. The outputs of both converge in the risk assessment module to form the final judgment. Finally, the application decision layer is the decision-making and execution center of the system. It transforms the brain's judgment into the optimal maintenance strategy and seamlessly integrates it into the local workflow through a Uyghur-Chinese bilingual interactive interface module. In the implementation of 50 wind turbines and a 100MW photovoltaic array, this methodological and hierarchical architecture has shown its effectiveness; 5 sets of quantum lidar drones have replaced human eyes and ordinary instruments to perform millimeter-level quantum scanning of wind turbine blades; 2,000 quantum-enhanced sensors continuously collect high-energy data in harsh environments; and the quantum-classical hybrid computing platform deployed in the station runs that unique triangular closed-loop intelligent kernel.

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