572 results about "Preventive maintenance" patented technology

The invention discloses a power grid equipment intelligent operation and maintenance method based on a large language model and a knowledge graph, and relates to the technical field of intelligent power grid operation and maintenance, and the method comprises the steps: carrying out the operation and maintenance of power grid equipment through a power grid operation and maintenance knowledge graph constructed through a large language model and a knowledge federation technology, the power grid equipment operation and maintenance comprises one or more of health state evaluation, fault risk prediction and early warning, intelligent operation and maintenance strategy generation and health degree dialogue query of the power grid equipment. According to the invention, a power grid operation and maintenance mode can be effectively promoted to be transformed and upgraded from a traditional manual experience type and a passive maintenance type to a data-driven, intelligent and active preventive maintenance mode. Key intelligent operation and maintenance technical support is provided for building a novel electric power system with new energy as a main body, the novel electric power system is assisted to achieve the development goals of being safer, more efficient, cleaner and lower in carbon, and important industry strategic significance and social contribution are achieved.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention discloses a ship heat exchanger heat transfer performance simulation method, particularly relates to the field of heat transfer, and comprises the steps of multi-source data acquisition, characteristic parameter calculation, heat transfer stability diagnosis, comprehensive heat transfer degradation evaluation, structure reliability prediction and system efficiency comprehensive evaluation. Multi-dimensional data synchronous acquisition is realized through a multi-source sensor network, a dynamic coupling analysis model is constructed to accurately calculate the heat transfer stability and the comprehensive degradation index, a three-level early warning mechanism is established to improve the state evaluation accuracy, turbulence attenuation characteristics and vibration data are fused to predict the structural reliability, and the reliability of the system is improved. And closed-loop management and control is formed by combining an efficiency index triple evaluation system, so that the spanning from single alarm to preventive maintenance is realized, the health management capability of the system is comprehensively enhanced, and full-life-cycle dynamic optimization support is provided for the ship heat exchanger.

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention belongs to the technical field of vehicle fault prediction, and discloses a vehicle fault evolution law modeling method and system based on digital twinning. According to the method, vehicle running state data are obtained and mapped to a digital twin virtual state space, a fault feature evolution sequence is extracted in combination with historical fault records, a state transition topological graph is constructed, the spatial distance between a real-time state and a feature vector group is calculated to match an evolution path, and the state transition topological graph is obtained. And a fault evolution prediction result is determined based on the corresponding timestamp information, and a preventive maintenance strategy is generated. According to the invention, accurate modeling of the vehicle fault evolution rule is realized, and the fault prediction accuracy and the maintenance efficiency are improved.

The invention discloses a municipal facility maintenance strategy optimization method and system based on big data driving, and relates to the technical field of urban operation and maintenance management and big data decision support, and the method comprises the steps: carrying out the data cleaning and standardization processing of multi-source operation state data, and forming a facility state database; the method comprises the following steps: extracting facility degradation trend characteristics, establishing a facility state degradation prediction model, calculating a comprehensive health degree index, establishing a multi-objective optimization model based on prediction of a degradation trend, solving an optimal maintenance opportunity and a resource allocation scheme, and generating a maintenance strategy list. And comparing and analyzing the maintenance strategy list with historical maintenance effect data in the facility state database to obtain strategy optimization feedback parameters, and dynamically updating parameter setting of the facility state degradation prediction model. According to the invention, the management mode from passive fault repair to active preventive maintenance is changed.

The invention relates to the technical field of equipment fault diagnosis, and discloses an intelligent fault detection method for a speed reducer, and the method comprises the steps: extracting fault feature knowledge from a source domain based on a domain adaptive transfer learning algorithm, migrating the fault feature knowledge to a target domain, and generating a synthetic fault sample based on physical model constraints; constructing interaction influence among the multi-subject causal capture equipment; executing causal intervention and anti-factual reasoning, and determining a fault root cause; constructing a fault knowledge graph and continuously optimizing the fault knowledge graph; and constructing a preventive maintenance decision system to generate an optimal maintenance strategy. According to the method, high-accuracy fault diagnosis can be realized under the condition of sample scarcity, interaction influence among multiple devices is analyzed, fault root causes are traced, and reliable preventive maintenance decision support is provided.

The invention discloses an electrical pipeline fault early warning method and system, and relates to the technical field of electrical pipelines, and the method comprises the steps: collecting electrical pipeline data, carrying out the preprocessing of the collected data, packaging the processed data, transmitting the packaged data to a central processing unit, analyzing the data, extracting characteristic parameters, and combining a predefined rule base, the method comprises the following steps: carrying out fault feature identification and abnormity determination, matching a fault mode based on identification features, evaluating a fault type and a severity degree, predicting a fault probability in combination with a historical data trend, automatically generating an early warning message when real-time data exceeds a preset threshold value, sending structured early warning information through a multi-mode communication channel, and tracking and confirming a state. And triggering preset emergency operation, and meanwhile, providing a manual intervention interface and recording a processing process. By monitoring the parameters of the electrical pipeline in real time and triggering an early warning mechanism, the safety problem is identified and processed before the fault occurs, the downtime is reduced, the reliability of the pipeline is improved, and preventive maintenance is realized so as to reduce the emergency maintenance cost.

The invention discloses a multi-element coupling heat supply monitoring system based on a meteorological condition prediction model, belongs to an industrial control system, realizes deep coupling of meteorological data and an equipment operation state, and solves a fusion error problem caused by fixed meteorological element weight in traditional monitoring to a certain extent. By establishing the efficiency evaluation model containing the environment compensation mechanism, the interference of external temperature fluctuation on equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. A multi-dimensional health assessment system is adopted to integrate equipment maintenance data and real-time operation parameters, potential fault risks can be recognized in advance, and data support is provided for equipment preventive maintenance.

The invention belongs to the field of data processing, and provides an equipment comfort measurement fault identification method, system and equipment, and the method comprises the steps: obtaining a plurality of modal sequences which are consistent with a sampling signal segment in length and are aligned in bit sequence through variational modal decomposition; traversing each sampling signal segment according to a bit sequence to form a signal segment extended wave; extracting the peak value of the signal segment extended wave and the characteristic of the bit sequence of the peak value to generate an extended wave ratio sequence; the difference between the wave ratio sequence of each sampling signal segment and the original sequence is used as the wave ratio fall; and screening each sampling signal segment according to the wave ratio fall by using the median of the wave ratio fall of all the sampling signal segments, and identifying a signal comfort abnormal segment. The method is not sensitive to amplitude fluctuation of random noise, but is more sensitive to envelope impact and bit sequence displacement of a specific frequency band, can prompt slight problems such as guide shoe clearance, guide rail joint impact and abnormal transmission of a traction machine in the early stage, and is beneficial to preventive maintenance.

The invention provides a power distribution control cabinet fault response method and device, and relates to the technical field of electrical automation control, and the key points of the technical scheme are that a weighted directed graph model representing the relation of internal components of a power distribution control cabinet is constructed in advance; mapping component fault information predicted by the digital twin system to attributes of corresponding nodes in the weighted directed graph model; identifying a fault path or a fault cluster satisfying a predetermined condition; for each identified fault path or fault cluster, calculating a composite risk index according to the prediction probability of the nodes included in the fault path or fault cluster, the weight of the connection edge and the severity of the influence on the system; and generating early warning information according to the composite risk index. According to the power distribution control cabinet fault response method and device provided by the invention, a potential fault propagation path or a fault aggregation area can be identified based on physical, electrical and influence relationships among components, and a composite risk formed by a plurality of potential fault combinations is quantified, so that a preventive maintenance decision is guided.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention provides an internal damage assessment method and system based on a multi-modal graphite component, and relates to the technical field of material deterioration assessment, and the method comprises the steps: generating a standardized multi-modal data stream through synchronously collecting five-modal data; extracting acoustic and electro-thermal physical features from the multi-modal data stream, and constructing a multi-dimensional damage sensitive feature vector; inputting the multi-dimensional damage sensitive feature vectors into a deep learning network corrected by a physical constraint layer, and extracting crack quantization parameters from the deep learning network; inputting the crack quantization parameters into a digital twin driven irradiation-creep-fatigue coupling damage model, and calculating the residual life and failure probability of the overall structure; and in combination with the space coordinate information of the three-dimensional crack probability field, a preventive maintenance instruction and schedule for the high-risk modular component are generated and optimized. According to the method, accurate perception, quantitative evaluation, accurate prognosis and optimized decision making of the internal damage of the graphite component are realized, the resource utilization efficiency is maximized, and unnecessary shutdown loss is reduced.

The invention provides a progress and resource management linkage application system based on BIM, relates to the technical field of building management, and is suitable for full life cycle management of design, construction and operation of a building. The system comprises a demand modeling module, an intelligent equipment optimization module, a collision detection and progress optimization module, a multi-professional data integration and real-time simulation module, a building full-life-cycle intelligent management module and the like. By optimizing arrangement and streamline design of equipment, the space utilization rate and the medical service efficiency are improved; in the construction stage, a collision detection function and a digital twinning technology of the BIM are utilized to realize multi-specialty collaborative design and progress optimization; in the operation stage, the operation state of the equipment is monitored in real time in combination with an Internet of Things sensor, a preventive maintenance plan is generated, and energy consumption management is optimized through energy consumption data analysis. The system improves the design precision and construction efficiency of the building and the intelligent level of equipment management and operation maintenance, and has a wide application prospect.

The invention relates to a vehicle remote diagnosis method and system and a medium, and the method comprises the steps: collecting diagnosis data from a vehicle, and the diagnosis data comprises vehicle controller area network data, vehicle electronic control unit logs, fault diagnosis codes and vehicle driving data; transmitting the diagnosis data to a cloud platform through a wireless network; analyzing the diagnosis data on the cloud platform by using an artificial intelligence model, wherein the artificial intelligence model comprises a fault diagnosis model and a fault prediction model; generating a diagnosis result based on an analysis result of the artificial intelligence model, wherein the diagnosis result comprises current fault diagnosis and potential fault prediction; sending the diagnosis result to the vehicle or the user equipment; and executing corresponding maintenance or preventive maintenance operation based on the diagnosis result. According to the invention, real-time monitoring, remote diagnosis and predictive maintenance of vehicle faults can be realized, so that the diagnosis efficiency is improved, and the maintenance cost is reduced.

The invention belongs to the technical field of wind turbine generator maintenance, and particularly relates to an offshore wind turbine generator two-stage rolling maintenance method considering subsystem correlation, which comprises the following steps: S1, constructing a comprehensive reliability model; s2, taking the minimization of the total maintenance cost in the operation cycle of the wind turbine generator as a target, and forming a target optimization problem based on the comprehensive reliability model; solving the target optimization problem to form a first-stage long-term maintenance strategy; s3, when an operation window meeting the storm accessibility condition appears, determining a subsystem of which the current reliability is lower than a preventive maintenance threshold value; s4, the opportunity maintenance benefit ratio is calculated, and if the opportunity maintenance benefit ratio is larger than a processing threshold value, advanced maintenance of the subsystem is triggered; and S5, after the advanced maintenance is executed each time, the optimal preventive maintenance time point is solved again so as to dynamically update the subsequent maintenance plan. According to the method, the fault coupling and degradation evolution law between subsystems can be accurately described, and the storm accessibility window is actively utilized to dynamically adjust the maintenance plan.

The invention discloses an intelligent cable life prediction method and system based on data driving and a cable, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting the current, voltage, partial discharge, temperature, environment temperature and humidity and other multi-dimensional historical operation time sequence data of the cable; a cable comprehensive state feature sequence is generated through processing of a multi-source heterogeneous data fusion technology; extracting a load fatigue cumulant, an insulation degradation index and a thermal stress damage degree based on an aging mechanism to form a degradation feature vector; inputting the vector into a pre-training model for time sequence evolution deduction, and outputting probability distribution of residual life and a key failure time node; and generating a preventive maintenance decision scheme including a maintenance window, an operation priority and inventory early warning. According to the method, more accurate evaluation and risk quantification of the cable life are realized through mechanism characteristic driving and probabilistic prediction, and a direct decision basis is provided for active operation and maintenance.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

The invention relates to a power transmission line fault risk grading evaluation method based on multistage early warning, and belongs to the field of power system safety. Comprising the following steps: acquiring an insulator surface pollutant data generation matrix; extracting spatial distribution characteristics of pollutants; leakage current waveforms under different humidity are calculated; extracting a leakage current harmonic component and pulse characteristics; dividing pollution flashover risk grades; matching the maintenance strategy and generating an equipment scheduling instruction; and performing fault risk grading evaluation to generate a report. According to the method, through multi-source data fusion and dynamic modeling, accurate evaluation and preventive maintenance of the pollution flashover risk of the insulator are realized, and the safety and reliability of a power system are improved.

The invention provides a preventive maintenance method, which comprises the following steps of: fusing data of a plurality of data sources to obtain fused data; performing feature extraction on the fused data to obtain feature data of at least one dimension; evaluating a target system and target equipment in the target system based on the feature data of at least one dimension to obtain multi-level health degree evaluation information; and generating preventive maintenance information based on the multi-level health degree assessment information. The invention further provides electronic equipment, a readable medium and a program product.

The invention discloses a laboratory detection equipment intelligent management system based on edge calculation and reinforcement learning, and relates to the technical field of equipment intelligent management, the system comprises a multi-dimensional data acquisition module, a model training and feature library construction module, a deviation degree research and judgment module, a model optimization module and an interaction and execution module; according to the invention, through integration of the multi-dimensional data acquisition module and the model training and feature library construction module, all-directional precision monitoring of the operation state of laboratory detection equipment is realized, and various feature data in the operation process of the equipment can be captured in real time through the multi-dimensional sensor array; the model training and feature library construction module uses a deep learning model of a CNN-LSTM mixed structure, combines historical operation data and fault cases of equipment, calculates each feature weight, and dynamically constructs and updates a health state feature vector library of the equipment, thereby improving the accuracy and timeliness of equipment state evaluation, and improving the reliability of equipment state evaluation. Therefore, managers can find potential faults in advance and take preventive maintenance measures.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention provides a spectral imaging type defect detection method and system for a coating of an isolating switch, and relates to the technical field of electrical equipment maintenance, and the method comprises the following steps: S1, collecting a multiband reflection image of the coating on the surface of the isolating switch through a multispectral imaging device, the multiband at least comprising a visible light band, a near-infrared band and a short-wave infrared band; and S2, carrying out registration and fusion processing on the multi-band reflection image. According to the method and system, visible light, near-infrared and short-wave infrared bands are fused through multispectral imaging, high-precision defect recognition and thickness inversion are achieved, physical damage of traditional contact type detection to a plating layer is avoided, reflection interference is restrained through polarization filtering, the illumination fluctuation influence is eliminated through an ambient light dynamic compensation algorithm, and the detection accuracy is improved. And finally, in combination with structured light three-dimensional reconstruction and a multispectral inversion model, a coating thickness distribution diagram and a service life prediction curve are generated, data support is provided for preventive maintenance, and the service life of equipment is prolonged.

The invention relates to the technical field of numerical control machining, in particular to a numerical control tool wear online monitoring method and system based on multi-sensor fusion, and solves the problems that the monitoring precision is reduced and the false alarm rate is high due to working condition parameter drift. The technical bottlenecks that the discrimination adaptability of a fixed threshold value is insufficient and an offline training model cannot be continuously optimized are overcome. According to the method, the tool wear identification accuracy is effectively improved, the false alarm occurrence frequency is reduced, the residual life prediction reliability is improved, the intelligent monitoring level and the production stability in the numerical control machining process are improved, a reliable technical means is provided for precise management and preventive maintenance of the tool in the whole life cycle, and the method is suitable for popularization and application. Through a dual synchronization mechanism of unifying timestamps of a hardware clock and triggering angles of a main shaft encoder, accurate alignment of multi-sensor data with different sampling frequencies in a time axis and an angle domain is ensured.

The invention relates to the technical field of wind power generation, and discloses a fan blade deformation degree detection method and system, and the method comprises the steps: obtaining laser radar point cloud data, an adaptive image and a high-frequency vibration signal of a fan blade; generating a real-time three-dimensional model of the fan blade; calculating the displacement of the preset key feature points, and generating a global deformation thermodynamic diagram; constructing a dynamic attractor sequence based on the high-frequency vibration signal, and extracting multi-scale fingerprint features through the dynamic attractor sequence; and according to the global deformation thermodynamic diagram, the deformation degree of the fan blade is determined in combination with curvature change, and based on a comparison result of the multi-scale fingerprint features and a preset fingerprint threshold value, a micro-deformation area is positioned and damage early warning is realized. According to the method, the problems of poor data quality, low fusion degree, inaccurate deformation quantification, early warning lag and the like in the prior art are effectively solved, the detection precision, efficiency and reliability are remarkably improved, and comprehensive technical support is provided for preventive maintenance of wind power equipment.