345 results about "Physical modelling" patented technology

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

Systems and methods of converting visual content for display on a virtual display system include extracting depth information from input visual content and formatting the input visual content and the depth information. The virtual display system may produce virtual images that are multifocal virtual images. The conversion of the input visual content into the multifocal virtual images may be impacted by properties of the human vision system, physical modeling of the input visual content, user input or sensory data, or generative content.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention relates to the technical field of data processing, and discloses a multi-scene-oriented unmanned aerial vehicle operation resource dynamic configuration and scheduling method and a multi-scene-oriented unmanned aerial vehicle operation resource dynamic configuration and scheduling system. The method comprises the steps of generating multi-scene task constraint data through semantic analysis; constructing a dynamic situation field matrix based on a Gaussian function; establishing a continuous motion velocity field containing pressure gradient and viscosity diffusion; the vortex velocity components are fused to form a self-adaptive control velocity field; and constructing a task allocation vector field to generate an operation area configuration scheme and converting the operation area configuration scheme into a flight control instruction. According to the method, the core problems that multi-scene adaptability is insufficient and real-time performance and optimality are difficult to balance in the prior art are solved, and the intelligent scheduling capability and the collaborative operation efficiency of the unmanned aerial vehicle group in a complex and changeable environment are improved through an innovative semantic understanding and physical modeling combined method.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention discloses an unsteady flow field dimension reduction and prediction method fusing physical modeling and deep learning, and belongs to the technical field of computer-aided fluid mechanics analysis. According to the method, firstly, a modal coefficient reflecting global dynamics is extracted from an unsteady flow field by using a DMD, and meanwhile, low-dimensional feature representation of a potential space is learned from a flow field snapshot through CVAE; the two types of features have complementarity in physical and statistical meanings, and the complex dynamic evolution law of the unsteady flow field is more effectively represented through the low-dimensional features constructed in a combined mode. On the basis, an LSTM model is used for carrying out time sequence modeling on the joint features, and high-precision prediction of future evolution of the flow field is achieved. The hybrid modeling method provided by the invention improves the dimensionality reduction efficiency and prediction precision of a high-dimensional nonlinear unsteady flow field while keeping physical consistency, and is suitable for intelligent simulation and rapid prediction tasks in a complex flow scene.

The invention relates to an error compensation method of a birefringence self-calibration laser level meter, in particular to the field of laser level meters, and aims to improve the precision of the laser level meter by combining a birefringence effect, physical modeling and neural network optimization. Firstly, temperature data are collected in real time, Kalman filtering is used for noise reduction, and accurate data are provided for physical modeling; then, through physical constraints such as a heat conduction equation and a Jones matrix, a coupling relation between the temperature gradient and optical parameter changes is established; then, dynamically optimizing the refractive index correction by using a physical information neural network to avoid an overfitting problem; and finally, calculating optical path difference compensation by using a hardware accelerator, correcting a system error in real time, and feeding back and adjusting a network weight. According to the method, the precision and the stability of the laser level meter in an environment with relatively large temperature change are effectively improved.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

The invention discloses an orthotropic rock physical modeling method. The method comprises the following steps: S1, obtaining tight reservoir parameters; s2, estimating the elastic modulus of the mixed minerals; s3, enabling the mixed mineral to be equivalent to a VTI background rock matrix caused by a thin layer; s4, matrix pores are added into a VTI background rock matrix; s5, embedding the vertical crack into the VTI background'dry 'rock skeleton to obtain an OA medium'dry' rock skeleton; s6, calculating the bulk modulus and saturated rock density of the mixed fluid; s7, adding the mixed fluid into the OA medium'dry 'rock skeleton to obtain OA medium saturated rock; s8, calculating longitudinal and transverse wave velocities and anisotropy parameters of OA medium saturated rocks; and S9, calculating OA medium saturated rock. According to the method, the tight reservoir with the orthotropic characteristic can be simulated, the Thomsen parameter and the fracture weakness parameter of the tight reservoir can be obtained, and data support is provided for fracture evaluation, anisotropic parameter inversion, permeability prediction and the like of the tight reservoir.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention discloses an intelligent closestool virtual-real synchronous quality prediction system based on a digital twinning system, and relates to the technical field of digital twinning, and the system comprises a data collection module which is used for collecting quality detection data of an intelligent closestool on a physical production line in real time, and the data at least comprises water pressure related data, temperature data and circuit performance data; the digital twinborn model module constructs an intelligent closestool digital twinborn model with a dynamic error compensation mechanism, compares collected actual quality detection data with simulation data in the model to calculate an error, calculates a fraction and a threshold value according to a preset weight, and automatically corrects the simulation error of a virtual model when a comprehensive error exceeds the threshold value; and the quality prediction module is used for realizing prediction of the quality of the intelligent closestool by adopting a fusion deep learning and physical modeling technology and combining data output by the data processing and analysis module. According to the method, the digital twin model is used for accurate comparison and error correction, and the quality problem in the production process can be found in time.

The invention relates to the technical field of data simulation, in particular to a bearing fault simulation method and system. The method comprises the following steps: collecting multi-modal bearing data so as to construct a bearing distributed edge data set; extracting contact power data of the bearing distributed edge data set, and calculating a bearing rigidity change curve according to the contact power data; carrying out random parameter modeling based on the bearing distributed edge data set, and carrying out physical modeling benchmark reference on the bearing rigidity change curve to obtain a bearing fault physical-data hybrid model; therefore, by integrating multi-modal data acquisition, physical-data hybrid modeling and a dynamic hyper-parameter adjustment mechanism, the defects of a traditional bearing fault diagnosis method in the aspects of accuracy and real-time performance are overcome, and the fault prediction and early warning precision and the response speed are improved.

The invention discloses an extended-range hybrid propulsion double-source dynamic coupling energy management method, which comprises the following steps: carrying out global physical modeling on a double-source power system and a flight scene, and establishing a double-source dynamic coupling model; designing a reinforcement learning physical constraint reward function, performing optimization training on each coefficient of the reward function by adopting a QMPSO algorithm, and outputting an optimized reward function coefficient; a qualified double-source dynamic coupling model is verified, and a power distribution coefficient is optimized; outputting the optimal power distribution coefficient of the battery and the range extender; the superiority of the dual-source power cooperative control strategy in the aspects of flight economy, operation stability and system life guarantee is verified through multi-dimensional comparative analysis of each performance index. Cooperative power distribution of the battery and the range extender is achieved through dynamic coupling modeling and reinforcement learning, the flight scene load requirement is met, the system energy efficiency is improved, and the service life of parts is prolonged.

The invention belongs to the field of energy storage battery health state estimation, and particularly relates to a method for estimating the health state of an energy storage battery by using a machine learning algorithm and a data model based on equivalent circuit feature screening. According to the method, equivalent circuit model features and data driving features are combined, redundant information is reduced through feature screening, SOH estimation precision and calculation efficiency are improved, the generalization ability of the model is enhanced, and the model can adapt to different working conditions and environment changes. Compared with a pure data driving method, the method introduces ECM features, so that the prediction result is more suitable for the physical degradation mechanism of the battery, and the interpretability of the model is improved. In addition, optimization of feature screening reduces calculation complexity, so that SOH estimation is more efficient, and real-time or online prediction requirements can be met. By fusing the advantages of data driving and physical modeling, the method has both reliability and accuracy, and a better solution is provided for lithium ion battery health management.

The invention relates to the field of battery thermal management, in particular to an AUV lithium ion battery thermal state prediction method. Comprising the following steps: constructing an electrothermal coupling reduced-order thermal model, and generating initial temperature estimation with physical consistency; a physical guidance space-time dynamic graph convolutional network PG-STDGCN is constructed as an error correction model, the model constructs a static and dynamic fused adjacency matrix by embedding physical priori such as a battery topological structure and circuit characteristics into dynamic graph learning, and a correction value of initial temperature estimation is output; and adding the initial temperature estimation and the correction value to obtain a final battery thermal state prediction result. According to the method, organic fusion from physical modeling to data-driven correction is realized, interpretability, precision and adaptability are considered under the dynamic working condition of the AUV, and the battery pack-level multi-cell temperature prediction performance is remarkably improved.

The invention relates to the technical field of medical education, in particular to a virtual patient skin care teaching system with enhanced pathological features and a method thereof, the system comprises pathological feature generation, parameter control, physical modeling, intelligent evaluation and a multi-mode feedback module, and can edit and simulate various skin pathological features in real time, such as pressure sores and diabetic feet, so that the system can be applied to the field of medical education. The visual display of the lesion degree is realized through a five-segment color mapping system, the system also simulates percolate reflection and cuticle spalling, the sense of reality is enhanced, the intelligent assessment module provides risk assessment and healing prediction according to physiological and morphological parameters, the multi-modal feedback module integrates visual sense, tactile sense and user behavior feedback, the interactive experience is improved, and the system is suitable for popularization and application. And the adaptive optimization module dynamically adjusts system parameters according to user feedback and learning effects, and provides personalized teaching. Compared with a traditional method, the method is higher in parameter control precision, supports personalized teaching, and is of great significance to medical education.

The invention discloses a magnetic source positioning method based on a spin-exchange relaxation-free atom magnetometer, and aims to provide a high-precision brand-new solution for magnetic source positioning in a near-zero magnetic environment by performing physical modeling and least square estimation on space magnetic field distribution of a target magnetic source. The magnetometer is enabled to be simultaneously sensitive to a three-axis magnetic field, a light absorption detection method is utilized to ensure that complete three-axis magnetic field information is obtained, and the sensitivity of the three axes reaches fT / Hz1 / 2 (1fT = 10 <-15 > T) magnitude. In the positioning process, the target magnetic source is abstracted into a physical model of the magnetic dipole, and the space magnetic field information generated by the target magnetic source is obtained through the atom magnetometer, so that the long-term stability of the magnetic source positioning process and the high precision of the positioning result can be ensured in the zero magnetic environment; therefore, a high-precision magnetic positioning technical means with great potential is provided for various application scenes such as paleomagnetic sample testing and magnetic source positioning in medical research, and technical progress and application expansion in related fields are expected to be promoted.

The invention discloses a building concrete waste treatment system, which belongs to the technical field of building waste treatment, and is characterized in that a data acquisition module acquires waste physical attributes and inputs the waste physical attributes into a model generation and optimization module; the model generation and optimization module establishes a waste physical characteristic model, dynamically updates the waste physical characteristic model and outputs a dynamic waste physical characteristic model to the strategy generation module; the strategy generation module outputs equipment operation parameters; the strategy mapping module maps the equipment operation parameters into physical execution signals through an equipment instruction mapping model to obtain execution results, and the execution results are input into the strategy feedback optimization module; and the strategy feedback optimization module identifies the execution deviation in the execution result and feeds the execution deviation back to the strategy generation module to optimize the equipment operation parameters. According to the scheme, an intelligent algorithm and physical modeling are combined, the waste treatment process is optimized through real-time data collection and feedback, equipment parameters are adjusted based on different characteristics of the waste, and the problems of excessive treatment and resource waste are avoided.

The invention relates to a digital prototype construction system for three-dimensional weaving preform equipment, the system comprises five different model construction methods and four associated construction methods among models, and the five different models are constructed by physical modeling, data modeling, motion modeling, process modeling and simulation. The four associations among the models are the association of a physical model and data, the association of a motion model, a physical three-dimensional model and a data model, the association of a process model, the motion model and the data model, and the association of a simulation result and the data model. According to the method, a virtual production process twin model is constructed, bidirectional real-time interaction with a physical production process is performed, and dynamic updating of a simulation model is driven by using multi-source data, so that the construction process of a digital prototype is standardized, real-time monitoring of the production process is realized, the utilization rate of data is improved, and closed-loop feedback and process optimization are realized.

The invention provides a ship pipeline leakage detection network training method, an application method and a pipeline detector, and belongs to the field of industrial equipment detection. The training method comprises the following steps: performing physical modeling analysis on acquired ship pipeline sensing data to obtain physical derived data, and fusing the physical derived data and the ship pipeline sensing data to obtain fused training data; performing multilayer graph convolution on the fusion training data to obtain spatial information features, performing spatio-temporal information attention enhancement to obtain spatio-temporal information features, fusing the spatial information features and the spatio-temporal information features to obtain spatio-temporal fusion features, predicting the spatio-temporal fusion features to obtain pipeline leakage prediction output, and determining prediction loss according to the pipeline leakage prediction output. And iteratively training to obtain a ship pipeline leakage detection network. According to the ship pipeline leakage detection method, the data dimension and the data volume are enriched through the physical derived data, the space-time relevance in the mined data is enhanced through multilayer graph convolution and space-time information attention, and the accuracy of ship pipeline leakage detection is effectively improved.

The invention provides a CFD optimization-based air volume matching and linkage control method and system for a smoke prevention and exhaust system of an underground powerhouse, and belongs to the technical field of underground smoke exhaust systems. The method comprises the following steps: performing physical modeling according to an actual underground powerhouse, and performing grid division, solver selection, boundary condition setting and airflow field change simulation to obtain an initial airflow field; cFD is adopted to simulate temperature changes, smoke concentration changes and airflow changes in different fire scenes, wind flow and smoke flow in the underground powerhouse are analyzed, the defects of an existing system are evaluated, and smoke exhaust pipeline arrangement and fan configuration are optimized according to the defects; the airflow change in the fire environment is dynamically calculated by combining the real-time monitoring system and the CFD, and the air volume of the pressurized air supply system, the smoke exhaust system and the air supplement system is adjusted. Efficient linkage of the system is achieved, while personnel evacuation safety is guaranteed, the smoke exhaust efficiency and the emergency response capacity are improved, energy consumption is reduced, and the dual goals of energy saving and efficiency improvement are achieved.

The invention discloses a semiconductor device etching structure prediction method and equipment, and a storage medium. The method comprises the following steps: acquiring three-dimensional etching evolution simulation data of a semiconductor structure to be etched under different etching process conditions; extracting a two-dimensional section image from the three-dimensional etching evolution simulation data; generating an etching morphology evolution sequence under high time resolution based on the two-dimensional cross section image; and obtaining an etched target contour image of the semiconductor structure according to the etching morphology evolution sequence under the high time resolution. Constructing a contour prediction model based on deep learning, and training the contour prediction model by using the data set; the input of the contour prediction model based on deep learning comprises etching process conditions and an initial contour image of the semiconductor structure before etching, and the output of the contour prediction model based on deep learning is a predicted contour image after etching evolving along with etching time. According to the method, physical modeling and a data driving method are combined, high-precision prediction and intelligent optimization control of the etching process are achieved, and the process stability and the product yield are improved.

The invention discloses an unmanned aerial vehicle aerial image imaging optimization method and device fusing a deep learning perception mechanism and physical modeling. The method comprises the following steps: acquiring an original image frame obtained in a flight process of an unmanned aerial vehicle; inputting the image into a MobileViT illumination estimation network, extracting local convolution perception and multi-scale global semantic features, and outputting a scene illumination intensity estimation value; constructing a differentiable imaging parameter reasoning module based on an illumination physical modeling relationship, reversely deducing an optimal exposure parameter combination of a current frame, and constructing a parameter optimization module based on a perceptual error; combining the difference between the reconstructed image and the target image in the semantic perception space to construct a multi-loss function joint training model, and optimizing an exposure combination; deploying an edge computing platform for the trained network model to complete parameter prediction, control feedback and image acquisition link closed loop; according to the method, exposure optimization is realized before imaging, image gamma decoding and target enhancement are realized after imaging, and the image quality in low-light and backlight scenes is improved.

The invention belongs to the technical field of textile, and discloses a reinforcement learning and physical modeling combined broken end dynamic capturing method and system. The method comprises the following steps: constructing a yarn microstructure evolution model; inputting real-time environment parameters of the textile workshop into the microstructure evolution model, and predicting to obtain real-time microstructure parameters of the yarn; performing nonlinear coupling feature extraction on the real-time environment parameters, and calculating an environment coupling feature vector; inputting the real-time microstructure parameters as material attributes into a yarn tension dynamical equation, and solving to obtain a predicted yarn macroscopic stress state; splicing the macroscopic stress state of the yarn and the environment coupling feature vector to form a state observation value; the reinforcement learning agent outputs a broken end risk probability value according to the state observation value; and when the broken end risk probability value exceeds a preset threshold value, generating and outputting a broken end early warning signal. According to the invention, early and accurate early warning of yarn breakage in a complex dynamic environment can be realized.

The invention provides a large-scale complex structural member machining deformation simulation dynamic modeling method, and relates to the technical field of machining and manufacturing, and the method comprises the steps: building an intelligent model, and carrying out the dynamic optimization: building a three-dimensional model, marking an easy-to-deform region, embedding tolerance information, and carrying out the self-adaptive grid division; carrying out multi-source data driven physical modeling, collecting data and correcting material attributes, and constructing an intelligent constitutive model; multi-scale coupling dynamic simulation is carried out, and macro-micro model coupling and machining force dynamic prediction are achieved; and digital twinborn verification and closed-loop control are carried out, deformation deviation is quantified, and machining parameters are optimized. According to the method, the machining mechanical behavior of the large complex structural part can be more accurately simulated by accurately marking the easily-deformed area, embedding tolerance information, performing adaptive grid division, driving and correcting material attributes through multi-source data and constructing the intelligent constitutive model, and the simulation precision is improved.

The invention relates to the field of unmanned aerial vehicle motor service life prediction, and discloses an unmanned aerial vehicle motor service life test method, which comprises the following steps: collecting motor data in real time through temperature, vibration, current and voltage sensors, carrying out cleaning, denoising and standardization processing, analyzing motor characteristics by adopting physical modeling, extracting parameters such as damage rate, temperature and load, and calculating the service life of the unmanned aerial vehicle motor. According to the method, historical data and physical modeling features are combined, an LSTM deep learning model is constructed for time sequence training, the remaining service life RUL is predicted, feature fusion is carried out, the prediction precision is improved, the health state of the motor is evaluated according to a prediction result, maintenance suggestions are provided, the model is optimized and fed back, and the prediction method is dynamically adjusted. According to the technical scheme of fusing physical modeling and the deep learning LSTM network, high-precision prediction of the service life of the motor is realized, the intelligent degree of data processing is improved, and the adaptability of the model to a complex operation environment is enhanced.

The invention is suitable for the technical field of agricultural information, and provides a fertilizer material volume change measuring method based on multi-sensor information fusion, which comprises the following steps: carrying out physical modeling on a detection area; selecting a depth camera and determining an angle; acquiring and processing point cloud data; and splicing and quantifying the point cloud target areas in the previous and next time. According to the method, a storage space is mapped through physical modeling, multiple sensors are used for data acquisition and reconstruction, and point cloud data of a fertilizer area are accurately extracted through a point cloud segmentation algorithm. The system calculates the change quantity before and after fixed-period data acquisition, and quantifies the accurate change volume through a voxel technology. According to the method, real-time volume change measurement can be provided, the measurement precision can be adjusted according to needs, and the method is suitable for storage spaces of different materials such as fertilizers and grains.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention discloses a digital twin-driven data-enhanced aviation undercarriage laser shock peening monitoring method, and belongs to the field of aviation structural member remanufacturing, intelligent manufacturing and data driving regulation, and the method comprises the steps: building a fatigue evaluation system for an alternating load in an undercarriage service process, recognizing a high stress concentration region, and carrying out the recognition of a high stress concentration region; a multi-modal monitoring system is constructed, impact data is expanded by adopting a data enhancement method, an impact strengthening physical model is constructed in combination with finite element analysis, impact wave propagation and residual stress evolution are simulated, and collaborative optimization of data driving and mechanism modeling is realized in combination with an experimental data correction model. Impact quality is evaluated based on deep learning, multi-modal data is fused to analyze impact uniformity, and an enhanced abnormal region is identified. And the impact process parameters are optimized through reinforcement learning, and intelligent feedback regulation and control are achieved. According to the method, multi-modal monitoring, data enhancement, physical modeling and intelligent optimization are combined, the stability of laser shock peening is improved, and the fatigue life of laser shock peening is prolonged.