204 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.

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 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 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 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 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 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 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 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 provides an electric power facility dynamic safety evaluation system based on blasting vibration propagation characteristics, which relates to the field of electric digital data processing and comprises a sensing and data acquisition module, a physical modeling and parameter library module, a real-time evaluation and risk inference module and a feedback execution and self-adaption module. The sensing and data acquisition module is used for converting on-site physical signals into high-quality data streams, the physical modeling and parameter library module is used for providing and building physical modeling and managing parameter information, and the real-time evaluation and risk inference module maps sensing data into system states and risk indexes through observation-model assimilation. The feedback execution and self-adaption module is responsible for driving decision execution, collecting feedback and continuously improving a system model; the system can monitor the influence of blasting vibration on electric power facilities in real time, accurately evaluate the structural damage degree and the system operation risk, and provide a scientific basis for safety protection and emergency decision-making of the electric power facilities.

The invention discloses a key part remaining service life prediction method based on a physical information neural Wiener process, and the method employs a Wiener physical information neural network model to predict the remaining service life of a key part, and integrates data driving and physical modeling. By combining the feature extraction capability of the feature extraction prediction sub-network and the physical degradation modeling capability of the Wiener degradation modeling solution sub-network, the complex nonlinear degradation features of the key parts can be captured more accurately, and high-precision prediction of the remaining service life of the parts is realized. Specifically, the feature extraction prediction sub-network extracts time sequence degradation features through a neural network, and the Wiener degradation modeling solving sub-network uses a Wiener process to model degradation dynamic states of key parts; a physical information joint loss function is designed, and a dynamic weight adjustment strategy is adopted, so that the relationship between data-driven learning and physical modeling is effectively balanced, a prediction result is ensured to accord with a real degradation law, and the physical interpretability of the model and the credibility of the prediction result are enhanced.

The invention discloses a standard modular energy storage thermal runaway early warning method and system based on digital twinning, and relates to the technical field of energy storage system safety monitoring. The method comprises the following steps: deploying multiple types of sensors in an energy storage unit to form a network, and collecting multi-dimensional data such as temperature, voltage and gas concentration; transmitting the data to the digital twin platform, and performing format conversion on the data for thermal runaway prediction; the platform is combined with the LSTM-GBDT hybrid model through physical modeling, and the risk probability is output; and comparing the risk threshold to trigger early warning. The system comprises a sensing module, a data acquisition and transmission module, a digital twinborn prediction module and an early warning execution module. According to the method, physical simulation and historical data training are fused through the digital twin platform, the heat transfer and out-of-control propagation process can be accurately simulated, virtual simulation optimization is supported, the early warning accuracy and reliability are improved, and the operation and maintenance burden and accident potential are reduced.

The invention discloses an offshore wind turbine multi-source heterogeneous data fusion and state evaluation method and device and a medium. The method comprises the following steps: constructing an offshore wind turbine multi-source heterogeneous database; data preprocessing is carried out; mapping the multi-source heterogeneous data subjected to data preprocessing to a unified embedding representation space by utilizing physical modeling and deep learning technologies; in the unified embedded representation space, constructing a feature interaction operator based on a multi-head cross attention mechanism, executing feature decoupling, and extracting a panoramic service state vector; and introducing the panoramic service state vector as a global observation item into a variational Bayesian calibration framework of an integrated physical information neural network, realizing dynamic calibration of digital twin physical parameters, and generating an offshore wind turbine service state evaluation report with uncertainty quantitative support. According to the method, the problems of data island, evaluation conservative, decision lag and the like are effectively solved, and a service state evaluation report with physical interpretability and uncertainty support is generated.

The invention provides a passenger compartment air conditioner control method based on model predictive control. The method comprises the steps that a passenger compartment model is constructed, and an original data set containing environment parameters, air conditioner control parameters and passenger compartment response parameters is obtained through experimental design; preprocessing the data; a neural network model is trained through the preprocessed data and serves as a prediction model to learn the nonlinear mapping relation among the air conditioner operation parameters, the environment parameters and the passenger compartment states; calling the prediction model to execute MPC rolling prediction, and generating a future state variable sequence; constructing a target function containing a state tracking error and control energy cost, obtaining an optimal control sequence through optimization solution, and acting the first control quantity on an actuator; and dynamically adjusting a prediction model or a control parameter according to real-time feedback. The technical problems that physical modeling is difficult due to high nonlinearity of a passenger compartment air conditioning system, and a traditional control strategy cannot give consideration to high-precision prediction and multi-working-condition adaptability can be solved.

The invention provides a wind turbine power prediction method based on multi-modal feature fusion, and relates to the technical field of wind turbine power prediction.The method comprises the steps that multiple types of sensors are used for obtaining multi-source sensor data, and a standardized time sequence data set is generated; establishing a first observation matrix based on the standardized time sequence data set, and constructing a weight sensing fractional order adaptive genetic algorithm to optimize the first observation matrix to obtain a second observation matrix; performing feature fusion and noise reduction on the second observation matrix by applying extended Kalman filtering and combining the working state and physical modeling of the wind turbine; constructing a bidirectional long-short-term memory network, and performing power time sequence modeling based on the multi-dimensional noise reduction feature vector to obtain an output power predicted value; and feeding back an error between an output power prediction value and a true value to a weight sensing fractional order adaptive genetic algorithm to carry out parameter iterative optimization to obtain a third observation matrix, and obtaining a power prediction value of the wind turbine based on the third observation matrix.

The embodiment of the invention provides a broadband oscillation source positioning method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of electric power. The method adopts a data driving strategy, does not need to depend on accurate physical modeling of a power system, and is suitable for a modern power system with a complex operation state and a changeable topological structure. The causal influence intensity between generator sets is analyzed by introducing transfer entropy, an adjacent matrix is constructed, and reasonable graph structure modeling under the condition of no topological prior is realized. Deep feature extraction is carried out on active power time sequence data of the generator set through the gating circulation unit, dynamic evolution characteristics in the broadband oscillation process are fully captured, and the representation capacity of nodes is enhanced. A causal graph structure and enhanced features are fused in a graph neural network model, space-time joint modeling is realized, the description capability of a disturbance energy propagation path is effectively improved, the spatial positioning precision of a forced oscillation disturbance source is greatly improved, and the method has good engineering applicability and popularization value.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

The invention discloses a self-supervised generative adversarial network defect detection method based on physical model constraint, and belongs to the technical field of industrial visual inspection. A physical model is embedded in a traditional GAN network structure, a physical constraint loss function is defined to control a generated sample, and meanwhile, a field adaptive migration mechanism is introduced; and mapping the generated image to a target production line domain to realize cross-production line model migration. A self-supervised consistency discriminator is further constructed, the robustness of the discriminator is enhanced through image disturbance invariance, and finally few-sample self-learning of high-precision defect detection can be completed without manual labeling. According to the defect detection method, a physical modeling mechanism and a generative adversarial network are combined, stress release constraint is introduced, a domain discrimination loss function, a cyclic consistency loss function and a self-supervision loss function are introduced to construct a high-performance discriminator, and the problems that defect samples are scarce, data are difficult to mark, and models are difficult to migrate in an industrial scene are solved.

The invention discloses an electric vehicle charging load prediction method based on cooperation of physical model driving and deep learning, and the method comprises the steps: constructing a physical model based on multi-source data including time, space and user behavior dimensions, and outputting physical features reflecting the internal rules of a charging load; physical features output by the physical model are used as one of key inputs to be input into a deep learning model (CNN-Transform-LSTM); a constraint term reflecting a charging load physical rule is explicitly added into a loss function of the deep learning model, a model prediction result is forced to conform to the physical rule, a model obtained after the physical model and the deep learning model are fused is (Phy-CNN-Transform-LSTM, PCTL), and a crown porcupine algorithm is applied to optimize hyper-parameters of a PCTL fusion model. And performing prediction by using the optimized PCTL fusion model. According to the method, through double fusion of physical modeling and deep learning on feature input and a loss function, and with the assistance of a CPO algorithm, the physical rationality and overall reliability of charging load prediction in a complex multi-dimensional scene are remarkably improved.

The invention discloses an automatic control method and system for fumaric acid production. The method comprises the following steps: collecting process variables in a fermentation process; constructing a soft measurement model fusing physical modeling and a residual neural network to obtain an acid production rate prediction value and prediction confidence; calculating a feed-forward control quantity, and adjusting the weight according to the prediction confidence to obtain a feed-forward control output; constructing a state vector, and establishing a Markov decision process; training a neural network control strategy by adopting a near-end strategy optimization algorithm, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient; calculating feedback control quantity, fusing the feedback control quantity with feed-forward control output, and applying change rate and amplitude limitation to obtain final control output; and switching to a feedback priority control mode under a specific condition, and regularly updating a soft measurement model and a control strategy parameter. According to the invention, high-precision stable control of pH in the fumaric acid fermentation process is realized, the production efficiency and the process robustness are improved, and the method is suitable for automatic control of the large-scale fermentation process.

The invention provides a multi-level observation station collaborative photovoltaic power prediction method based on the satellite internet and a related device, and relates to the technical field of photovoltaic power prediction. Based on a multi-stage observation station collaborative network, photovoltaic power and meteorological data of a target photovoltaic station, a large photovoltaic station and a micro observation station are collected in real time, and a high-precision photovoltaic power prediction model is constructed in combination with a high-resolution remote sensing cloud picture and multi-source meteorological data. Through a deep learning method and physical modeling, the model realizes long-time-scale trend prediction and short-time-scale fluctuation prediction respectively, meanwhile, a self-adaptive weight adjustment mechanism is utilized, the data weight of an observation station is dynamically optimized, the prediction precision is improved, and particularly, the response capability to sudden meteorological changes is improved under complex weather conditions. Under the layout of a large-scale photovoltaic field station, a spatial dependency relationship and local meteorological changes are effectively captured, high-precision photovoltaic power prediction is provided for power grid dispatching, and safe and stable operation of a power grid is ensured.

The invention discloses a thermal power plant coal pulverizing system load adaptive time sequence control method, and particularly relates to the technical field of thermal power plant intelligent control, and the method comprises the steps: S1, collecting multi-mode operation data of a coal pulverizing system, S2, carrying out the multi-step prediction of numerical time sequence data, generating a key parameter prediction sequence, and carrying out the calculation of the key parameter prediction sequence. The method comprises the steps of S1, predicting data, real-time data and text working condition data, S3, fusing the predicted data, the real-time data and the text working condition data to generate a structured natural language description, S4, inputting a state description into a large language model special for the coal pulverizing system subjected to field fine adjustment, and outputting a time sequence control instruction containing a decision reason, S5, carrying out safety verification on the control instruction, S6, executing control or feedback correction according to a verification result, and S5, outputting a control result. And closed-loop intelligent control is formed. According to the method, through deep fusion of time sequence prediction, natural language processing and physical modeling technologies, cognitive-level adaptive control of the coal pulverizing system from perception to decision is realized, and the control quality and economic safety of the system under complex working conditions are effectively improved.

The invention relates to the technical field of image semantic segmentation, and discloses a sea surface scene infrared image intelligent generation method, which can output different infrared images by inputting the same text, makes up for the defects of insufficient diversity of existing data set samples and incomplete coverage of extreme scenes, and meets the requirements of a deep learning model for data scale and distribution completeness. Meanwhile, on the basis of a Stable Diffusion model overall framework of LoRA parameter fine tuning, a variational auto-encoder, a language-vision pre-training model and a U-Net network model are combined, so that the problems that a traditional physical modeling method is complex in calculation and difficult to generate on a large scale are solved, and the real-time performance of the system is improved. The problems that a traditional data driving model is prone to deviating from a real physical process, a GAN model mode collapses and artifacts exist in a local high-heat-flux area are solved, the physical credibility and detail accuracy of generated images are guaranteed, and finally the generalization ability of a marine target recognition system is effectively improved through data synthesis.

The invention discloses an intelligent diagnosis method for wear of a high-speed bearing of an emulsifying machine, and the method comprises the steps: collecting multi-working-condition vibration and environmental noise signals through an experimental design system, and building a structured and standardized multi-channel data set; injecting various typical abnormal multi-scale signals by utilizing a physical modeling and simulation algorithm; adopting a working condition gating multi-branch time-frequency feature extraction network to obtain adaptive features; according to the method, high-confidence anomaly discrimination is realized in combination with a multi-discrimination sub-model and ensemble learning, and anomaly traceability and model self-learning are realized through a back projection and dynamic update algorithm. The method is suitable for intelligent monitoring and health management of emulsifying machine bearings under complex variable working conditions.

This invention provides a method and system for high-fidelity physical modeling and detection performance evaluation of space targets, relating to the field of deep space exploration technology. The method first acquires the relative motion parameters of the spacecraft and the asteroid, as well as camera parameters, and determines the static apparent magnitude based on observation geometry and the asteroid's physical characteristics. Then, it calculates the angular velocity modulus through relative velocity, combines it with camera parameters to obtain tail parameters, and quantifies the equivalent magnitude loss and effective apparent magnitude based on a piecewise model. Subsequently, it constructs a two-dimensional parameter grid of focal length and exposure time, calculates the effective signal-to-noise ratio and detection margin for each combination, and selects the optimal parameters using a heatmap. Finally, it generates a high-fidelity simulation image and feeds back the observation values ​​to the guidance, navigation, and control system via a UDP asynchronous interface. This invention achieves quantitative assessment of tail loss and automated parameter optimization, balancing high fidelity and real-time performance, and improving detection accuracy and system reliability.

The invention relates to the technical field of building structure engineering, in particular to a partition wall structure integrated refined modeling and simulation method based on physical reality. A complete and closed technical chain from refined physical modeling to connection and failure linkage algorithm definition and then to whole-process nonlinear simulation to finally realize automatic multi-target parameter optimization is constructed, and'work-damage-failure-influence 'full-life-cycle behaviors of a partition wall subsystem are subjected to full-process nonlinear simulation, so that the whole-life-cycle behavior of the partition wall subsystem is optimized. A rigorous mathematical model and a numerical algorithm are integrated into the performance design of the whole structure, a special design system which is highly innovative, computable and verifiable is formed, and therefore quantitative and performance design of the damping connection structure is completed. By introducing a series of mathematical models, criteria and linkage algorithms with clear physical significance, the black box experience of traditional'cycle reduction coefficient 'is improved.

The invention provides an electrostatic force tactile rendering method and system based on physiology and physics modeling, and belongs to the technical field of virtual reality and man-machine interaction, and the method comprises the steps: responding to tactile event triggering, matching tactile scene nerve pulse features corresponding to a tactile event from a nerve pulse database, the nerve pulse database comprises a plurality of tactile scene nerve pulse features; generating a neural pulse signal based on the neural pulse feature, and converting the neural pulse signal into a driving parameter corresponding to the electrostatic force tactile representation device according to a preset rule; and controlling the electrostatic force tactile representation device to generate a target tactile sense corresponding to the tactile event according to the driving parameter. According to the electrostatic force tactile rendering method and system based on physiology and physics modeling, the authenticity of tactile representation is improved.

The application discloses a kind of information physical fusion method and system of mechanical equipment health prediction, wherein the method comprises: collecting the working state data of mechanical equipment;Working state data is input to pre-trained information physical fusion system CPS hybrid model, and the health prediction result of mechanical equipment is obtained, wherein CPS hybrid model is obtained according to the fusion of signal processing physical modeling and deep learning data modeling;The health prediction result is mined, and the real-time state of mechanical equipment is evaluated according to the mining result, and the state evaluation result of machine equipment is obtained.The application aims to use the optimized physical modeling method to extract more rich prior knowledge, and then realize the improvement of hybrid modeling accuracy through the optimized data modeling method, which is beneficial to support predictive maintenance activities, so as to realize the monitoring and control of CPS from physical components to network components to physical components of mechanical equipment in network collaborative manufacturing and intelligent factory.

The invention discloses a multi-level voxel simulation system and a cross-level communication method thereof, and relates to the technical field of computer simulation and physical modeling. The system of the present invention includes at least two levels of voxel grids (e.g., coarse grids and fine grids), each voxel in the coarse grid representing a voxel region in the fine grid. During simulation, the coarse hierarchy model quickly simulates global behaviors or performs coarse collision detection, and the fine hierarchy model performs fine calculation on local key areas. When a potential event is detected by the coarse hierarchy, deep simulation of the corresponding fine hierarchy region is triggered through a communication mechanism; and after a fine result is obtained through fine hierarchy calculation, key information is fed back to the coarse hierarchy for global state updating. The hierarchies are kept consistent in modes of sharing occupation information, boundary conditions and the like, so that cross-scale co-simulation is realized. According to the method, waste of calculation in an open area is avoided, and the simulation precision of a detail complex area is ensured.