95 results about "Physical neural network" patented technology

The invention provides an alloy processing workshop scheduling method based on real-time monitoring of a physical neural network, and belongs to the technical field of intelligent scheduling of alloy processing workshops. A deep operator network combining physical information and a deep learning model is provided and used for predicting the service life of the tool, and the network not only can learn the dynamic change of tool wear from data, but also can enhance the interpretability and prediction capability of the model through a physical signal; according to the intelligent scheduling method based on the cutter residual life, cutter life prediction and workshop production scheduling are deeply combined, the cutter state is monitored in real time, distribution of machining tasks and a cutter replacement plan are dynamically adjusted, and the cutter is prevented from being excessively used or replaced too early; the invention also provides a hybrid optimization strategy combining an adaptive genetic algorithm and reinforcement learning, global optimization is carried out through the genetic algorithm, local optimization is carried out in combination with reinforcement learning, the change of a workshop environment is dynamically responded, and the real-time performance and high efficiency of a scheduling scheme are ensured.

The invention relates to the technical field of battery health state management, and discloses a lithium battery internal state estimation method based on a physical information neural network, and the method comprises the steps: obtaining a loss function for carrying out the optimization training of the internal parameters of a neural network model through an e-SPM model, enabling the internal parameters of the neural network model to be subjected to the optimization training through the loss function, and achieving the estimation of the internal state of a lithium battery. And internal state estimation of the lithium battery is realized. According to the invention, the current, voltage and temperature of the lithium battery are directly mapped to the internal electrochemical state of the lithium battery, so that the output result of the physical neural network is more explanatory and is easy to understand and analyze, and the output state quantity is substituted into the simplified physical model of the voltage by adopting an unsupervised learning mode. In the training process, a large amount of labeled data is not needed, and the experimental data acquisition difficulty is reduced.

The invention provides a diesel engine misfire fault diagnosis method based on a GRU and a PINN. The diesel engine misfire fault diagnosis method comprises the following steps of diesel engine multi-source data acquisition, data preprocessing and model sample generation; designing a gating circulation unit, introducing a physical constraint embedding module, constructing a misfire fault diagnosis model, and fusing the misfire fault diagnosis model with a long-short term memory model and a physical neural network; performing joint training and optimization on the misfire fault diagnosis model in the step S2 by using the samples in the step S1; and deploying a misfire fault diagnosis model and performing real-time diagnosis. The method has the beneficial effects that the time sequence data-physical model dual-drive diagnosis of the misfire fault of the diesel engine is realized; the accuracy of misfire diagnosis is improved; and the false alarm rate of complex working conditions is effectively reduced.

The invention relates to an intelligent thermal simulation method for a monolithic three-dimensional integrated chip, belongs to the technical field of monolithic three-dimensional integrated chips, and solves the problems of long thermal simulation period and large operation memory space in the prior art. The thermal simulation method comprises the following steps: layering a monolithic three-dimensional integrated chip, and constructing a coordinate system of the monolithic three-dimensional integrated chip and physical equivalent information of each layer; measuring and acquiring a plurality of training samples; measuring the coordinate points of the heat source and the power of each heat source coordinate point at different time points to form heat source power data; according to the plurality of training samples, in combination with the physical equivalent information and the heat source power data of each layer, training a physical neural network (PINN) to obtain a thermal simulation model of the monolithic three-dimensional integrated chip; and inputting all the coordinate points of the monolithic three-dimensional integrated chip and all the prediction time points of each coordinate point into the thermal simulation model to obtain the prediction temperature of each coordinate point at each prediction time point. And rapid thermal simulation of the monolithic three-dimensional integrated chip is realized.

The invention belongs to the technical field of building energy conservation, and provides a real-time inversion method and system for a heat transfer coefficient of a building envelope, and the method comprises the steps: obtaining surface parameters and environment parameters of the building envelope; based on the obtained parameters and the physical neural network model, taking the minimum joint loss function based on the physical mechanism as a target, and performing inversion to obtain the optimal heat conductivity coefficient of the building envelope; and calculating the heat transfer coefficient of the building envelope according to the obtained optimal heat conductivity coefficient of the building envelope, and completing real-time inversion of the heat transfer coefficient of the building envelope.

The invention relates to a basin flood simulation optimization method based on a physical information neural network, and aims to construct a basin flood acceleration solution mode based on PINNs, and ensure the continuity and the physical characteristics of mass conservation during model operation, and the method comprises the following steps: simulating basin flood by using an urban flood space-time simulation model; obtaining a data set required for constructing the physical neural network; physical rules are integrated and simplified into limiting conditions, so that integration of a neural network structure is facilitated; establishing a partial differential equation (PDE) used for describing river flood routing, and taking the partial differential equation as a loss function of the physical information neural network model; constructing and training a PINNs drainage basin flood model constructed on the basis of a CNN (Convolutional Neural Network); and establishing a model evaluation index and performing evaluation. The method has the beneficial effect that the speed and precision of basin flood simulation are improved.

The invention relates to a mechanical arm optimization control method and device based on a coupled physical neural network, and belongs to the field of mechanical arm optimization control. The method comprises the following steps that S1, a mechanical arm optimization problem is extracted; s2, constructing accurate solution data; s3, establishing a constraint condition network and training; s4, establishing a target function network; s5, taking the objective function as a loss function, and establishing a dual-network coupling architecture; s6, training a dual-network coupling architecture; s7, outputting a predicted optimal solution result; s8, performing deviation correction on the predicted optimal solution by using a deviation correction method to obtain an optimal solution; and S9, mechanical arm parameters are adjusted according to the optimal solution to achieve control. According to the method, a multi-task objective function can be quickly coped with, so that a large number of repeated solving data sets are avoided, and the target required by a product is responded more timely and accurately; meanwhile, the innovative training method not only ensures the global search capability, but also effectively balances the calculation cost and the solving precision, and provides efficient technical support for solving a complex optimization problem.

The invention discloses a performance prediction model determination method, a performance prediction method and a performance prediction device of a fuel cell, mainly aiming at modeling simulation and performance prediction of the fuel cell, the method comprises the following steps: constructing a fitting coefficient training database of the fuel cell based on a semi-empirical physical model and a computational fluid mechanics model of the fuel cell, and training a neural network model based on the data in the fitting coefficient training database to obtain a target neural network model of the fuel cell, coupling the neural network model and a semi-empirical physical model, and obtaining a performance prediction model of the hybrid physical neural network architecture. And the performance prediction model is used for outputting a fitting coefficient of the semi-empirical physical model based on the input operation parameters, taking the fitting coefficient as the input of the semi-empirical physical model, and finally outputting performance parameters. Therefore, by effectively combining a machine learning method with a physical model, a hybrid physical-data driven performance prediction model with an efficient calculation characteristic and a high degree of detail can be formed.

The invention relates to the technical field of lithium ion battery health monitoring, and discloses a lithium ion battery health state estimation method based on a physical neural network, which comprises the following steps: reconstructing an SEI membrane growth process of a lithium battery, fitting the growth process through the physical neural network, and analyzing voltage time sequence data in a lithium battery charging process to obtain a lithium ion battery health state estimation result. Calculating a health index in a 71%-90% state of charge range of a lithium battery charging interval; according to the estimation method, the health indexes are extracted by analyzing short-period data of the charging state of the battery within the range of 71%-90%, the method is successfully applied to four data sets of different chemical systems including LFP, NCM, NCA and the like and multiple charging and discharging protocols, and the good applicability of the method to different battery types and working conditions is proved.

The invention relates to the technical field of environment monitoring, in particular to a building decoration environment harmful gas dynamic monitoring method based on deep learning, which comprises the following steps: acquiring temperature, humidity, volatile organic compound concentration, carbon dioxide concentration, energy consumption and equipment attitude in a building model coordinate system, and eliminating redundant channels according to spatial topology to form a simplified data stream; dense features are extracted from the simplified data flow through a space-time auto-encoder, query key values are generated through quantification, and adjacent historical features are retrieved and spliced into a fusion feature vector; inputting the fused feature vector and a sensing node coordinate into a physical neural network of an embedded gas diffusion and heat conduction equation, and predicting a temperature gradient and regional carbon equivalent emission; calculating a hazard index field according to prediction, heat flow simulation, construction progress and a material release rate, iteratively optimizing a sensing node set and writing the sensing node set into an environment mirror image body; and reconstructing a coupling field in the environment mirror image body, comparing a measured value to obtain a residual field, and updating a hazard index field and a sensing node set when the environment mirror image body is abnormal.

The invention proposes a steel slag asphalt interface adhesion work prediction method based on a physical neural network, and relates to the technical field of deep learning, and the method comprises the steps: obtaining chemical component parameters and environmental impact factors of a steel slag asphalt mixture interface sample; creating an original neural network prediction model based on a diffusion item, a reaction source item, a multi-mechanism dissipation item and a state variable corresponding to spatial-temporal evolution of the adhesion work; inputting the chemical component parameters and the environmental impact factors into an original neural network prediction model, and generating interface adhesion work prediction parameters in combination with physical constraint terms; training the original neural network prediction model by using the interface adhesion work prediction parameters and a gradient descent algorithm to obtain a target neural network prediction model; the physical constraint items comprise data loss, PDE residual error loss, state equation loss and boundary condition loss; and inputting the chemical component parameters and the environmental impact factors of a to-be-tested material into the target neural network prediction model to obtain target prediction parameters.

The invention belongs to the technical field of ocean wind turbine platform coupling stress intelligent forecasting, and discloses an ocean wind turbine platform coupling stress forecasting method and system, and the method comprises the steps: obtaining an ocean wind turbine platform stress data set needed by training through employing a computational fluid dynamics method; constructing a physical information neural network architecture of a full-connection structure; a loss function of the physical neural network is constrained by physical equations related to the wind load borne by the fan blade surface, the wind load borne by the semi-submersible platform and the wave load borne by the semi-submersible platform, and an ocean fan platform coupling stress forecasting model based on the physical information neural network is formed. The method comprises the following steps: outputting a wind load borne by a fan blade surface based on an ocean fan platform coupling stress forecasting model of a physical information neural network; and the semi-submersible platform bears wind load and wave load. The conformity of the output result and the measured data set is good, and the error does not exceed 2%.

The invention relates to the technical field of side-blown furnace smelting, and discloses a nonlinear intelligent control system and method for side-blown bath smelting, and the method comprises the steps: building a computational fluid mechanics and physical neural network coupling model according to boundary condition data, and carrying out the calculation of the boundary condition data, and obtaining the dynamic data of a multiphase flow physical field; predicting the molten pool liquid level fluctuation state; analyzing the prediction result to obtain the frequency and the amplitude of the intense liquid level fluctuation region; determining liquid level fluctuation dominant frequency and amplitude according to the frequency and amplitude of the intense liquid level fluctuation area; the main frequency and the amplitude of the liquid level fluctuation are fed back to a central control system to be integrated, and working parameters of jet flow are obtained; and calculating the frequency and the peak value of liquid level fluctuation based on the jet flow working parameters, and if the frequency and the peak value exceed preset values, adjusting the frequency and the amplitude of the side-blown nonlinear synthetic jet flow. The system comprises a boundary data acquisition module, a liquid level prediction module and a liquid level data integration module. Peak shifting regulation and control of the side blowing gas speed and the molten pool liquid level fluctuation condition are achieved.

The invention discloses a real-time sea clutter simulation method based on a physical neural network, belongs to the field of sea clutter simulation, and particularly relates to a real-time sea clutter simulation method. The invention aims to solve the problems of poor real-time performance and long time consumption of the existing sea clutter simulation method. The method comprises the following steps: S1, initializing parameters and simulation; s2, numerical simulation of sea surface dynamics is realized according to a shallow water equation, and the obtained sea surface height field changing along with time, the flow velocity of the sea surface height field in the axial direction and the flow velocity of the sea surface height field in the axial direction are periodically stored; s3, constructing a physical neural network; s4, obtaining a trained physical neural network; s5, inputting the space coordinate and the time coordinate of the real-time sea clutter to be measured into the trained physical neural network, and outputting the sum; generating a real-time file in an STL format; and S6, obtaining sea clutter data by using the open source library.

The invention discloses a fiber composite material damage diagnosis method based on a physical neural network, and the method comprises the steps: firstly constructing a multi-dimensional physical parameter fused physical neural network model, comprehensively considering material, mechanical, thermal and electrical parameters, and fusing a physical mechanism to construct a network structure; input features are determined and preprocessed, acoustic emission, strain, temperature and other signals are collected, and input layer features are constructed after filtering and standardization processing. In the aspect of model architecture design, a neural network comprising an input layer, a hidden layer and an output layer is constructed, and a physical mechanism is integrated in the neural network. A loss function under physical constraints is constructed to ensure that the model accords with a physical rule, a final model is obtained through model initialization and training optimization, finally, real-time dynamic monitoring of damage is achieved, a diagnosis result is output, and the reliability of material use is improved.

The invention discloses an airfoil profile multi-objective optimization method and system based on reinforcement learning and Pareto, and relates to the technical field of mechanical optimization design. Comprising the steps of building an airfoil profile parameterization design platform based on a convolutional neural network and a generative adversarial network, evaluating the airfoil profile performance by adopting Neuralfoil based on a physical neural network, and building an airfoil profile multi-objective optimization model based on reinforcement learning and Pareto optimization. According to the method, through combination of the convolutional neural network and the generative adversarial network, airfoil profile parameter dimension reduction is realized, and airfoil profile malformation in a design space is avoided; the problem that traditional Xfoil is not converged in the calculation process is solved through Neuralfoil, the method is particularly suitable for processing airfoils with the shapes frequently adjusted in the reinforcement learning process, and more stable and faster aerodynamic characteristic calculation is provided; through combination of reinforcement learning and Pareto, application exploration of reinforcement learning in the aspect of airfoil profile multi-objective optimization is realized, and a new thought for airfoil profile optimization is provided.

The invention relates to a hip joint motion track prediction method based on a Lagrange physical neural network. The method comprises the following steps: inputting kinematics data of a hip joint at a current moment into a Lagrange physical neural network, and training a kinetic parameter estimation module based on a pre-constructed loss function according to a trajectory prediction value and a real trajectory to obtain a trained Lagrange physical neural network; and predicting a hip joint movement track at the next moment by adopting a Lagrange physical neural network. By adopting the method, the unification of the motion trail prediction interpretability and the algorithm robustness is realized.

According to the method, the system, the medium and the equipment for monitoring the abnormal state of the wind generating set, a self-adaptive noise reduction mechanism, feature extraction and model classification are combined, so that physical information features are effectively extracted, and accurate state detection is achieved. The method comprises the following steps: firstly, building a signal acquisition system, acquiring an acoustic signal in real time, and executing direct current offset removal preprocessing to eliminate low-frequency noise; secondly, noise reduction of the original sound signal is completed by using a self-adaptive noise reduction method; then, extracting the MFCC characteristics of the noise reduction signal; then, calculating an MFCC inter-frame correlation feature and an aerodynamic-harmonic imbalance factor, and forming a fusion feature vector; and finally, constructing a physical neural network model based on physical information to carry out abnormal state detection.

The invention discloses a cabin immersion flow field prediction method and device based on an adaptive physical neural network, and relates to the field of complex flow field prediction of neural networks. The problems that in the prior art, a cabin immersion flow field has high nonlinearity and dynamic changes, and the free liquid level hydrodynamic force phenomenon is complex and difficult to predict are solved. The method comprises the following steps: constructing a ship model of a low-speed flow field; importing the ship cabin model into STAR-CCM + software for fluid simulation, and generating grid data of a flow field; processing the generated grid data of the flow field according to time steps; processing and storing the processed grid data through a Pytorch framework, and constructing a data set for neural network training; performing dynamic training on the data set trained by the neural network by using a Pytorch framework; the above steps are used for cabin immersion flow field prediction, and cabin immersion flow field prediction based on the adaptive physical neural network is completed. The method is suitable for the self-adaption field of cabin immersion flow fields.

The invention discloses a bearing fault diagnosis method and system based on a simulated physical neural network. The method comprises the following steps: S1, preprocessing a collected vibration signal to obtain a to-be-diagnosed signal; s2, executing a hybrid optimization algorithm to obtain an optimal transmission band parameter and an optimal weight parameter; s3, configuring a feature extraction module according to the optimal transmission band parameter, and writing the optimal weight parameter into a classification module; s4, inputting the to-be-diagnosed signal into a feature extraction module to obtain a simulation feature vector; s5, inputting the simulation feature vector into a classification module, and outputting a classification voltage signal; and S6, determining a fault type, and outputting a result. According to the method, a hybrid optimization algorithm is adopted, the ratio of the inter-class dispersion degree to the intra-class polymerization degree of the feature vectors is calculated, optimal adaptation of feature extraction and classification tasks is achieved, and the diagnosis precision and generalization ability are improved; by constructing a full analog domain signal processing architecture, analog-to-digital conversion and digital calculation are not needed, and microwatt-level power consumption and microsecond-level real-time response are realized.

The invention provides a base station multi-energy power supply optimization control method and device and a readable storage medium, and the method comprises the steps: carrying out the load prediction of a base station through a physical neural network model, and obtaining a current base station load; obtaining the current photovoltaic generating capacity of the base station; when the current photovoltaic generating capacity is smaller than the current base station load, determining that the current power supply mode of the base station is photovoltaic and commercial power joint power supply; and performing electricity price prediction, and determining the power supply proportion of the commercial power according to an electricity price prediction result. Optimal matching between the communication base station mains supply and photovoltaic cell power generation can be achieved, optimal matching of multi-energy power supply is achieved on the premise that the load requirement is met, the power consumption cost of the base station can be effectively reduced on the premise that the load requirement of the base station is met to the maximum degree, and the power consumption efficiency is improved compared with the current power consumption state. And the energy consumption of the base station is reduced under the condition that the hardware configuration is not changed.

The invention discloses a photovoltaic power short-term prediction method based on an adaptive physical neural network. The method comprises the following steps: acquiring historical meteorological data and photovoltaic power data of a photovoltaic power station; selecting meteorological characteristic data from the historical meteorological data, and clustering weather types; taking the meteorological characteristic data and the photovoltaic power data corresponding to each weather category label as a group of training samples, and generating an original training sample set; performing oversampling on the minority class sample set to generate a new sample, and combining the new sample with the original training sample set to form an equalized training sample set; determining a meteorological characteristic combination of each weather type according to the physical characteristics of photovoltaic power generation under different weather types; based on the combination, extracting corresponding meteorological characteristic data from the equalization training sample set as input, taking corresponding photovoltaic power data as a training target, and training a photovoltaic power short-term prediction model; and optimizing network parameters and physical parameters based on the composite loss weight to realize photovoltaic power adaptive prediction under different weather types.

The present invention discloses a fluid topology optimization method and system based on a physical neural network. The method includes: based on the fluid topology optimization problem, determining the basic parameters of the optimized fluid pipeline through a two-dimensional steady-state control equation; based on the basic parameters of the fluid pipeline, constructing a fluid topology optimization mathematical model according to the target optimization function; based on the fluid topology optimization mathematical model, sampling the calculation domain sample points, and constructing a neural network model in combination with the basic parameters of the fluid pipeline; constructing a loss function and iteratively training the neural network model according to the loss function to obtain a topology optimization result. The present invention solves the problem of uneven density distribution of fluid pipeline materials, shortens the calculation time of fluid pipeline material density distribution, and realizes a method for finding the optimal material density distribution in the design space of uniformly distributed density materials. As a fluid topology optimization method and system based on a physical neural network, the present invention can be widely used in the field of fluid topology optimization.

The invention relates to the technical field of SiC MOSFETs, particularly discloses a SiC MOSFET I-V characteristic modeling method and system based on a physical information neural network, and aims at solving the problem that an existing SiC MOSFET simulation model cannot give consideration to high precision, high speed and practicability. Loss between a calculated value of a physical model and a predicted value of the neural network is added into a loss function of the neural network, and the model can be efficiently and accurately trained only through a small amount of data. According to the method and the system, the internal advantages of the physical neural network are fully utilized, and the actual physical behavior information of the device is embedded into the model, so that a modeling result is highly matched with experimental data in numerical value, strict physical interpretation is shown in theory, and key technical support is provided for industrial application of the silicon carbide power device.

The invention discloses a gas foil bearing wear high-fidelity modeling method based on a physical neural network, and relates to the technical field of rotor dynamics and fault diagnosis. The method comprises the following steps: firstly, constructing physical models of gas foil bearing wear, wherein the physical models comprise a gas foil bearing structure and lubrication mechanism model and a gas bearing rotor system nonlinear dynamic model; and then building a hybrid modeling framework fusing a physical mechanism and data driving, training a model mismatch item by adopting a neural network, representing an unknown parameter by adopting a Sigmoid function, introducing a displacement compensation factor, and finishing model parameter updating by combining forward calculation and reverse calculation. According to the method, high-fidelity modeling of wear evolution and key parameter collaborative identification can be realized, the model adaptability and the data matching precision under complex working conditions are improved, and technical support is provided for a gas bearing digital twin system and predictive maintenance.

The application provides a method and device for optimizing an improved network of a physical neural network. The method comprises adding an input scaling layer, an output scaling layer and a feature layer to the basic physical neural network to construct an improved physical neural network; using a plurality of observation points and the improved physical neural network to make a difference as a first loss function to perform first training on the improved physical neural network; and adding a residual term of the differential equation set to the first loss function to obtain a second loss function to perform second training. The method realizes the feasibility of using a physical neural network to solve a strongly rigid rate theory equation set, adds observation points as a supervised training term and performs pre-training to determine the optimization direction of the model, so that the model training has higher training efficiency. An optimization method for balancing each residual weight is adopted, so that the improved network has better regularization effect.

The invention discloses a biological image data processing prediction method based on a physical neural network, and belongs to the field of biological image processing, and the method comprises the steps: carrying out the denoising of a biological image: constructing a multi-layer memristor neural network structure which comprises an input layer, a plurality of hidden layers and an output layer, the memristors are distributed among all layers of neurons so as to realize weighting and processing of signals; preparing a large number of noisy biological images and corresponding clear image pairs as a training data set; according to the denoising method based on the memristor neural network, noise can be intelligently recognized, noise interference in a biological image can be effectively removed, image details can be reserved to the greatest extent, and high-quality image data can be provided for subsequent image analysis; by using the strong parallel processing capability and feature learning capability of the simulated VLSI neural network, a complex structure in a biological image can be accurately segmented, the segmentation precision and robustness are improved, and accurate analysis of a specific structure in biomedical research is facilitated.

The invention discloses a task scheduling method, device and equipment and a storage medium, relates to the technical field of resource management, is applied to a Kubernetes cluster, and comprises the following steps: determining a current cloud computing task to be scheduled; determining to-be-selected physical neural network processors according to a matching relationship between a virtual resource request corresponding to the current cloud computing task and physical resource information corresponding to the physical neural network processors in the current cluster; determining a target physical neural network processor and a target scheduling node from the to-be-selected physical neural network processors based on a preset processor scoring rule in combination with the virtual resource request; and creating virtual resource information through the target scheduling node to execute the current cloud computing task. Therefore, by means of the virtualization technology, the cloud computing task is scheduled through the virtual resources corresponding to the physical neural network processor, the virtual resources can be dynamically allocated according to the running condition of the cluster, and the resource utilization rate and the task processing efficiency are improved.

The application discloses a physical neural network-based electrical property tomography method, which spatially obtains permittivity and conductivity of all points through four steps.The application does not need to simplify the core equation of MR-EPT to solve, and does not need to use a numerical method to solve by discretizing the derivative of the dielectric property in space, thereby avoiding the discretization error, so that the application has higher precision in obtaining the permittivity and the conductivity.The physical neural network-based electrical property tomography method uses a neural network to construct a mapping relationship between a transmit field and its spatial derivative and electrical properties, so that the precision of the obtained result is higher.

The invention relates to a process industry system modeling method based on an anti-noise hard constraint physical neural network. The method comprises the following steps: firstly, constructing an anti-noise hard constraint physical neural network comprising a framework front end, an anti-noise hard constraint correction layer and an anti-noise loss function; based on the physical relationship between the input and the output of the required modeling system, constructing a linear equality constraint; then quantifying the uncertainty of each sensor in a system needing modeling, and configuring parameters of an anti-noise hard constraint correction layer based on the uncertainty and linear equality constraints; and finally, adopting an anti-noise loss function, and training the anti-noise hard constraint neural network after parameter configuration by using a noise data set to obtain a process industry system model. Compared with the prior art, the method has the advantages that the prediction accuracy of the built substitution model in the noisy input environment is improved while the physical consistency is ensured.