51 results about "Physical neural network" patented technology

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 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 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 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.

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 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 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 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 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.

This invention discloses a method, apparatus, device, and storage medium for correcting measurement data of a thermal system, relating to the field of thermal system measurement and processing technology. The method includes the following steps: obtaining a simulation calculation model of the thermal system; constructing a known parameter correction model and an unknown parameter estimation model through a physical neural network; connecting the outputs of the known parameter correction model and the unknown parameter estimation model with the input of the simulation calculation model to obtain a fast correction model; inputting real-time measurement data and real-time operating data into the trained fast correction model to obtain the corresponding correction values ​​and unknown parameter estimates. Compared to classical data coordination methods, the method proposed in this invention effectively avoids the problems of slow calculation speed and poor iterative convergence, making it more suitable for the measurement parameter correction calculation of complex thermal systems in engineering application scenarios.

The invention provides a pipeline leakage detection method and system based on a domain adaptive physical neural network, and the method comprises the steps: inputting the space coordinates of a pipeline into a deep learning network model, and obtaining a physical quantity prediction value of the pipeline; inputting the features output by the previous layer of the deep learning network model into a domain classifier to obtain a feature source category; determining experimental measurement values corresponding to the space coordinates, experimental data loss and simulation data loss between CFD simulation values and physical quantity prediction values, determining physical equation residual loss and leakage boundary loss of the physical quantity prediction values, and determining domain confrontation loss according to feature source categories output by a domain classifier; training deep learning network model parameters, leakage position parameters and leakage aperture parameters; and determining the leakage information of the pipeline according to the physical quantity predicted value, the leakage position parameter and the leakage aperture parameter output by the trained deep learning network model. According to the invention, the precision, credibility and robustness of leakage detection are improved.

This invention discloses a photovoltaic power prediction method and system that considers physical mechanism correction. It acquires historical photovoltaic power plant data and future physical data, then selects parameters highly correlated with historical photovoltaic power as prediction inputs. These input parameters and future physical data are fed into a pre-trained physical-neural network joint model to obtain the final photovoltaic power prediction. The training process of this joint model includes inputting the prediction input parameters into the neural network model to obtain neural network prediction values, simultaneously obtaining physical prediction values ​​based on the physical data, and combining the two through weight correction to obtain the final prediction value. Finally, a joint loss function is determined based on the actual photovoltaic power and the final prediction value, and a GA genetic algorithm is used to optimize the weights and neural network hyperparameters, thereby obtaining the trained model. This method combines physical mechanisms and neural network technology, improving the accuracy and reliability of photovoltaic power prediction.

The invention provides a flatness prediction method and system based on a wavelet and a physical neural network, and relates to the technical field of road engineering, and the method comprises the steps: carrying out the calculation through a scale function of a dbN wavelet, and obtaining a first weight coefficient of a pavement longitudinal contour elevation function; solving the quarter vehicle dynamic equation, determining unsprung mass displacement and sprung mass displacement, and obtaining a second weight coefficient and a third weight coefficient corresponding to the unsprung mass displacement and the sprung mass displacement respectively through wavelet transform; training a preset deep learning network model by using the obtained weight coefficient, and determining a loss function of the preset deep learning network model; performing iterative optimization on the preset deep learning network model by using a Bayesian optimization algorithm and the loss function to obtain a target deep learning network model; and inputting the fourth weight coefficient of the to-be-measured pavement into the target deep learning network model, and determining the to-be-measured unsprung mass displacement, the to-be-measured sprung mass displacement and the international flatness index.

The application provides a spacecraft lithium battery health state estimation method based on a physical information neural network, and has the characteristics that the following steps are included: a second-order hybrid equivalent circuit model of a battery monomer is established, and the relationship between the open circuit voltage of the equivalent circuit model and the state of charge of the battery is represented by a multilayer perceptron; the multilayer perceptron is trained by using the open circuit voltage and the state of charge of the battery in the collected sample data; the multilayer perceptron is used as a data driving layer of a physical neural network model, and the physical information neural network model is designed; the training of the physical neural network model is carried out by using the discharge curve in the collected sample data, and the estimation of the battery health state is realized by using the trained neural network model. The application does not need additional tests, and on the basis of using limited data to establish a physical information neural network model, the health state online estimation is accurately realized, and the stable operation of the spacecraft is ensured.

The invention discloses a hydroacoustic physical neural network construction method, and belongs to the crossing field of hydroacoustic physics and artificial intelligence. The core of the method is to directly realize neural network calculation by utilizing the physical propagation process of water sound waves in a medium. Comprising the steps of hydroacoustic sensor array deployment, multi-layer physical mapping architecture construction and hybrid training mechanism implementation. Deploying a sound source and an N-layer physical cascade system to acquire acoustic signals and environmental parameters; a network architecture embedded with an acoustic propagation rule is constructed, a physical convolution layer is designed based on a wave equation, a physical activation layer simulates medium attenuation, and a loss function adopts layered physical constraint; an in-situ forward propagation-digital back propagation mixed training mode is adopted, and parameters are updated through a layered differentiable digital model and a layered optimization strategy. The problems that a traditional digital neural network is high in energy consumption, insufficient in generalization ability and lack of physical interpretation are solved, and the method has the advantages of being low in energy consumption, high in robustness and naturally provided with physical rules and is suitable for scenes such as underwater intelligent sensing, communication, detection and sound field simulation.

The application discloses a new energy power system transient stability evaluation method and device, belongs to the field of power grid transient stability evaluation technology, and comprises the following steps: constructing a transient dynamics model of a new energy power system; including establishing a transient dynamics model of a synchronous machine, establishing a transient dynamics model of a virtual synchronous machine, and establishing a transient dynamics model of the whole system based on the transient dynamics model of the synchronous machine and the transient dynamics model of the virtual synchronous machine; adopting an adjoint gradient physical neural network to predict the transient characteristics of the system; including: constructing a transient dynamics dataset, calculating a data loss function, a physical loss function and a total loss function, calculating an adjoint state and its gradient, and updating the parameters of the adjoint gradient physical neural network; and adopting a self-attention network to evaluate the transient stability performance of the system. The application realizes quantitative evaluation of the transient stability of a power system, and solves the problems of poor interpretability and low precision of existing data-driven artificial intelligence methods.

The application belongs to the technical field of building energy saving, and provides a real-time inversion method and system for the heat transfer coefficient of a building envelope, comprising: obtaining surface parameters and environmental parameters of the building envelope; based on the obtained parameters and a physical neural network model, taking the minimum of a joint loss function based on a physical mechanism as the target, and inversely obtaining the optimal thermal conductivity coefficient of the building envelope; and calculating the heat transfer coefficient of the building envelope according to the obtained optimal thermal conductivity coefficient of the building envelope, thereby completing the real-time inversion of the heat transfer coefficient of the building envelope.

The invention provides a cold region crop irrigation system based on multi-source sensing prediction, and belongs to the technical field of intelligent irrigation. The system comprises the steps that soil temperature, ice content, moisture and pipeline working condition data are collected through a multi-source sensor, and a physical neural network fused with ice resistance effect factors is utilized; the effective water increment of the root system layer is predicted by coupling a heat-water migration rule; the irrigation water amount, the irrigation time and the water temperature are dynamically determined in combination with a multi-objective optimization model, and the pipeline head loss is counteracted in cooperation with a flow dynamic compensation module. The system can adapt to the low-temperature environment of the cold region, accurate matching of irrigation supply and demand is achieved, the freezing and blocking risk and water resource waste are effectively reduced, and the irrigation efficiency and crop stress resistance are improved.

The invention discloses a fusion control method and device for a shouldering growth process and electronic equipment. The method comprises the following steps: acquiring image sequence data and process sequence data; predicting to obtain a first pulling speed response value through a first mapping model based on the image sequence data and the process sequence data, wherein the first mapping model comprises a pre-trained residual neural network; predicting a second pulling speed response value based on the process sequence data through a second mapping model, wherein the second mapping model comprises a physical neural network; and outputting a pulling speed control response value through a fusion control module according to the first pulling speed response value and the second pulling speed response value, thereby controlling the silicon single crystal shouldering growth process. According to the technical scheme, the drawing speed response value is predicted according to the time sequence data of the image and the process through the mapping model, the drawing speed control response value finally used for controlling the shouldering growth process is comprehensively determined in combination with the two drawing speed response values, and the reliability of automatic control over the shouldering growth process is improved.

This invention belongs to the field of safety monitoring technology for electrochemical energy storage systems, and discloses a hierarchical diagnostic method for abnormal degradation of energy storage batteries that combines data and mechanisms. The specific steps are as follows: Step 1: Multi-dimensional dynamic feature extraction. Data preprocessing, voltage range feature identification based on a sliding window, multi-dimensional dynamic feature extraction, and feature optimization and filtering are performed on the original operating data of the energy storage battery. Through standardized formulas for voltage, capacity, and temperature, and related formulas for feature calculation, the optimal feature set characterizing the battery's aging state is obtained. This invention, based on a holographic dynamic characterization method that combines sliding window voltage range feature identification with incremental capacity-internal resistance multi-source fusion, overcomes the limitations of single parameters and achieves multi-dimensional and accurate characterization of the battery's aging state. Simultaneously, a hybrid reasoning framework combining a lightweight physical neural network and temporal fuzzy logic is constructed, ensuring interpretability while quantifying uncertainty.

The invention discloses a system and a method for monitoring oil spill pollution of soil. The system mainly comprises a signal transmitter, a point source electrode, a measuring electrode array, a high-density electrical method host and a data acquisition system. The positive electrode of the signal transmitter is connected with the point source electrode inserted into soil, and the measuring electrode array is arranged in a detection area in a latticed mode and connected with the high-density electrical method host through a multi-core cable. A direct-current voltage gradient method is adopted to measure surface potential distribution, a high-density resistivity method is combined to obtain soil resistivity data, space coordinates, potential values and resistivity serve as input of a physical neural network, and pollutant concentration three-dimensional distribution is predicted through the trained network. Compared with the prior art, the device has the characteristics of high detection efficiency, good positioning precision and wide application range.

This invention relates to a multi-scale neural network electric field simulation method for insulation assessment of lithium-ion battery energy storage, belonging to the technical field of lithium-ion battery energy storage systems. It constructs a multi-scale geometric and physical model including a cell-level microscopic geometric model and a module-level macroscopic geometric model; builds a multi-scale cyber-physical neural network architecture consisting of microscopic subnets, macroscopic subnets, and cross-scale coupling layers. The cross-scale coupling layers employ a bidirectional attention mechanism to achieve feature interaction and constraint transfer between microscopic and macroscopic dimensions; generates a training dataset based on the multi-scale geometric and physical model; trains the network using a hybrid loss function including data loss, physical loss, boundary loss, and cross-scale coupling loss; inputs the parameters to be simulated into the trained network, outputting multi-scale electric field distribution data; extracts electric field feature indicators from the electric field distribution data and inputs them into the insulation state assessment model, outputting the insulation safety level. This invention achieves rapid and accurate electric field simulation and integrated assessment of insulation safety.

The invention discloses an underwater cooling system sea water pump fault diagnosis method based on a physical information neural network, and relates to the technical field of underwater equipment fault diagnosis, and the method comprises the steps: carrying out the multi-source data synchronous collection and preprocessing of a sea water pump in an underwater cooling system when the sea water pump operates under typical working conditions in a land-based environment and an underwater environment; constructing a large-scale source domain data set and a target domain data set of underwater small samples; establishing a multi-physical field coupling model of the sea water pump as a loss function of a physical constraint embedded physical information neural network; pre-training the physical neural network by using the source domain data set, and performing field adaptive fine tuning based on the target domain data set to reduce the feature distribution difference between the source domain and the target domain; based on the trained physical neural network, each physical field fault sensitive feature conforming to the physical law is extracted and input to the decision network to identify the typical fault type of the sea water pump, and the method significantly improves the accuracy and robustness of sea water pump fault diagnosis of the underwater cooling system.

The invention discloses a liquid cooling electronic equipment cavity flow field prediction method based on a quantum physics neural network, and relates to the technical field of electronic equipment thermal management and computational fluid mechanics numerical prediction. Comprising the following steps: constructing a physical model of flow and heat transfer in the liquid cooling cavity; collecting or generating multi-source supervision data, and performing normalization processing; constructing a hybrid physical neural network structure with a quantum feature head; designing a total loss function containing multiple physical constraints and local weighted terms; carrying out iterative training by adopting a late time and hot plume region oversampling strategy; and utilizing the trained quantum physical neural network to quickly predict the flow field and the temperature field in the cavity under different working conditions. By constructing a hybrid physical neural network model and integrating multi-source supervision, partial differential equation residual error, boundary condition constraint and gradient sensing items of local thermal plume and thermocline in a loss function, high-precision prediction of a three-dimensional flow field and a temperature field in the liquid cooling cavity is realized.

The application relates to a structure topology optimization method based on a boundary physical information neural network, wherein the method comprises the following steps: generating an implicit level set field of a target structure based on a pre-constructed geometric neural network, generating a boundary node coordinate set and a boundary element set, and generating geometric information of each boundary element; predicting a physical field variable of the boundary node based on a pre-constructed physical neural network, calculating a boundary physical information residual error, updating the weight of the physical neural network, and solving the boundary physical field; constructing a topology optimization loss function, calculating the gradient of the topology optimization loss function with respect to the weight of the geometric neural network, updating the weight of the geometric neural network, driving the structure topology evolution, and generating a structure topology optimization result until a preset convergence condition is met. Therefore, the problems that, in the related art, the boundary element algorithm lacks an acceleration mechanism suitable for dynamic topology and shape sensitivity analysis is complex, leading to difficulties in explicit derivation and low repeated calculation efficiency are solved.

This invention discloses a multimodal physical neural network system and method based on gradient neurons, relating to the field of multimodal information fusion technology. The system includes an input layer, a hidden layer, and an output layer. The input layer encodes information from at least three modalities as the number, frequency, and amplitude of electrical pulses, and synthesizes them into a single input electrical signal through time-division multiplexing. The hidden layer uses a two-stage semiconductor laser as its core. Under the sequential excitation of driving voltages encoding different modal information, the carriers in its gain region undergo nonlinear accumulation in two stages, thereby achieving superadditive fusion of multimodal information at the physical level and outputting a gradient pulse optical signal characterizing the fusion result. The output layer amplifies, converts, and processes the optical signal. This invention utilizes the physical dynamics characteristics of semiconductor lasers to directly simulate the superadditive fusion behavior of biological neurons at the hardware level, effectively improving the efficiency and quality of multimodal information fusion.