7396 results about "Statistical physics" patented technology

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention discloses a complex terrain three-dimensional modeling and earthwork volume calculation method based on a multi-source fusion point cloud, belongs to the technical field of surveying and mapping and engineering surveying, and mainly solves the problems of low terrain modeling precision and insufficient earthwork calculation efficiency in a complex scene. The method comprises the following steps: constructing a ground-air integrated multi-source sensing network to synchronously acquire laser point cloud, multi-view images and positioning data; adopting PointNet + +-based initial registration and multi-scale ICP fine registration fusion to generate a unified point cloud; combining semantic segmentation and penetration probability filtering to accurately extract a digital elevation model; constructing a similar triangular prism voxel model by using a constrained Delaunay triangulation network; and finally, the earth volume is rapidly calculated by adopting a GPU parallel voxel cutting and filling algorithm, so that high-precision terrain modeling and rapid engineering quantity calculation are realized, and the method can be efficiently and reliably applied to large-scale projects such as roads and mines.

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention discloses a live-action three-dimensional dynamic change modeling method based on digital twinning. The method comprises the following steps: collecting multi-source data; carrying out space registration and time synchronization processing, and outputting a fusion data set; carrying out three-dimensional modeling, optimizing model parameters, and generating a live-action three-dimensional model; virtual-real mapping is carried out, and generated virtual space data and state parameters thereof are stored; performing differential analysis to generate a change detection result data set; performing dynamic change modeling to generate an updated virtual model; executing geometric accuracy optimization and semantic consistency check, and outputting an optimized virtual model; bidirectional data flow association and dynamic updating are executed, and the live-action three-dimensional dynamic change modeling process is completed. The real scene dynamic updating is realized by adopting the improved Gaussian sputtering modeling, and the method has the advantages of high precision, strong real-time performance and virtual-real synchronization.

The present application relates to the technical field of mold structure design. Disclosed are a mold structure design optimization method and apparatus. The method comprises: performing parametric modeling on a three-dimensional model of a laser cutting die to obtain an adjustable parameter set; performing cutting die geometric feature extraction and feature classification to obtain a feature classification result; performing adaptive multi-scale mesh division to obtain a multi-scale finite element analysis model; performing multi-physics coupling analysis to obtain stress distribution data, deformation data and temperature field distribution data; performing variance analysis to obtain target impact parameters, and, on the basis of the target impact parameters, constructing a multi-objective optimization model; by means of a non-dominated sorting genetic algorithm, solving the multi-objective optimization model to obtain a Pareto optimal solution set; and determining, from the Pareto optimal solution set, target optimization structural parameters of the laser cutting die, thereby improving the optimization efficiency while ensuring the calculation accuracy, and achieving the overall performance improvement of the laser cutting die.

A multi-physics computation method and system for digital twin online simulation, relating to the technical field of physics simulation. A multi-physics coupling simulation computation model of a simulated object is established, and the multi-physics coupling simulation computation model is simplified, and the order of a temperature field simulation model is reduced, thereby greatly reducing the amount of computation, and improving computational efficiency; a low-precision dataset is obtained by means of temperature field analysis, so that the data size required for a model is reduced by means of low-precision data, thus reducing modeling costs; and a basic data-driven model is constructed by means of the low-precision dataset and a sample space corresponding to the low-precision dataset, so that a temperature field distribution result can be rapidly outputted, further improving computational efficiency and reducing computational errors.

The present invention relates to a contour accuracy compensation method and system for a precision hot-press forming mold. The method comprises: delineating grids on a three-dimensional model of a hot-press forming mold to obtain mold surface grid data; performing curvature analysis to obtain a high-curvature region and a low-curvature region; performing high-density sampling on the high-curvature region, and performing low-density sampling on the low-curvature region, so as to obtain a sampling point set; using a coordinate measuring machine to perform three-dimensional coordinate measurement on the sampling point set to obtain surface contour data; performing comparative analysis on the surface contour data and a theoretical model to obtain a contour error distribution; on the basis of the contour error distribution, using an inverse distance weighted interpolation algorithm to perform interpolation operation on an entire mold surface to obtain a mold surface compensation amount distribution; and on the basis of the mold surface compensation amount distribution, correcting the three-dimensional model of the hot-press forming mold to generate a compensated mold processing model. The implementation of the present invention realizes intelligent and automated contour accuracy compensation.

The invention belongs to an AI-based charging process dynamic temperature control method and system, and the method comprises the steps: collecting various data, and obtaining the heat dissipation difference data of a battery according to the arrangement channel difference data and the aging state of a battery pack structure. And the attenuation influence degree of the cooling medium on the battery performance is analyzed, the charging related data is analyzed to obtain transient electromagnetic interference, and transient heat dissipation influence factors are obtained in combination with the heat dissipation difference data of the battery. And analyzing the interaction between the battery heat dissipation difference data and the battery performance data, constructing an AI temperature prediction model in combination with the attenuation influence degree, inputting real-time battery data, and outputting a predicted temperature. Adjusting the temperature regulation and control strategy according to the transient heat dissipation influence factors to obtain a dynamic temperature adjustment strategy; according to the invention, the safety and service life of the battery can be improved, the charging efficiency and physical examination are optimized, and intelligent and adaptive management is realized.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a lightweight three-dimensional reconstruction method and system based on a three-dimensional Gaussian model, and the method comprises the steps: constructing the three-dimensional Gaussian of a target scene based on a multi-view original image of the target scene through employing a 3DGS algorithm; identifying the minimum resolution of each Gaussian primitive in the three-dimensional Gaussian, setting a spherical region according to the minimum resolution, taking the intersection result of the spherical region and the Gaussian primitive as a region evaluation score, and trimming the redundancy region geometry in the three-dimensional Gaussian in combination with the opacity; and taking the maximum zoom scale of the Gaussian primitive as a spatial density proxy variable, and dynamically distributing spherical harmonic coefficient orders according to a local spatial density proxy variable so as to meet the rendering requirements of different three-dimensional Gaussian geometric regions. On the premise that the image synthesis quality is not lost, the model scale and the transmission overhead can be remarkably compressed, the resource constraint condition of the edge computing equipment can be effectively adapted, and the higher training speed and the better rendering effect are achieved.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention provides a feed mixing process uniformity dynamic evaluation and parameter adjustment method, and relates to the technical field of data processing, and the method comprises the steps: building a material characteristic matrix according to raw material characteristic data; correspondingly storing the material characteristic matrix and initial operation parameters of the mixing equipment to generate first mixing control data; in the mixing process, collecting local vibration amplitudes and temperature rise rates at different spatial positions, and combining the local vibration amplitudes and the temperature rise rates with the first mixing control data; calculating the vibration amplitude distribution difference in adjacent sampling periods in the feed mixing treatment process to obtain a spatial fluctuation index; when the spatial fluctuation index is continuously reduced and the temperature rise rate is periodically and suddenly increased, generating local accumulation indication data, and triggering short-time acceleration and inversion operation; executing feed mixing treatment, and generating a mixing termination signal when the spatial fluctuation index change rate of three continuous sampling periods is lower than a preset fluctuation threshold value; according to the invention, autonomy and accuracy of feed mixing uniformity adjustment are improved.

According to the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning provided by the invention, the actually measured spectrum and the reference spectrum of the pollution gas smoke plume are collected, the spectrum data set is constructed after differential processing, the meteorological data and the online mass concentration label are synchronously collected, and meanwhile, the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning are provided. A high-resolution gas absorption section is obtained and is convolved into a matrix; and constructing a deep learning model fusing a feature extraction module, an expanded least square module and a full connection module, taking the spectral data set, the meteorological data and the absorption cross section matrix as input, performing training in combination with labels to obtain an optimization model, and predicting the mass concentration of the target gas. According to the method, the problems of error accumulation, low calculation efficiency and poor interpretability caused by dependence on a complex physical model in a traditional method are solved, and high-precision, high-efficiency and interpretable real-time imaging of the mass concentration of the smoke plume of the pollution gas is realized.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

The invention relates to the technical field of quantum computing, and discloses a quantum circuit mapping method and system based on deep reinforcement learning, according to the method, an original logic quantum circuit is analyzed and simplified into a ZX-graph, then the ZX-graph is converted into a quantum circuit dependency graph, the computing complexity is remarkably reduced, and the quantum circuit mapping efficiency is improved. A large-scale quantum circuit mapping task can be processed within reasonable time, a Markov decision process environment is constructed through a quantum circuit dependency graph and quantum chip parameters, a deep reinforcement learning agent is trained by using the Markov decision process environment, and an optimal mapping strategy optimization model is obtained. Through the optimal mapping strategy optimization model, the corresponding optimal quantum bit mapping scheme can be output to the current to-be-mapped logic line, so that the method can be adapted to quantum chips of various topological structures, and the generalization ability and adaptability of the mapping scheme are improved.

The invention discloses a regional carbon emission prediction method and system based on carbon emission monitoring and electronic equipment, and relates to the technical field of carbon emission prediction, and the method comprises the steps: collecting regional discrete point emission data through a multi-modal sensing network, and generating a regional-level continuous flow field; performing feature decoupling and quantization on different emission source types to generate a source stream feature vector matrix; reconstructing a continuous space-time emission field based on the source stream feature vector matrix, identifying a conduction path, and dynamically binding with field features along the path to generate a dynamic field data set; wherein the field characteristics comprise emission intensity, diffusion rate and regional coupling coefficient; constructing a field domain succession rule model based on the dynamic field data set, identifying a dominant evolution path based on an evolution baseline library of the model, injecting external disturbance along a conduction path of the dominant evolution path, and generating a multi-branch evolution scene tree; wherein the external disturbance comprises policy regulation and control intensity and a meteorological anomaly index. According to the invention, accurate prediction of regional carbon emission is realized.

The invention belongs to the technical field of geophysics. According to the truncation model space three-dimensional magnetotelluric inversion method and system, a three-dimensional inversion objective function is constructed according to an inversion initial model, the inversion objective function is optimized by adopting a Gaussian-Newton method, and a Gaussian-Newton increment equation is obtained, a sensitivity matrix in the Gaussian-Newton increment equation is a sparse matrix which is obtained after truncation is carried out by adopting a distance truncation threshold value and a sensitivity amplitude truncation threshold value, the Gaussian-Newton increment equation is converted into an unconstrained least square form, and an inversion updating direction is determined according to the least square form; and performing iterative optimization according to the initial inversion model, the inversion updating direction and the model updating step length to obtain an updated inversion model. According to the invention, units with small contribution to inversion updating are effectively cut, and the model space corresponding to each piece of data is reduced.

The invention discloses an island-shaped frozen soil degradation prediction method based on remote sensing and ground temperature coupling, and relates to the technical field of frozen soil degradation prediction in cold regions, and the method comprises the following steps: S1, multi-source data fusion and intelligent decomposition, S2, cross-scale dynamic parameterized model construction, S3, quantum enhanced Bayesian multi-scale calibration, and S4, three-dimensional dynamic uncertainty quantification and prediction output. According to the method, the problems of frozen soil data scarcity and remote sensing conversion precision are solved through multi-source remote sensing and actually measured data fusion in combination with CGAN, AM-PFN and virtual sample closed-loop expansion; the defects of a traditional model are overcome through a microscopic-macroscopic coupling model, DRL parameter switching and meta-learning algorithm selection; through quantum enhanced Bayes, DMD and a four-dimensional uncertainty matrix, cross-scale prediction divergence is reduced; and through a carbon release risk correlation model and a co-evolution product, a prediction dimension is expanded, and a support is provided for ecological protection of the cold region.

The invention provides a reinforcement corrosion electrochemical parameter inversion method based on LSTM (Long Short Term Memory) time sequence prediction, which comprises the following steps: S1, acquiring electrochemical time sequence data in a reinforcement corrosion process through an electrochemical workstation to form an original reinforcement corrosion electrochemical time sequence data set; s2, preprocessing is carried out to obtain a training set, a verification set, a test set and normalization coefficients of all parameters; s3, constructing and training an LSTM time sequence prediction model; s4, constructing and calibrating a steel bar corrosion electrochemical parameter forward modeling model; and S5, constructing an inversion framework fusing a particle swarm optimization algorithm, a simulated annealing algorithm and an Adam optimization algorithm, forming closed-loop cooperation by the particle swarm optimization algorithm, the simulated annealing algorithm and the Adam optimization algorithm so as to minimize an error between a target electrochemical response parameter and a theoretical electrochemical response parameter, and outputting an inversion result. According to the method, through organic combination of time sequence prediction and multi-algorithm cooperation, the problems that a traditional inversion method is low in precision and poor in stability are solved, and a reliable technical means is provided for reinforced concrete structure health monitoring.

Systems and methods for performing image modification. In particular, the system can, using a rectified flow neural network, perform an image inversion and image editing process to generate a modified image that has been modified according to a conditioning input received by the system.

The invention provides a prediction method for a sudden change type signal of gas dissolved in oil. The prediction method comprises the following steps: firstly, acquiring a time sequence signal of transformer gas monitoring; processing the time sequence signal according to a variational mode decomposition algorithm to obtain a plurality of mode components; according to the variational mode decomposition algorithm, the sum of confusion entropies of all mode components is used as an objective function of parameter optimization; and finally, according to a pre-established prediction model, performing prediction and superposition reconstruction on each modal component to obtain a prediction result of the abrupt change type signal of the gas dissolved in oil. According to the method, the VMD decomposition architecture optimized by the frost ice algorithm is introduced, the non-stationarity of the signal is quantified and obviously reduced through the chaos entropy index, higher prediction precision and robustness of the gas signal in the mutation type oil are realized, and the method has obvious innovativeness and superiority in the field of transformer fault trend prediction.

The invention discloses a wind field rapid prediction method and system based on optimized Latin hypercube sampling and POD-BPNN, and the method comprises the steps: carrying out Latin hypercube sampling to generate an initial sample point set, introducing a sensitivity weight, and carrying out the iterative optimization of sample distribution through a greedy strategy; assembling the CFD numerical simulation flow field data of all sample points in the sample point set into a flow field snapshot matrix, and determining a dynamic multi-target truncation order and a corresponding POD mode and coefficient; building and training a BPNN model, packaging the trained BPNN model, the POD modal matrix, the mean field and the flow field reconstruction logic into an FMU module, obtaining the FMU module which can be called in a cross-platform manner, and achieving the real-time prediction of a wind field. The invention relates to the technical field of wind field prediction, significantly improves the precision and calculation efficiency of wind field prediction, and provides an efficient and accurate solution for the rapid prediction of a wind field.

The invention belongs to the technical field of additive manufacturing, and particularly relates to a multi-scale parallel optimization method for a lightweight structure based on lattice discrete optimization. According to the method, through multi-scale parallel optimization design, a macro structure and a micro lattice structure are described through geometric parameters of a movable deformation rod piece, an improved two-value coding parameterization method, called a BCP method for short, is adopted to solve discrete optimization of the micro lattice structure, and geometric parameters of an optimization result are easy to extract.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention provides a method for solving signal drift of an oxygen sensor in a high-temperature and high-pressure environment, which comprises the following steps: collecting acid-base gas concentration data and zirconium oxide material surface state information in the high-temperature and high-pressure environment, and classifying erosion sensitivity degrees by adopting a support vector machine algorithm to obtain an initial response characteristic deviation value; a neural network model is adopted to process the determined potential signal drift trend and oxygen content detection historical data, a simulation response characteristic curve is generated, and a quantitative mapping relation of the pH value change to erosion sensitivity is obtained; refining the correlation between the determined long-term monitoring reliability index and the pH value change by adopting a neural network model, generating a dynamic response characteristic prediction sequence, and obtaining a signal drift suppression threshold set; and through fusion of the obtained signal drift suppression threshold set and the oxygen content detection real-time signal, if it is judged that erosion sensitivity is aggravated in a prediction sequence, a compensation model is activated to update and circulate, and optimized sensor stable output is obtained.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

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 provides a liquid crystal display lightening parameter self-adaptive calibration method, and relates to the technical field of image display. The method comprises the following steps: firstly, establishing a characteristic matrix and a parameter library of liquid crystal molecules, and dynamically correcting according to a real-time working state; and based on the characteristic matrix and the parameter library, executing pixel-level forward prediction on the input image, and generating a color error distribution diagram by solving a coupling solution of a liquid crystal molecule dynamic response differential equation and a backlight attenuation compensation equation. And taking the error distribution diagram as input, obtaining a gradient optimization direction of the calibration parameter mapping diagram through back propagation calculation, and iteratively approaching the optimal pixel value compensation amount in each frame period. And injecting a test pulse into the characteristic pixel point, capturing a voltage response curve through a sampling circuit, and converting the voltage response curve into a real-time correction value mapping graph. And finally, carrying out weighted fusion on the real-time correction value mapping graph and the pixel value compensation amount to generate a calibration parameter mapping graph. The self-adaptive calibration of the display parameters is improved, and the display consistency and the color accuracy are effectively improved.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.