95 results about "Residual correction" patented technology

The invention discloses an efficient optical flow estimation method and device based on Mama, and the method comprises the steps: carrying out the normalization and size alignment of two adjacent frames of images, and extracting the dense features of a fixed down-sampling rate through a shared weight convolution encoder; the two-frame features are sent to a multi-level feature enhancement module, an intra-frame modeling unit and a cross-frame interaction unit are cascaded and matched with channel reforming and residual error correction, and enhanced features are obtained; constructing a four-dimensional cost body on a low resolution, performing probability normalization along a target coordinate dimension, weighting a target coordinate grid according to a probability to obtain a corresponding coordinate, and subtracting the corresponding coordinate from a source coordinate to obtain an initial optical flow; and carrying out attention-guided space fusion on the initial optical flow and context and local correlation, sending the fused optical flow to a differential Mama-based autoregressive refinement module, carrying out iterative updating according to a small number of fixed steps, recovering to a target resolution through convex combination up-sampling, and outputting a final optical flow. According to the method, the optical flow field can be accurately estimated under the conditions of low complexity and low time delay.

The invention discloses a tunnel or mine water gushing space-time prediction method and system coupled with a hydrodynamic numerical model, and the method comprises the steps: outputting multi-source data based on an identified and verified underground water numerical model, complementing the missing of measured data, quantifying the difference between the permeability characteristics of a fault and a normal stratum, and coupling the difference to a data system, and tunnel or mine excavation space data are merged. And constructing an LSTM-isolated forest-K neighbor regression coupling model, and configuring a multifunctional module to realize multi-scene data co-training. The preprocessed multivariate time series data is divided into a training set and a test set, hidden features are extracted through a coupling model, anomaly detection results are fused, a residual error correction model is synchronously trained, and hyper-parameters and weights are adaptively optimized according to multi-engineering prediction error feedback. And based on the trained coupling model, carrying out synchronous water gushing space-time prediction by adopting a window rolling strategy, and outputting prediction data meeting engineering precision in combination with residual correction. And reliable technical support is provided for safety prevention and control of engineering construction.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention discloses an industrial process online prediction model construction method based on self-attention manifold learning, which is suitable for measuring phase or pulse characteristics, and is characterized in that an initial data manifold is constructed on the basis of an information entropy weight theory, and a self-attention mechanism and a residual error correction algorithm are introduced; and synchronous optimization of weight distribution and embedding mapping is realized through loop iteration, so that a self-adaptive multi-entropy weight manifold embedding prediction model capable of deeply mining data time sequence characteristics is constructed. The effectiveness of the method is verified through a preferable embodiment, specifically, the method is applied to processing pulse nuclear magnetic resonance relaxation spectrum data, and real-time and high-precision online prediction of the wax content in a crude oil sample is successfully achieved. According to the method, the prediction precision and robustness are remarkably improved.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The invention discloses a flood peak enhanced physical base flow and residual error correction collaborative runoff prediction method, which belongs to the field of hydrological prediction, and comprises the following steps of: dividing a training set and a verification set according to a proportion, performing oversampling processing on flood peak samples, and constructing a time sequence window; a Xinanjiang model is discretized and expressed by adopting an ordinary differential equation, rainfall and potential evaporation data are input, and intermediate variables are obtained. A physical base flow and residual error correction dual-channel module is constructed, and physical base flow and residual error correction is calculated through two full-connection networks. And calculating a final runoff predicted value by adopting a residual connection structure, taking basic NSE loss as a core, superposing a flood peak sample error weighted item, strengthening flood peak fitting precision, and updating physical parameters and neural network weight through a back propagation algorithm. And verifying the model, and respectively calculating prediction indexes of the training set and the verification set. According to the method, fusion of a traditional hydrological model and a deep learning method is realized, the physical interpretation of the model is enhanced, and the basin runoff prediction precision is improved.

The invention discloses a significant wave height two-stage space-time prediction method and system based on diffusion residual correction, and relates to the technical field of sea wave prediction, historical significant wave height data and corresponding historical wind field data are input into a significant wave height space-time prediction model for processing, and a preliminary significant wave height prediction result is obtained; and inputting the initial prediction error, historical significant wave height data and synchronous forecast wind field data into a residual error correction module based on a diffusion model, and compensating the initial prediction error to obtain a final significant wave height prediction result. A diffusion model based on an EDM framework is innovatively introduced to carry out refined reconstruction on a prediction residual error, and a basic prediction field, a synchronous wind field dynamic factor and a prediction aging code are used as multi-dimensional physical strong conditions to be injected into a diffusion generation process. According to the method, high-frequency texture details and non-linear fluctuation components missed by the first-stage model can be certainly recovered, long-term deviation accumulation is relieved to a certain extent, the high-wave region prediction quality is improved, and the prediction stability is improved.

The invention relates to the technical field of image pattern recognition, in particular to a plate defect detection system based on edge calculation, which comprises a convolution kernel directivity screening module, a convolution kernel separable reconstruction module, a model structured constraint fine tuning module and an edge end parallel reasoning module. According to the method, calculation cores sensitive to specific directional defects are automatically screened, the accuracy of recognizing key features such as scratches is enhanced, complex detection operation is decomposed into two simplified vector convolution and one residual error correction calculation, the design greatly compresses the operation complexity and parameter quantity, and the detection accuracy is improved. By means of a parallel computing architecture of an edge end, separated computing tasks are synchronously processed, results are combined, efficient reasoning is achieved on equipment with limited computing resources, real-time on-site detection of plate defects is achieved, delay caused by a data remote transmission center server is effectively avoided, and the detection accuracy is improved. And the response speed and deployment flexibility of the whole detection process are improved.

The invention discloses a deep learning method for uniform background light and shadow based on a Transform model. The method comprises the steps that an input image and a corresponding portrait main body mask are acquired, and the input image comprises an area with uneven background light and shadow; preprocessing the input image and the portrait main body mask, including image standardization and feature fusion, to obtain a fused feature map; based on a patch segmentation method, converting the fusion feature map into sequence features; encoding the sequence features to output enhanced sequence features, modeling a light and shadow distribution dependency relationship of full image pixels through a global attention mechanism, and distinguishing portrait main body features and background features based on the portrait main body mask; and based on the inverse logic of patch segmentation, recovering the enhanced sequence features into a spatial feature graph and the like. According to the method, semantic mask prior, global context modeling and adaptive residual correction are organically integrated, and a solution is provided for solving the core problem in background light and shadow homogenization.

The invention provides a metal processing shape-property-process database construction method based on macro-micro coupling and simulation-test residual error correction. The method comprises the following steps: determining a plurality of groups of initial process parameter combinations; performing initial forming simulation and initial processing test on the metal material to obtain an initial residual error sample, and performing initial training on the residual error estimation model; evaluating the residual level of the simulation result under each initial process parameter combination so as to add a plurality of additional process parameter combinations; carrying out additional forming simulation and additional processing test on the metal material based on the additional process parameter combination so as to obtain an additional residual error sample and carrying out additional training on the residual error estimation model; and a residual error estimation model subjected to additional training is used for correcting a forming simulation result under the encryption process parameter combination, and a metal machining shape-property-process database is established. According to the technical scheme, high-precision simulation data reflecting the macro-micro coupling forming process of the metal material can be established.

The invention belongs to the technical field of intersection of inertial navigation and deep learning, and discloses a missing data interpolation method of an inertial measurement unit, which comprises the following steps: constructing a baseline interpolation sequence based on an original IMU observation sequence with missing, and observing by taking the difference between the two as a residual error; constructing missing perception information based on an original IMU observation sequence, and splicing the missing perception information with residual observation to form an enhanced feature vector; constructing a parallel bidirectional LNN branch and a bidirectional LSTM branch which are respectively used for extracting an enhanced feature vector to obtain a double-path residual prediction item and generating a hidden state; and taking the hidden state, uncertainty estimation and an original IMU observation sequence as input, calculating an adaptive fusion weight and fusing a double-path residual prediction item to obtain a residual correction item, and superposing the residual correction item to a baseline interpolation sequence to output an interpolation result. The invention also discloses a missing data interpolation device of the inertial measurement unit. When IMU missing data is processed, the interpolation physical consistency can be enhanced, the perception capability is improved, and the interpolation precision is effectively improved.

The invention relates to the field of electric power engineering, and particularly discloses a port shore power load double-flow LSTM prediction method, which comprises the following steps: S1, selecting and collecting factors influencing power load data by using longitudinal data to obtain a multivariable time sequence data set D1, and preprocessing the data set D1 to obtain a data set D2; s2, correlation analysis is carried out, and dominant features and auxiliary features are divided based on the correlation degree of each variable and the load in the MIC quantitative data set D2; respectively inputting the dominant features and the auxiliary features into heterogeneous LSTM branch modeling, and splicing output results to generate a fusion feature map; and S3, constructing a BO-LSTM neural network, inputting the fusion feature map into a double-flow time sequence learning module, extracting dominant features and auxiliary features, performing deep representation, performing splicing, introducing a channel attention mechanism to perform weighting processing on fusion feature vectors, and outputting a power load prediction value through a residual error correction module. According to the method, the prediction precision and robustness are remarkably improved, and the real-time scheduling of the port shore power system is supported.

The invention relates to a hydropower cluster power generation power prediction method based on a residual hybrid model, and the method comprises the following steps: firstly, collecting the historical power data of a hydropower cluster, and carrying out the periodic coding, and forming a time feature set; historical power data is subjected to lagging processing, a lagging power feature set is constructed, the lagging power feature set and a time feature set are combined into a comprehensive feature set, and then the comprehensive feature set is divided into a training set and a test set. Training a basic prediction model by using the comprehensive feature set to obtain basic prediction results of the training set and the test set; and calculating a residual error based on a prediction result of the training set, constructing a residual error data training set, and training a residual error correction model according to the residual error data training set. And finally, performing residual error prediction on the test set through the residual error correction model, and adding a residual error prediction result and a basic prediction result to obtain a final prediction value. According to the method, the prediction precision and robustness are effectively improved through residual hybrid modeling.

The invention discloses a stress detection method and device for a pipeline and a medium, and relates to the technical field of structural health monitoring, and the method comprises the steps: collecting the geometric dimensions and material parameters of the pipeline, building a digital twin model, completing the zero calibration and residual strain baseline setting, and obtaining a parameter baseline set; performing synchronization, noise filtering and restoration on the multi-source observation data by using the parameter baseline set to generate a data frame with a quality mark; the initial calculation stress is obtained based on temperature compensation and residual correction, the initial calculation stress is compared with a digital twin model to recognize the load and disturbance, corrected stress is obtained, the corrected stress and the initial calculation stress are subjected to fusion estimation, and a fusion stress result and uncertainty are output; risk indexes are calculated and graded according to the stress anomaly and the acoustic emission event, residual life and maintenance suggestions are given, and an audit record is formed. According to the invention, synchronization, noise filtering and deletion repairing are carried out on the multi-source observation data based on the parameter baseline set, so that high reliability of stress detection data and improvement of anti-interference capability are realized.

The invention relates to the technical field of machine vision and optical measurement mechanics, and discloses a deformation identification and measurement method based on machine vision and deep learning, and the method comprises the following steps: S1, system calibration and base library construction; s2, offline training of the hybrid model; s3, real-time decoupling and coefficient regression are carried out; and S4, final assembly and physical quantity calculation of the deformation field. According to the method, a deformation field is decoupled into a global nonlinear deformation field () and a residual deformation field () through S3, and the global nonlinear deformation field () and the residual deformation field () are superposed. The physical significance and the stability of a deformation main body are ensured by utilizing the physical deformation base library constructed in S1 and model reconstruction; meanwhile, a luminosity residual image () generated in the S3 is used for driving module reconstruction, local high-frequency disturbance which is not covered by a physical model is accurately compensated, a high-dimensional deformation field is decoupled into low-dimensional global physical prior regression and residual correction, and efficient real-time measurement is achieved; meanwhile, numerical difference is replaced by analytic derivation, so that the signal-to-noise ratio and the measurement precision of the strain field are remarkably improved.

The application discloses a tunnel or mine gushing water space-time prediction method and system coupled with a water power numerical model, and the method comprises the following steps: based on the identified and verified underground water numerical model, outputting multi-source data, complementing the missing measured data, quantifying the difference between fault and normal stratum permeability characteristics and coupling to the data system, and incorporating the tunnel or mine excavation space data. An LSTM-isolation forest-K nearest neighbor regression coupled model is constructed, and a multifunctional module is configured to realize common training of multi-scene data. The pretreated multivariate time series data is divided into a training set and a test set, hidden features are extracted through the coupled model, abnormal detection results are fused, a residual correction model is trained synchronously, and the hyperparameters and weights are adaptively optimized according to the multi-project prediction error feedback. Based on the trained coupled model, a window rolling strategy is adopted to carry out synchronous gushing water space-time prediction, residual correction is combined, and prediction data meeting the engineering precision is output. Reliable technical support is provided for engineering construction safety control.

This invention belongs to the fields of new energy technology and artificial intelligence technology, and provides a high-resolution long-term photovoltaic power prediction method and system based on a dual-branch architecture. By constructing a parallel dual-branch architecture, the baseline branch extracts multi-scale features, and the ramp branch identifies power ramping events. The features from both branches are embedded through multiple channels and then fed into a BiGRU-Enhanced Transformer model with shared weights. By strategically deploying BiGRU layers at the input and between the encoder and decoder, local instantaneous dependencies and global long-term trends are captured simultaneously. A multi-task joint optimization loss function is designed for the ramp branch, using weighted cross-entropy and... L The probability, temporal location, and fluctuation amplitude of slope occurrence are jointly optimized using the L1 norm. Finally, nonlinear residual correction is applied to the outputs of the two branches to achieve the final prediction. This invention effectively overcomes the oversmoothing effect of deep learning models, demonstrating extremely high capture accuracy and robustness in long-term prediction of one month's worth of data at 1-minute resolution.

The embodiment of the invention discloses a projector lens distortion residual error correction method and system in a fringe projection contour, and the method comprises the steps: firstly confirming a lens distortion model of a projector, and carrying out the preliminary correction of an original distortion projector coordinate, and obtaining a preliminary correction projector coordinate with a distortion residual error; measuring a plane object by using a calibrated fringe projection contour measurement system, calculating a pixel point weight based on a three-dimensional coordinate point corresponding to a projector coordinate subjected to preliminary correction, and fitting an ideal plane by using a weighted least square method to obtain an ideal plane parameter; three-dimensional coordinate points on the ideal plane and corresponding ideal projector image coordinates are determined according to the ideal plane parameters and the projector projection relation, distortion residual errors are calculated, and a distortion residual error distribution diagram is generated; and finally, in combination with the distortion residual distribution diagram and a target compensation strategy, performing compensation processing on the distortion residual of the projector lens, generating corrected projector coordinates, and realizing high-precision lens distortion correction.

The invention relates to the technical field of artificial intelligence model optimization, in particular to a large model reasoning acceleration method and device for packet sensing quantization and residual correction, and the method comprises the steps: carrying out the statistical analysis of the weight and activation of each layer of a large model, and generating a channel feature matrix; constructing a learnable grouping mapping matrix, dividing channels into different groups, and dynamically distributing quantized bit width to obtain a grouping weight matrix; on the basis of the channel weight-activation joint sensitivity, calculating each grouping error contribution on line by using a small prediction model, and adjusting a grouping weight matrix to generate an optimized weight matrix; constructing an error control matrix to dynamically adjust the quantization error along the propagation path, and generating a correction matrix; dynamically adjusting the sparse rate and the quantization precision according to the channel feature matrix and the hardware constraint by combining a structured sparse strategy, and generating a sparse quantization matrix; in the reasoning process, model reasoning is carried out according to the correction matrix and the sparse quantization matrix, and the weight matrix and the correction matrix are updated and optimized in a closed-loop mode.

The application relates to the technical field of data processing, and discloses an application control method considering data collaborative prediction and adaptive correction, which collaboratively predicts the data amount of a prediction period through a baseline prediction model and a residual correction model, can significantly improve the data amount prediction accuracy, and then determines the deviation feature of a target prediction period according to the predicted data amount of the target prediction period and the actual data amount of the target prediction period, so that the correction decision basis can be obtained, the deviation scene adapted by the current prediction period can be accurately identified based on the deviation feature, and the first prediction sequence is adaptively corrected according to the deviation scene, so that the second prediction sequence is obtained. It can be seen that the layered correction mechanism not only avoids frequent global correction, prevents overcorrection or undercorrection, but also ensures that the actual prediction scene change condition can be quickly responded, has strong real-time adaptive capacity, and thus the control accuracy can be effectively improved.

The application discloses an effective wave height double-stage space-time prediction method and system based on diffusion residual correction, relates to the technical field of sea wave prediction, and inputs historical effective wave height data and corresponding historical wind field data into an effective wave height space-time prediction model for processing to obtain a preliminary effective wave height prediction result, and then inputs the preliminary effective wave height prediction result, the historical effective wave height data and synchronous forecast wind field data into a residual correction module based on a diffusion model to compensate for the preliminary prediction error and obtain a final effective wave height prediction result. The diffusion model based on the EDM architecture is innovatively introduced to finely reconstruct the prediction residual, the basic prediction field, the synchronous wind field dynamic factor and the prediction time limit are taken as multi-dimensional physical strong conditions to inject the diffusion generation process, the application can deterministically restore the high-frequency texture details and nonlinear wave components missed by the first-stage model, to a certain extent, the application can relieve the long-term deviation accumulation, improve the prediction quality in high wave areas and improve the prediction stability.

This invention discloses a method for designing the composition of nickel-containing high-strength material systems based on transfer learning and the barrel theory optimization. The method includes: first, using a recursive feature elimination algorithm to screen key features across source domain benchmark data and target domain experimental data containing sparse components; second, constructing a deep neural network for pre-training on the source domain dataset, extracting nonlinear feature representations between components and performance through hidden layers; third, freezing the weights of the feature extraction layers of the pre-trained model, and for sparse samples in the target domain, introducing Bayesian ridge regression to construct a residual correction model to achieve cross-domain knowledge transfer; and finally, combining multiple metallurgical constraint criteria to perform an evolutionary search within a limited composition space. This invention solves the problem of effectively modeling sparse data in new material systems. By combining deep feature extraction with Bayesian residual correction, it significantly improves the robustness of high-strength material performance prediction and enables precise optimization of industrial-grade composition formulations.

The invention belongs to the technical field of semiconductor manufacturing, and provides a wafer test offset detection method and system and a storage medium, and the method comprises the steps: collecting key vector data in a wafer test process, the key vector data comprises a thermal load vector, a system state vector and a real-time observation vector, and carrying out the arrangement to obtain a multi-modal data set, establishing a hybrid prediction model combining a physical simulation model and a residual error correction model, predicting an actual thermal displacement prediction vector by adopting the hybrid prediction model based on the multi-modal data set, judging whether a thermal displacement mismatch risk exists or not, if so, triggering offset root cause identification, establishing a historical knowledge base, and if not, identifying the offset root cause. The method comprises the steps of identifying thermal displacement mismatch root causes of wafer testing based on a multi-modal data set and a historical knowledge base, generating a sequence root cause list, generating a mismatch compensation strategy for the thermal displacement mismatch root causes in sequence according to the obtained sequence root cause list, and compensating a thermal displacement mismatch risk.

The present invention relates to the technical field of oil and gas geophysical exploration, and discloses a method, device, and electronic device for residual static correction of time-varying dynamic targets. The method for residual static correction of time-varying dynamic targets includes: denoising; obtaining the statistical effect DDi21 of the signal-to-noise ratio of the stacked seismic data body A1 along the layer corresponding to Di, and simultaneously obtaining the statistical effect DDi22 of the signal-to-noise ratio of the pre-stack gather B1 along the layer corresponding to Di; defining a time window for time-varying residual static correction; and performing dynamic cross-correlation calculation between the optimized model trace of the stacked seismic data body A1 and the pre-stack gather B1 to obtain different residual correction time differences for all reflection layers in the CMP gather or CRP gather. This technical solution can be performed on the basis of the existing residual static correction effect, and can further improve the signal-to-noise ratio of the data, thereby improving the overall imaging effect of the data. Of course, this method can also be used directly to perform residual static correction after conventional static correction; and the residual correction error can also be reduced.

The invention belongs to the technical field of SOC prediction, and particularly relates to an SOC prediction method based on ARIMA power prediction and residual correction. The method comprises the following steps: acquiring historical power data to form a power time sequence; building an ARIMA power prediction model based on the power time sequence, and predicting the power; performing residual error correction on the predicted power to obtain corrected predicted power; and constructing an SOC dynamic prediction model, and predicting a future value of the SOC based on the corrected prediction power. A residual error correction mechanism can adaptively counteract systematic errors of a linear model, historical residual error information is fully considered, and power prediction is more accurate; when the SOC dynamic prediction model is constructed, factors such as the capacity, the charging and discharging efficiency and the sampling interval of the energy storage system are comprehensively considered on the basis of the corrected prediction power, and the actual operation condition of the energy storage system can be better reflected.

The invention provides a salinization type quantitative inversion method based on feature weighting and residual error correction, and relates to the field of remote sensing technology and machine learning, and the method comprises the steps: collecting actual measurement concentration data of soil salt ions in a target region, and obtaining a remote sensing image of a corresponding region; extracting feature information data from the remote sensing image; constructing a salinization type quantitative inversion data set through the feature information data and the measured data; evaluating the correlation between each feature and the soil salinity by using a feature selection method to obtain a feature importance weight vector; the first stage is used for preliminary prediction of the soil ion concentration, the second stage is used for introducing a residual learning mechanism, the first stage residual error is fitted to improve the prediction precision, and ion concentration data of the target area are predicted and generated; and according to an inversion result, obtaining a two-dimensional space-time distribution diagram of soil salinization type quantitative classification. The technical scheme of the invention is suitable for high-precision identification and classification management of regional scale soil salinization types.

The application discloses a kind of based on contrast learning's weld pipe unit abnormality detection method, to solve the time scale drift of multiple sensing channels, cross-station alignment and the lack of physical consistency, transient interference causes detection instability and inaccurate positioning problem, the present application is through the time scale correction, time to cumulative length mapping and encoder boundary constraint, neural residual correction and alignment confidence evaluation, fixed length grid constructs physical consistent contrast sample, memory bank and dynamic threshold detection and causal online fine-tuning rollback, realizes cross-station unified alignment and station level positioning, improves the technical effects of abnormality detection accuracy and robustness and maintains online stability.

The invention discloses a facility damage identification and evolution prediction system based on physical information generation, and belongs to the technical field of structural health monitoring and digital twinning. In order to solve the problems that in the prior art, a data-driven model depends on a large amount of real labeled data, and a physical model is difficult to capture a nonlinear effect and lacks self-adaptive ability, the system comprises a physical information generation module for generating a synthetic data set based on the physical model to train an artificial intelligence model; the real-time state diagnosis module is used for receiving a real observation image and diagnosing a current internal state field; and the double-flow evolution prediction module is used for generating prediction of the physical flow and the data flow at the next moment in parallel based on the current state. According to the method, the problem of data sparsity is solved through physical information generation, prediction accuracy is improved through dynamic residual correction, long-term self-adaption of the model is achieved through a closed-loop self-evolution mechanism, and robustness and interpretability of the system are enhanced.

The application provides a robot control method and an electronic device, wherein the method comprises: obtaining a current robot body state through a generative middleware, performing geometric residual parameterization on an original motion trajectory to obtain a linear interpolation baseline, and predicting and recovering residuals by using a flow matching model. According to the deviation between the current body state and the last control cycle correction trajectory, adaptive time scale planning is performed to dynamically determine the time domain length and the re-planning interval, and then the key frame trajectory is corrected in combination with the baseline, the residual and the time parameter and is executed by a physical tracking module. By introducing the adaptive time scale planning and the generative residual correction mechanism, the dynamic response speed, the motion fluency and the control robustness of the robot in a complex environment are significantly improved, and efficient collaborative control from high-level planning to low-level execution is realized.

The invention discloses a long-term-rental apartment pricing method, device and equipment. The method comprises the following steps: acquiring multi-source data; based on the multi-source data and a random forest model, determining an initial price of the long-term-rental apartment; obtaining a market feedback result based on the initial pricing of the long-term-rental apartment; and performing residual error correction on the random forest model based on a market feedback result to obtain an optimized random forest model, performing pricing of the long-term-rental apartment based on the optimized random forest model, and outputting a target pricing of the long-term-rental apartment. According to the invention, the pricing accuracy and effect of the long-term-rental apartment can be improved.