25 results about "Autologistic regression" patented technology

The invention is suitable for the technical field of geographic element data analysis, and provides a geographic spatiotemporal weighted vector autoregression-based multi-element collaborative change modeling method, which comprises the following steps of: constructing a GTWR-VAR model, and combining a multivariable feedback mechanism of vector autoregression with locality of geographic spatiotemporal weighted regression to obtain a GTWR-VAR model; the method comprises the following steps of: quantifying spatial-temporal heterogeneity of mutual influence among geographic elements, introducing a Bayesian optimization mechanism, adaptively determining an optimal lag order, a spatial bandwidth and a time bandwidth by taking verification set mean square error minimization as a target, and finally adopting Monte Carlo block permutation test to determine the optimal lag order, the spatial bandwidth and the time bandwidth on the premise of retaining inherent time autocorrelation of data. And evaluating and eliminating non-significant items in the local coefficient to ensure the statistical effectiveness of the result. According to the method, the continuous change rule of the influence mechanism between the geographic elements in time and space is disclosed, and a more accurate and reliable basis is provided for regional response research under fine management, prediction and climate change.

The invention belongs to the technical field of computers, and discloses a GUI (Graphical User Interface) positioning method and system based on a routing prediction framework. According to the method, a route prediction mechanism based on lexical item type judgment is introduced in the autoregression decoding process of the multi-modal large language model, and an interface semantic generation task and a spatial positioning task are dynamically shunted, so that a user instruction with a target element can jump out of a discrete text generation path; continuous space coordinate regression is directly carried out based on a hidden state and structure enhanced interface image features, and a null positioning result is returned through a special refusal response lexical item when a target element does not exist, so that the coordinate quantization error and illusion positioning problems are effectively avoided under an end-to-end unified framework, the positioning reasoning delay is remarkably reduced, and the positioning accuracy is improved. Precision, real-time performance and robustness of graphical user interface element positioning are improved, and the overall understanding ability of the model for interface structure semantics and spatial layout relation is enhanced.

The application provides a source-sink landscape-based soil heavy metal remediation method and system, and relates to the field of ecological environment; the method generates target region small watershed space data by analyzing digital elevation model through GIS; on the other hand, the source-sink landscape type of the target region is determined according to satellite images and remote sensing images, and the pollution source intensity is determined by combining rainfall runoff and soil environment sampling monitoring to establish a source-sink landscape geographic information database; soil heavy metal monitoring and spatial evaluation are carried out by selecting typical landscape types, and a self-regression model is established by spatial autocorrelation analysis of soil heavy metal spatial distribution information and source-sink landscape types, and the best scheme of landscape space optimization is proposed based on the model; based on the optimal scheme, the landscape space pattern most conducive to the soil heavy metal self-repairing process is obtained; finally, physical, chemical and biological treatments are further carried out for different landscape types, and the source-sink landscape-based soil heavy metal remediation application is completed.

A method for estimating a covariance with respect to a plurality of bonds is provided. The method includes: receiving historical bond market returns data; using a first algorithm based on an Auto-Regressive-Moving-Average (ARMA) model to calculate ARMA model regression errors based on the historical bond market data; using a second algorithm based on a logarithmic Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model to calculate an estimated volatility vector based on the ARMA model regression errors; using the ARMA model regression errors and the calculated volatility vector to estimate a time-varying covariance matrix of the ARMA model regression errors with respect to the historical bond market data; using the estimated time-varying covariance matrix of the ARMA model regression errors and the calculated volatility vector to estimate a time-varying covariance matrix of the bond returns; and using the estimated time-varying covariance matrix to calculate a set of predicted hedge ratios.

A method for differential diagnosis of gallbladder adenoma and cholesterol polyps based on the fusion of deep learning and clinical features in ultrasound images includes: performing deep learning on ultrasound images using a deep learning network to output an adenoma / polyp risk score; the deep learning network is selected from one of VGG, ResNet, DenseNet, GoogLeNet, Vision Transformer, and Inception; the adenoma / polyp risk score and clinical features are input into a trained machine learning model for learning to obtain the final probability of identifying the polyp as a gallbladder adenoma rather than a cholesterol polyp; the clinical features include: echo intensity, maximum polyp diameter, and age; the machine learning model is selected from one of Logistic Regression, Gradient Boosting, K-nearest neighbors, Gaussian naïve Bayes, AdaBoost, Random Forest, ExtraTrees, and SVM.

The application discloses a sequence data timespectrum radiation unification processing method and system under reference data constraints, and the method comprises the following steps: acquiring a remote sensing image sequence and generating a multi-source radiation normalization image database to form a multi-source reference sequence data pair; a reconstruction self-similarity normalization pre-training model is constructed, the reconstruction self-similarity normalization pre-training model comprises a decomposition and reconstruction module, a self-similarity weight matrix module and a variational normalization module, the decomposition and reconstruction module obtains a final timespectrum feature sequence, and the self-similarity weight matrix module obtains a timespectrum self-similarity weight matrix; the variational normalization module processes to form timespectrum radiation consistent sequence data; and corresponding timespectrum radiation consistent sequence data is obtained based on the reconstruction self-similarity normalization model. The application constructs a variational model framework of timespectrum normalization, cooperates self-similarity weight, timespectrum self-regression and similarity, realizes timespectrum radiation normalization of a timespectrum sequence cube, and solves timeserial remote sensing data which is accurate in trend and faithful in spectrum.

The invention relates to an autoregression image generation method based on semantic detail decoupling and local information enhancement. The method comprises a VQ-GAN feature learning process based on semantic detail decoupling and an autoregression generation process based on local context lexical elements. In the feature learning process, a stage type and alternating type double training strategy is adopted, firstly, a small-size semantic codebook is trained to learn an image core semantic structure and construct the basic reconstruction capability, then a large-size detail codebook is introduced, and through the layering process of semantic quantification, residual calculation and detail quantification and alternating training of three iteration rounds and one period, the image core semantic structure can be learned. And semantic-detail hierarchical double-codebook functional differentiation and collaborative optimization are realized. In the autoregression generation process, a local window and a dynamic cache mechanism are introduced to extract preorder local context lexical elements, and conditional guidance of local information is enhanced; and finally, combining an adaptive progressive classifier-free guidance strategy to dynamically adjust the guidance intensity of the reasoning stage, and balancing the semantic consistency and the degree of freedom of details.

The invention discloses a financial market mobility toughness network deconstruction and vulnerability traceability method, system and device and a computer readable storage medium, and relates to the technical field of metering economy and financial risk early warning. The method comprises the following steps: acquiring standardized fluidity indexes and external fluidity impact indexes of multiple sub-markets; constructing a quantile time-varying parameter vector autoregression model, and estimating quantile impulse response under multiple quantiles; based on the pulse response, respectively calculating two kinds of toughness indexes, namely, the flowability impact absorption strength and the absorption duration of the financial market; further, quantile regression is combined with skewed t distribution mapping to obtain conditional distribution and in-danger growth of mobility and toughness acceleration; and realizing vulnerability traceability by adopting marginal contribution decomposition of orthogonalization residual errors, and outputting time-varying risk source weights and alarms. The technology can be stably operated under the extreme descending state and the prosperous state, the state dependence of toughness and cross-market asymmetric overflow can be depicted, and quantitative early warning and source tracking of the mobility toughness lower tail risk are achieved.

The invention provides a line icing thickness prediction fusion method based on a two-way parallel network. The method comprises the steps that a multi-dimensional data sequence is acquired, and a neural network prediction model predicts an empirical thickness prediction curve of icing thickness change conditions in a future time period according to the multi-dimensional data sequence; a thickness observation sequence is obtained, the regression prediction model carries out iterative calculation according to the thickness observation sequence to obtain the current potential state of the current moment, the regression prediction model carries out multiple times of autoregression iterative calculation based on the current potential state, and a mechanism thickness prediction curve of a future time period is predicted; a plurality of prediction features are obtained, the nonlinear element model generates nonlinear weights based on the prediction features, and the empirical thickness prediction curve and the mechanism thickness prediction curve are multiplied by the corresponding nonlinear weights and are added so as to be fused into a final prediction curve. According to the method, the prediction results of the two branches can be complementarily corrected, the disturbance of complex environmental factors to the prediction process is reduced, and the accuracy of the final prediction curve is improved.

The invention discloses a multi-scale adaptive time sequence prediction method and system based on multi-expert cooperation, and belongs to the technical field of time sequence data analysis. The method comprises the following steps: firstly, dividing a time sequence data sample into a plurality of normalized time sequence blocks with equal length; secondly, obtaining a time sequence prediction result for the normalized time sequence block by using a hybrid multi-expert self-adaptive predictor; and finally, reconstructing the hybrid multi-expert adaptive predictor as an auto-regression iterative residual network architecture, so as to extract complementary multi-scale information in the time sequence representation to improve the time sequence prediction performance. The method is realized through a multi-scale adaptive time sequence prediction system, and comprises a normalized time sequence partitioning module, a mixed multi-expert adaptive prediction module and an iterative residual error autoregression learning module. According to the method, the limitation of a fixed-length prediction head can be broken through through iterative residual error autoregression learning, the problem that a shared linear prediction layer is insufficient in capturing a heterogeneous time sequence mode is solved through hybrid expert prediction, and a high-precision and high-flexibility cold load adaptive time sequence prediction solution is provided for realizing intelligent environmental control and energy-saving operation.

The invention relates to the technical field of dam safety monitoring, and discloses an arch dam monitoring data abnormal value real-time diagnosis method, device and equipment and a medium, and the method comprises the steps: data preprocessing: carrying out the preprocessing of collected arch dam monitoring data and exogenous variables; model construction: constructing a Bayesian dynamic linear model based on the preprocessed data, wherein the Bayesian dynamic linear model comprises a trend component, a season component, a regression component and an autoregression component; parameter estimation: initializing an expectation maximization algorithm based on a subspace method, and estimating model parameters of the Bayesian dynamic linear model in combination with a Kalman smoother; and anomaly detection: dynamically calculating a threshold value based on log-likelihood difference of historical data, and diagnosing an abnormal value of arch dam monitoring data in real time through a Bayesian dynamic linear model in combination with secondary inspection. The method is suitable for online anomaly detection and restoration of monitoring data such as dam displacement, osmotic pressure, stress and temperature.

The invention discloses a real-time prediction method for low-storage environment monitoring time sequence flow data, and belongs to the field of environment monitoring and pollution early warning, and the method comprises the steps: S1, collecting environment monitoring data, carrying out the preprocessing, dividing the data into independent batches, and taking the target pollutant concentration as a response variable; s2, constructing an autoregression error model, integrating regression and autoregression parameters, and configuring optimization parameters; s3, initializing parameters based on a renewable estimation framework, calculating historical summary statistics, alternately updating the parameters, synchronously screening key factors, determining a time sequence dependence order, performing loop optimization and releasing an original data memory; and S4, outputting the optimal parameter verification performance and predicting the concentration. By the adoption of the method, full-amount original data does not need to be reserved, real-time model updating is achieved through the current data and historical key statistical information, the storage pressure is reduced, the prediction precision is improved, and the requirements for environment monitoring and pollution real-time early warning are effectively met.

The invention discloses an AAOAO process dynamic optimization method and system fusing asymmetric space and an autoregression model, and a medium, and belongs to the technical field of water treatment process optimization, and the method comprises the steps: forming a panel data set of time T * individual N based on the historical operation data of each individual; constructing an asymmetric space weight matrix W, and describing a relation between main flow conduction and backflow conduction between individuals; and based on the independent variable, the target variable and the spatial weight matrix, constructing a fixed-effect two-factor spatial autoregression model. Updating a regression coefficient of the fixed effect two-factor spatial autoregression model based on the dynamic time window and the step length; and according to the absolute value of the regression coefficient and the significance ranking optimization priority, combining with the process safety experience boundary, and quantitatively adjusting the independent variable corresponding to the regression coefficient. According to the method, the problems of target variable distortion, neglect of space conduction effect, large parameter estimation deviation, static model problem and the like in the existing AAOAO process optimization technology are solved, and the method has relatively good practicability.

The invention relates to the technical field of carbon emission monitoring, and provides a decomposition regression fused carbon emission monitoring method and system, electronic equipment and a medium, and the method comprises the steps: obtaining real-time power load data, carrying out the empirical mode decomposition, and removing an air conditioner load to obtain a residual IMF component; reconstructing the residual IMF component and the residual term, and outputting a real-time residual power load sequence after the influence of the air conditioner is eliminated; obtaining an electricity-carbon correlation coefficient of a previous time period and an exogenous variable value in a set historical time period, and inputting the electricity-carbon correlation coefficient and the exogenous variable value into the constructed autoregressive moving average regression model for calculation to obtain a current electricity-carbon correlation coefficient; and utilizing the real-time residual power load sequence and the current electricity-carbon correlation coefficient to estimate and obtain a near-real-time terminal fuel carbon emission estimation value of the target area as a monitoring result. According to the method, through power load data reconstruction and machine learning modeling, the regional carbon emission estimation precision is improved under the small sample condition.

The invention relates to a method and system for efficiently evaluating the reliability of a wind power plant system based on a graph neural network, and the method comprises the steps: modeling the uncertainty and spatial correlation of a wind speed through Weibull distribution and a t-Copula function, and capturing time correlation in combination with a vector autoregression model, so as to generate a wind speed sequence with time-space correlation; adopting a non-sequential Monte Carlo method to sample a wind power plant output state and an element fault scene, and constructing a minimum output reduction mixed integer linear programming model considering standby cable switching; a graph neural network based on double-graph topology is introduced, a mapping relation between a fault scene and output reduction is established through cooperative training of a complete set edge supervision graph and a closed edge propagation graph, and rapid regression of EWOL and edge level classification of branch switch states are realized; and balancing the precision of the graph-level task and the edge-level task through a weighted joint loss function. Compared with the prior art, the method has the advantages of efficiently estimating the average loss power of the sample and the like.

An ensemble time series prediction system that makes predictions based on observed data. The disclosed ensemble time series prediction system may leverage different types of datasets and information from different resources for making predictions. The disclosed ensemble time series prediction system may extract time dependent features from autoregressive time dependent data, embedding features from sparse datasets, continuous features from continuous dataset, and time lagged features from data that include time-lag information. The disclosed ensemble time series prediction system may then consolidate the features extracted from the different types of datasets and generate a set of consolidated input features for training a neural network, which may include a recurrent neural unit that finds sequential pattern for the sequence of input features and a regression unit that performs regression and predictions. The ensemble time series prediction system may output a set of outputs that include predicted values and associated confidence intervals.

This invention discloses a flood forecasting method and system based on bias correction and physical guidance, using an expert stacking ensemble. It acquires a multi-mode state dataset based on a hydrophysical model and proposes a comprehensive evaluation index that considers both the accuracy of the Nash coefficient and the peak percentage threshold as an optimization objective, enhancing the ability to capture high-flow events. A terminal error correction mechanism based on source regression and weighted least squares is proposed, and the flow attenuation coefficient is used to suppress the excessive dominance of flood peak on weight estimation. Heterogeneous base learners are integrated, and a physical guidance expert stacking ensemble method is proposed to identify the dynamic weights of the models under different hydrological scenarios. The SHAP interpretability analysis method is introduced to quantify the predictive contributions of base learners and state variables. This highly diverse and heterogeneous model stacking framework not only enhances the complementarity between base learners but also achieves good synergy between accuracy, stability, and interpretability, providing a new paradigm for intelligent hydrological forecasting that combines high performance and transparent decision support.

A gallbladder polyp-like lesion benign and malignant prediction method based on ultrasonic image deep learning and clinical feature fusion comprises the following steps: performing deep learning on an ultrasonic image through a deep learning network, and outputting a malignant polyp risk score; the deep learning network is selected from one of a VGG (Vector Graphics Generation), a ResNet, a DenseNet, a GoogLeNet and a Vision Transform; inputting the risk score of the malignant polyp and the clinical features into a trained machine learning model for learning to obtain the final probability of benign and malignant gallbladder polyp-like lesions; the clinical characteristics comprise CA19-9, CEA and the maximum diameter of polyp; and the machine learning model is selected from one of Logistic regression, LightGBM, Random Forest, XGBoost, ExtraTrees, NaiveBayes and SVM (Support Vector Machine), and the machine learning model is selected from one of the Logistic regression, the LightGBM, the Random Forest, the XGBoost, the ExtraTrees, the NaiveBayes and the SVM.

The application is suitable for the technical field of geographic element data analysis, and provides a kind of geographic space-time weighted vector autoregressive multi-element collaborative change modeling method, comprising: constructing GTWR-VAR model, combining the multivariate feedback mechanism of vector autoregression with the locality of geographic space-time weighted regression, for quantifying the space-time heterogeneity of mutual influence between geographic elements, then introducing the Bayesian optimization mechanism, to verify the minimum mean square error minimization as the goal, adaptively determine the optimal lag order, spatial bandwidth and time bandwidth, finally using Monte Carlo block replacement test, under the premise of preserving the inherent time autocorrelation of data, evaluate and eliminate the insignificant items in local coefficient, ensure the statistical validity of the result. The application discloses the continuous change rule of influence mechanism between geographic elements in time and space, and provides more accurate and reliable basis for fine management, prediction and regional response research under climate change.

The application relates to the technical field of intelligent decision-making, and discloses an agricultural irrigation intelligent decision-making system based on artificial intelligence. The system constructs a collaborative architecture of a field perception layer, an edge computing layer, a cloud decision-making layer and an irrigation execution layer, forms an irrigation task context through multi-source perception data; on the basis, a multi-round proxy search decision mechanism driven by meta-reinforcement learning is introduced, the iteration optimization of a decision-making process is realized in combination with explicit self-reflection; at the same time, a reward evaluation model of preference perception is constructed, multi-objective trade-off modeling is realized through latent variable mirror image constraint and inverse self-regression flow transformation, and a global optimization of a strategy is carried out in combination with a cross-round income attribution mechanism, so that an irrigation scheduling scheme meeting water saving, crop water demand and energy consumption constraints is generated. The application can improve the accuracy, stability and self-adaptive ability of irrigation decision-making.

This application relates to a method and apparatus for reconstructing sound velocity profiles. The method includes: acquiring geographic location information, topographic information, and time information of a currently collected target sea area; inputting the geographic location information, topographic information, and time information as input data into a pre-constructed sound velocity profile prediction model, whereby the sound velocity profile prediction model predicts the sound velocity profile based on the input data to obtain a corresponding predicted sound velocity profile; wherein the sound velocity profile prediction model is constructed based on a nonlinear regression model and an autoregressive moving average model. The method of this application fully considers time-varying factors when predicting sound velocity profiles, thereby making the factors considered when predicting the sound velocity profile of a target sea area more comprehensive, which effectively improves the accuracy of the sound velocity profile prediction results.

The application discloses a multi-output Gaussian process regression underwater vehicle system identification algorithm based on a deep partial self-encoder, and comprises the following steps: designing an adjustable amplitude sawtooth vertical rudder input signal, acquiring state data of an underwater vehicle and dividing a training set and a verification set; constructing an autoregressive model based on underwater vehicle dynamics according to the state data; building a deep partial self-encoder according to the autoregressive model; taking low-dimensional feature data extracted by the deep partial self-encoder as input of a multi-output Gaussian process, taking output of the autoregressive model as output of the multi-output Gaussian process, searching hyperparameters of the multi-output Gaussian process through a particle swarm algorithm, and training the model; when a model training error reaches preset precision, the model training is completed, and the model is verified by using verification set data; and the problem that high-dimensional input of the multi-output Gaussian process increases calculation of a kernel function and searching time of the hyperparameters, increases complexity of the model and reduces prediction efficiency of the multi-output Gaussian process is solved.

The application belongs to the technical field of computers and discloses a GUI positioning method and system based on a routing prediction framework. The application introduces a routing prediction mechanism based on word type discrimination in the autoregressive decoding process of a multi-modal large language model, dynamically shunts the interface semantic generation task and the spatial positioning task, enables the user instruction with the target element to jump out of the discrete text generation path, directly performs continuous spatial coordinate regression based on the hidden state and the interface image features enhanced by the structure, and returns an empty positioning result through a special rejection response word when the target element does not exist, thereby effectively avoiding the coordinate quantization error and the illusion positioning problem under the end-to-end unified framework, significantly reducing the positioning reasoning delay, improving the accuracy, real-time performance and robustness of the graphical user interface element positioning, and enhancing the overall understanding ability of the model to the interface structure semantics and spatial layout relationship.

The embodiment of the invention provides a carbon quota processing method and device based on risk early warning, a medium and equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring various historical carbon quota data of a target enterprise, predicting a carbon quota value at a preset future moment through a pre-trained carbon quota prediction model based on the data, and calculating a carbon quota value fluctuation ratio through a generalized autoregression condition heterovariance model so as to determine a risk early warning signal of the carbon quota, and finally, based on the carbon quota value and the risk early warning signal, carrying out carbon quota processing. Wherein the carbon quota prediction model is obtained by training an Adaboost model in combination with a classification regression tree (CART) based on a carbon quota value parameter in historical carbon quota data. By means of the method, powerful support is provided for the carbon quota processing mode of the enterprise, and it is ensured that the carbon quota processing mode can accurately meet the supply and demand requirements of carbon emission of the power enterprise.

The invention relates to a space-time diagram network traffic flow prediction method based on adaptive memory enhancement, and belongs to the technical field of traffic flow prediction. The method comprises the following steps: firstly, constructing a shortest path adjacency matrix based on breadth-first search (BFS) to represent traffic network topology; converting the original data into multi-dimensional representation through a data embedding layer; a space-time Transform block is adopted to extract space-time dependence features; an adaptive memory enhancement module (AMA) is introduced, and a long-term traffic mode is dynamically stored and called through a gating index moving average mechanism (GEMA); designing a dual-channel diffusion diagram fusion module to realize multi-view spatial topology modeling; and finally, outputting a multi-step prediction result through an autoregressive attention layer (ARA) and a regression layer. According to the method, the defects of an existing method in the aspects of long-term dependence on modeling and spatial topology representation are effectively overcome. The overall method achieves optimal balance in the aspects of precision and efficiency, and provides an efficient and accurate technical solution for an intelligent traffic system.