203 results about "Uncertainty estimation" patented technology

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention relates to the technical field of agent path planning, in particular to a multi-agent collision-free path planning method based on a fusion DQN algorithm. The multi-agent collision-free path planning method comprises the following steps: firstly, constructing a two-dimensional grid map as an environment, and carrying out feature extraction by utilizing a CNN (Convolutional Neural Network); then, behavior clone learning is carried out through the expert model to obtain a BC model; the core innovation lies in that a BC model and a CNNDQN model are fused, an adaptive strategy learning framework is constructed, and intelligent dynamic combination of expert experience and reinforcement learning exploration is realized by adopting uncertainty estimation, antagonistic knowledge distillation and performance perception sampling technologies; and finally, further processing an initial path output by the fusion model by a CBS algorithm, and completing multi-agent collision-free path planning. According to the method, the accuracy and efficiency of path planning are optimized through a mixed learning strategy.

The invention relates to an intelligent measurement and control optimization method and device for dynamic parameter adaptive calibration, equipment and a medium. The method comprises the following steps: executing invariance causal test based on an external environment context to obtain a cross-environment invariant explanatory variable subset, and performing anti-fact simulation on each parameter in the cross-environment invariant explanatory variable subset to generate a causal attribution report; based on an under-excitation parameter subset and a cross-environment invariant explanatory variable subset in the baseline recognizable atlas, performing safe active excitation planning on the parameters to obtain a safe micro-perturbation excitation plan; and based on the residual error, the uncertainty estimation, the delay cross-correlation feature, the hysteresis loop area feature, the causal contribution score and the excitation-response fragment, performing calibration estimation on the target parameter by using a hierarchical estimator, and generating a calibration packet according to a calibration estimation result. By adopting the method, self-adaptive calibration of intelligent measurement and control dynamic parameters can be realized through residual attribution and cross-environment invariance test in combination with safe perturbation excitation.

The invention provides an AI-driven equipment health state assessment method and system, and relates to the technical field of intelligent operation and maintenance. The method comprises the following steps: acquiring equipment operation data, and performing time and dimension unification and quality control to form a multi-source operation data set and an environment context; generating an initial state feature based on the mechanism feature library, and obtaining a general representation through self-supervised pre-training; executing calibration learning by using a preset health label, and establishing a fusion evaluation model containing time sequence consistency and physical boundary constraint; carrying out distribution alignment and uncertainty estimation on the basis of scene differences to obtain alignment characterization and credibility scores so as to optimize a model gating strategy; performing joint mapping on the new data, outputting health index, fault probability and residual life estimation, and generating a root cause clue; lightweight online updating is executed under drifting detection, health indexes and root cause clues are written back to a mechanism feature library, early warning levels and maintenance suggestions are generated, and therefore complete-cycle intelligent sensing and self-adaptive optimization of the equipment state are achieved.

The invention discloses a hotel room type matching method and device, and the method comprises the steps: obtaining the original information of a newly-added hotel and room type, carrying out the preprocessing of the original information, and generating a standardized hotel and room type feature data set; constructing a multi-level feature vector based on the standardized feature data set; screening and generating a candidate matching set in combination with an inverted index and a locality sensitive hashing algorithm; a matching score matrix is calculated through fusion of the rule matching model, the machine learning model and the deep learning model; generating a final matching conclusion through Bayesian uncertainty estimation and threshold dynamic adjustment; and optimizing the matching model based on the matching conclusion and the business feedback data. Through multi-dimensional feature extraction, multi-model fusion matching and confidence evaluation, the problem of accuracy of hotel room type cross-supplier matching is solved, and the matching efficiency and robustness are improved.

In various examples, systems and methods for uncertainty estimation for object detection in autonomous and semi-autonomous systems and applications are provided. The systems and methods may use data from one or more sensors (e.g., camera(s) and / or LiDAR sensor(s) to generate a representation of features surrounding a machine. A model may be used to generate probabilities of objects being present in the representation of features and uncertainty estimates corresponding to the object presence probabilities. The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and / or highlight areas where object detections may have been missed. The systems and methods may also be used to auto-label scenes associated with the representation of features, and the auto-labeled scenes may be used for training purposes.

The invention provides an end-to-end automatic driving behavior decision-making method and system considering uncertainty estimation, and the method comprises the steps: inputting image information into a convolutional neural network, carrying out the feature extraction, obtaining the state information of surrounding traffic participants, carrying out the feature fusion of the state information of a vehicle, and inputting the fusion features into a full connection layer; constructing an integrated quantile network to obtain complete uncertainty estimation of an agent decision result, wherein the complete uncertainty estimation comprises data uncertainty estimation based on an implicit quantile network and model uncertainty estimation based on a multi-model integrated network considering prior Bayesian estimation; evaluating the confidence coefficient of the agent decision result, if the confidence coefficient is high, outputting the optimal driving action of the vehicle predicted by the integrated quantile network, and if the confidence coefficient is low, switching to the braking strategy output; and a control module of the end-to-end automatic driving decision model agent is further input, and a control signal is output to enable the vehicle to execute different control actions. According to the invention, the reliability and credibility of end-to-end automatic driving behavior decision are improved.

The invention relates to the technical field of ship energy consumption prediction, in particular to a ship main engine energy consumption prediction method and device based on uncertainty estimation, and the method comprises the steps: receiving historical navigation data of a target ship, dividing the historical navigation data into a training set and a test set, and carrying out the standardization processing; training the base model by using the training set and generating a residual sample; taking a prediction result of the base model as a new input feature, and training a linear regression meta-model to construct a Stacking integrated model; calculating quantiles according to the residual samples and constructing a prediction interval; calling an MAPIE library to automatically realize a CV + conformal prediction process, and automatically optimizing hyper-parameters of the base model by using Optuna to obtain an optimal model parameter combination; and outputting the energy consumption predicted value of each target ship and the corresponding confidence interval. According to the method, the stability and reliability of the ship energy consumption prediction result can be improved, quantitative expression of the confidence coefficient of the prediction result in an energy efficiency management system is realized, and the practicability and decision support capability of the method in scenes such as intelligent shipping scheduling are enhanced.

A short-term scheduling rule extraction method for a water-optical complementary system comprises the following steps: correcting an existing business prediction result by adopting a probability prediction model based on historical observation data and business prediction data of runoff and optical power; generating multiple groups of future possible runoff and optical power scenes by adopting a data sampling method based on a commercial prediction result corrected by the probability prediction model in the step 1; constructing a water-light complementary peak regulation optimization scheduling model; 3, constructing a Bayesian gated loop network which can consider the commercial prediction result and the model parameter uncertainty, introducing a probability recalibration mechanism which can effectively relieve the uncertainty estimation deviation of the Bayesian gated loop network, taking the optimal scheduling solution set obtained in the step 3 as the basis, extracting a complementary system scheduling rule which considers the multiple uncertainties, and finally obtaining the optimal scheduling solution set. And the peak regulation scheduling of the water-light complementary system is guided by using a complementary system scheduling rule. The comprehensive benefits of the multi-energy complementary system can be improved.

The invention discloses a semi-supervised bladder tumor medical image segmentation method based on supervised branches and uncertainty estimation, and relates to the technical field of bladder tumor medical image segmentation. Comprising the following steps: S1, acquiring data; s2, data preprocessing; s3, data division; s4, constructing a model; s5, performing model training; and S6, carrying out segmentation identification. The invention provides a new semi-supervised task method, which introduces a supervision branch, combines a pseudo tag and uncertainty estimation based on information entropy, uses an average teacher model as a bottom layer architecture, and provides a supervision signal through a prediction result of a supervision branch network. Therefore, the bladder tumor segmentation model is supervised to generate a more accurate segmentation result.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

A control system and a method for estimating a risk exposure of an automated driving system (ADS) of a vehicle. Obtaining an actuation capability of the vehicle, wherein the obtained actuation capability includes an uncertainty estimation for the actuation capability. Obtaining a location of free-space areas in the surrounding environment of the vehicle, wherein the obtained location of free-space areas comprises an uncertainty estimation for the estimated location of free-space areas. Forming a risk map of the surrounding environment of the vehicle based on the obtained actuation capability and the obtained location of free-space areas, wherein the risk map includes a risk parameter for each of a plurality of area segments included in the surrounding environment of the vehicle. Determining a total risk value of the ADS based on the risk parameters of a set of area segments intersected by at least one planned path of the ADS.

The invention provides a medical image cell segmentation method based on progressive pseudo-label optimization in order to solve the problem of learning deviation caused by unreliable pseudo-label learning and the limitation that a fixed-form pseudo-label cannot provide reliable cell morphological characteristics in an existing weak supervision method. The method comprises the following specific steps: 1) utilizing weak supervision point labeling information, generating two initial pseudo labels with complementarity through a clustering algorithm and a superpixel segmentation algorithm, and respectively guiding a training process of a double-branch network; 2) utilizing a dynamic threshold and watershed algorithm to realize growth and boundary division of a cell region in the pseudo tag, so that the pseudo tag is gradually close to the real form of a cell; 3) designing a bidirectional cross supervision mechanism, and realizing knowledge migration and collaborative optimization through high-confidence prediction results of the two branch networks; and 4) uncertainty estimation and a difficult sample attention loss function are designed, the feature learning ability of the network to difficult samples with low contrast, fuzzy boundary and the like is enhanced, and the precision of weak supervision cell segmentation is effectively improved.

A method for training an artificial intelligence (AI) model includes receiving a training dataset and utilizing processing circuitry to train a Bayesian Neural Network (BNN) based on the dataset and a selected training algorithm including backpropagation. The method estimates aleatoric and epistemic uncertainty for each sample in the dataset. Based on these uncertainty estimates, weights are assigned to the samples, prioritizing those with lower aleatoric uncertainty. A conditioned training dataset is generated by increasing the weights of these prioritized samples. The AI model is then trained using this conditioned dataset. Finally, the pre-trained AI model outputs a prediction. This approach improves model performance by focusing on data with more predictable characteristics, reducing bias and enhancing prediction reliability.

The invention discloses a dynamic target tracking method based on LBP (Local Binary Pattern) features and semantic features, and the method can accurately predict and reduce a first search area through the calculation of the offset of a target in an inter-frame image, the introduction of a motion amplification coefficient, uncertainty estimation and other technologies, and reduces the interference of background information on the target while reducing the calculation amount. According to the method, the target features are extracted through ResNet-50, the target features are subjected to dimensionality reduction processing while the discrimination capability and robustness of the target features are guaranteed, and the problems that the calculation amount is too large and the complexity is high are effectively avoided. Through accurate positioning and reduction of a first search area where a target is located and robust feature extraction and cooperative interaction, the ability of continuously and stably tracking the target under the complex conditions that target movement is difficult to fit, the illumination condition is complex, the target is shielded and the like is enhanced.

The invention discloses a human mobility prediction and uncertainty estimation method and system based on a sparse spatio-temporal trajectory, and the method comprises the steps: obtaining a spatio-temporal trajectory, and carrying out the double-graph convolution embedding and global timestamp embedding of the spatio-temporal trajectory, and obtaining a spatio-temporal vector; for the discrete time trajectory, predicting a place of the discrete time trajectory at the next moment based on a sequence encoder of an autocorrelation mechanism, evaluating a place prediction effect based on an evaluation index, and optimizing a discrete time trajectory prediction model; for the continuous time track, the location and time of the next time-space point of the continuous time track are predicted based on a relative time self-attention sequence encoder, the location prediction effect is evaluated based on an evaluation index, the accuracy of time is measured based on a mean square error, and a continuous time track prediction model is optimized; and for an uncertain trajectory, defining the data uncertainty as trajectory entropy based on the spatio-temporal trajectory, determining a trajectory entropy coordination loss function based on the trajectory entropy, and estimating the uncertainty of the model.

The invention provides a dynamic sparse observation-oriented deep neural process ocean data assimilation method, and relates to the field of ocean data processing, and the method specifically comprises the following steps: constructing a training data set; simulating actually observed non-uniform and uncertain characteristics through Gaussian nuclear diffusion; building an ocean assimilation network oriented to sparse dynamic observation, outputting an analysis field and estimating uncertainty; and performing end-to-end training on the ocean assimilation network model by taking the reanalysis true value field as a supervision signal, and optimizing network parameters by combining a minimum error term and a structure constraint term. And after training is completed, inputting the background field in the test stage and sparse observation into the ocean assimilation network model for reasoning to obtain an ocean state reconstruction field conforming to the actual physical quantity scale. According to the technical scheme, the problem that in the prior art, calculation feasibility, cross-scale correlation modeling and credible uncertainty output cannot be considered under the real conditions of sparse observation and dynamic change of spatial-temporal distribution is solved.

Systems and methods are provided for subsurface characterization from seismic data. The system can receive a plurality of candidate velocity and anisotropic parameter models and seismic gather data. A subset of the plurality of candidate velocity and anisotropic parameter models can be selected to form a training data set. The system can generate a joint probability functions of depth differences and seismic semblances based on the training data set and generate a likelihood function based on the joint probability function. A Bayesian model can be defined using the likelihood function and the prior probability functions. The system can draw a plurality of samples from a posterior distribution of the Bayesian model using Markov Chain Monte Carlo sampling methods and calculate depth uncertainty values using the plurality of samples.

The present invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on uncertainty estimation. The method is as follows: input the T1, T1c, T2, and Flair images of the brain tumor into a trained image segmentation model, which includes 4 encoder modules, 1 decoder module, 1 Monte Carlo simulation module, and 1 image processing module; the Monte Carlo module repeatedly calls the encoder module and the decoder module for sampling; the image processing module processes the output to obtain a mean image, a random uncertainty image, an epistemic uncertainty image, an entropy uncertainty image, and a mutual information uncertainty image, so as to achieve accurate segmentation and analysis of the brain tumor. The present invention improves the accuracy of segmentation and the reliability of diagnosis, has strong scalability, and is applicable to a variety of medical image analysis tasks.

The invention relates to a hyperspectral image classification uncertainty estimation method and system, and the method comprises the steps: firstly constructing a hyperspectral image classification architecture based on a deep neural network, and enabling the architecture to be divided into a cascade structure of a feature extractor and a classifier; secondly, the space correlation Gaussian mixture model is embedded into an output layer, and the output layer based on the space correlation Gaussian mixture model is obtained and used for calculating the output probability; then, solving the space correlation Gaussian mixture model by using a variational Bayesian inference method, and estimating initial variation posteriori distribution; and finally, considering spatial relevance of the hyperspectral image, and improving a variational Bayesian inference method to obtain final variational posteriori distribution, namely uncertainty expression of classified output of the hyperspectral image. According to the method, the spatial correlation Gaussian mixture model is introduced to carry out probabilistic expression on the output feature space of the deep neural network, and the probability expression is used as confidence estimation output by the classification model, so that the credibility of the model is enhanced.

The invention discloses an electronic injection fuel injection strategy intelligent optimization method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source working condition data of an engine, and generating a standard data set; s2, constructing a Bayesian neural network model, and outputting an integrated fuel injection parameter prediction value and an uncertainty estimation value; s3, optimizing model structure parameters and hyper-parameters by using an ant lion optimization algorithm to obtain an optimal structure and parameters; s4, training the model by using the optimal structure and parameters, and performing performance evaluation by using a standard data set; s5, collecting working condition data in real time, and outputting an optimal integrated fuel injection parameter prediction value and an uncertainty estimation value; and S6, periodically collecting feedback data, training the model in combination with the standard data set increment, and re-executing ant lion optimization in good time. According to the method, high-precision intelligent optimization of the oil injection strategy of the engine is achieved, the fuel economy is improved, emission is reduced, and the self-adaptive capacity under the complex working condition is enhanced.

The invention belongs to the cross technical field of artificial intelligence and decision support systems, and discloses an intelligent decision support method based on a dynamic knowledge graph and multi-modal fusion, which comprises the steps of 1, acquiring and preprocessing multi-source heterogeneous original data, 2, constructing and updating the dynamic knowledge graph, carrying out multi-modal fusion, outputting multi-modal fusion features, and 3, carrying out multi-modal fusion on the multi-modal fusion features. The method comprises the following steps of: 1, obtaining a multi-modal fusion feature and a knowledge graph embedding matrix, 2, receiving the multi-modal fusion feature and the knowledge graph embedding matrix, and outputting a candidate decision path set, decision probability distribution and a state value, 4, outputting dual uncertainty of each path by an uncertainty estimation module, 5, calculating a comprehensive reward of each path, 6, carrying out meta reinforcement learning and strategy optimization, and 7, carrying out multi-modal fusion. And step 7, outputting a final decision result, an uncertainty evaluation report and an interpretable reasoning path, and completing the decision. According to the method, the defects in the prior art are effectively overcome, and more accurate, reliable and explainable decision support is provided for a complex scene.

The invention discloses a joint blind denoising method and system based on self-heuristic learning and Bayesian reasoning, belongs to the field of computational imaging, and solves the problems that in the prior art, the mixed noise modeling capability is insufficient, the performance is degraded under the condition of low signal-to-noise ratio, the combination of uncertainty quantization and regularization is lacked, and the generalization capability is limited due to data dependence. Comprising the following steps: collecting an original image and preprocessing; generating a noise data pair; an enhanced residual attention U-Net model is constructed; a noise estimation sub-network is adopted to extract noise features, the noise features are fused with original image features, and the model is trained; adopting the trained model to carry out multiple times of forward propagation on the same input image to obtain multiple groups of denoising results; calculating a mean value and a standard deviation to obtain a de-noising prediction and uncertainty heat map; and training the trained model again based on the uncertainty heat map, optimizing network parameters, and obtaining a final denoising prediction result and uncertainty estimation thereof. The method is suitable for complex noise distribution processing scenes.

The invention discloses a geological disaster hidden danger point distribution prediction method based on neural network integration. The geological disaster hidden danger point distribution prediction method comprises the following steps of S1, acquiring geological environment data and performing preprocessing; s2, constructing and training a heterogeneous model integrated by a graph convolutional neural network and a multi-layer perceptron; s3, combining residual connection and an attention mechanism to enhance the model feature recognition capability, and optimizing model parameters through a cross entropy loss function; s4, evaluating the heterogeneous model by adopting cross validation, calculating precision, mean square error and confidence coefficient, and dynamically distributing integrated weight; s5, weighting the output of the fusion model according to the integrated weight, and introducing Bayesian uncertainty estimation to generate a risk distribution probability graph; s6, quantifying the risk levels of the hidden danger points, mapping the hidden danger points into a two-dimensional spatial distribution map and visualizing the two-dimensional spatial distribution map And S7, screening hidden danger points of which the risk levels are higher than a threshold value, and outputting a prediction result. The invention provides a high-precision and reliable geological disaster hidden danger point prediction method through integrating a neural network and a deep fusion technology.

The invention relates to a model based on multi-modal cross attention and uncertainty integral gradient and application, and relates to the technical field of bioinformatics. The model is obtained based on multi-modal cross attention and uncertainty integral gradient construction, the problems that in an existing drug sensitivity prediction method, multi-modal data fusion is difficult, the model generalization ability is weak, and interpretability is lacked can be solved, the model captures correlation between modals through a cross attention mechanism, and the prediction accuracy of the drug sensitivity is improved. According to the method, uncertainty estimation is realized by combining Monte Carlo Dropout (MC Dropout), feature importance is analyzed by using an integral gradient algorithm, and finally, unification of high-precision prediction and biological interpretability is realized.

The embodiment of the invention provides a model training method and device, a classification method and device, a medium and a program product, and relates to the field of artificial intelligence, and the method comprises the steps: inputting unlabeled data into a target model, so as to enable the unlabeled data to be input into a feature extraction module to obtain the features of the unlabeled data; the features of the unlabeled data are respectively input into the classification module and the uncertainty estimation module to obtain a prediction label of the unlabeled data and a first numerical value used for representing the uncertainty of the unlabeled data; obtaining a first loss of the classification module based on the pseudo label and the prediction label of the unlabeled data; obtaining a second loss of the uncertainty estimation module based on the first numerical value and the first loss; determining a weight corresponding to the first loss based on the first numerical value, and if the first numerical value is larger, the weight corresponding to the first loss is smaller; if the first numerical value is smaller, the weight corresponding to the first loss is larger; a target model is trained based on the first loss, the weight, and the second loss. Therefore, the model training precision can be improved.

The invention discloses a personalized exercise prescription generation method fusing deep learning, learnable similarity and multi-modal time sequence analysis. The method comprises the steps that firstly, static signs and health risk labels of a user and dynamic signals such as the heart rate, the HRV and the step number collected by wearable equipment in real time are integrated, and unified modeling is conducted through a multi-modal encoder to form individual state vectors; then outputting a preliminary motion scheme by a BERT-Transform text generation model, mapping a user to a hidden space taking a historical execution effect as supervision by using a comparative learning network, and accurately inferring an FITT core parameter through approximate nearest neighbor retrieval and effect weighting; performing consistency optimization on texts and parameters through a large language model, completing risk verification under a safety barrier constructed by a medical rule base, ensuring that output meets clinical taboo and individual tolerance, and finally continuously optimizing long-term health income under medical constraints in combination with an offline reinforcement learning strategy, and cooperating with meta-learning cold start and uncertainty estimation, so as to achieve the purpose of improving the safety of the health management system. Safe, accurate, self-adaptive and explainable personalized exercise prescription generation for new and old users is realized, and the method can be widely applied to intelligent fitness, chronic disease exercise intervention and digital health management scenes.

The invention discloses an autism spectrum disorder classification method and system based on evidence decision fusion. The method comprises the following steps: creating an autism multi-modal data set; an autism classification model based on evidence decision fusion is constructed and comprises a data preprocessing module, an evidence extraction module, a category credibility and classification result uncertainty estimation module and a decision fusion module. Training a model by using samples in the autism multi-modal data set; and performing autism classification on a newly input subject sample by using the trained model. According to the method, key evidences of three modes of T1 weighted imaging, diffusion tensor imaging and functional magnetic resonance imaging are comprehensively utilized, and a reliable and credible classification decision framework is constructed according to a Dempster combination rule, so that the accuracy and robustness of autism classification can be improved, overall classification uncertainty evaluation of a final decision can be given, and the accuracy and robustness of autism classification are improved. And better model interpretability and decision credibility are provided.

The invention relates to an air conditioner load prediction method based on a self-adaptive double-flow graph attention network, and belongs to the technical field of building energy conservation and intelligent control. The method comprises the following steps: collecting historical power and environmental data of an air conditioner, and after preprocessing and normalization, constructing an input sequence through a sliding window and dividing a data set according to time; a prediction model is constructed, and a causal graph learning module, a multi-scale graph structure learning module, a self-adaptive space-time attention module, an uncertainty quantization module and a self-adaptive sampling module are integrated; a training set and a joint loss function training model are adopted, and a load prediction result and uncertainty estimation are output through Monte Carlo Dropout during testing. According to the method, the dynamic causal relationship between variables and multi-scale space-time dependence can be adaptively learned, reliable uncertainty quantification is provided while the prediction precision is improved, and the method is suitable for intelligent regulation and control and energy efficiency optimization of the air conditioning system.