130 results about "Domain prediction" patented technology

The invention discloses a confrontation trajectory prediction method, a readable storage medium, electronic equipment and a vehicle, and belongs to the field of automatic driving. The prediction method comprises the following steps: constructing a trajectory prediction model; inputting a historical track, a real track and environment context information; track features are obtained through feature extraction and a space-time attention mechanism; constructing a confrontation model of the explicit space; mapping the track features to a submerged space; disturbance is carried out on the submerged vectors, and the submerged vectors are regularized to specific distribution; and mapping the latent vector to the explicit space, and generating a confrontation trajectory of the target driving vehicle. The readable storage medium stores a program of the method. A program including the method is stored in the electronic equipment. The vehicle comprises the intelligent system for executing the method. According to the method, the explicit space and the implicit space are emphasized for multi-space collaborative optimization, the pertinence and the real concealment of attacks are remarkably improved, the prediction precision is improved, and then the target driving vehicle track can resist surrounding vehicles and cope with complex vehicle conditions.

The invention particularly relates to a time sequence mapping knowledge domain prediction method based on continuous features and a large model. The method comprises the following steps: acquiring mapping knowledge domain data; constructing a knowledge graph prediction model, wherein the knowledge graph prediction model comprises a historical coding module, a continuous time neural network module, a feature fusion module and a training loss module; the historical coding module outputs a historical entity matrix, a historical relationship matrix and a historical matrix based on the knowledge graph data; the continuous time neural network module obtains a time continuous entity matrix and a time continuous relation matrix according to the historical entity matrix and the historical relation matrix; the feature fusion module generates probabilities of all candidate entities based on the time continuous entity matrix and the time continuous relation matrix; and the training loss module calculates final loss and generates probabilities of all final candidate entities so as to predict the time sequence knowledge graph. According to the method, continuous time dynamic modeling and multi-scale feature fusion are realized, and the prediction precision and generalization ability of the time sequence knowledge graph are remarkably improved.

The invention discloses a power electronic device fault early warning method based on multi-physics field coupling, and the method comprises the steps: (1) collecting aging data of a power electronic device under the condition of electric field and thermal field coupling, and dividing the aging data into a source domain data set and a target domain data set; (2) establishing a power electronic device aging parameter screening method, selecting a parameter which can best represent an aging rule as an aging precursor, and converting discrete voltage and current signals into time sequence signals which can be accepted by a neural network; (3) compressing the time sequence signal set into sliding window graph nodes, calculating a distance relationship between the graph nodes, and obtaining a graph matrix containing data structure information; (4) building a space-time fusion fault predictor, wherein the space-time fusion fault predictor comprises a feature extractor, a time sequence predictor and a domain discriminator; and (5) a weak supervision adversarial learning strategy is adopted, a small amount of target domain label data participates in the model training process, and the source domain prediction loss, the target domain prediction loss and the discrimination loss are combined for training.

The invention discloses a remote sensing image cross-scene classification method, device and equipment based on collaborative learning, and the method comprises the steps: training and generating an initial classification model through employing source domain data with noise labeling, predicting unlabeled target domain data based on the initial classification model, generating a target domain pseudo-label, and carrying out the recognition of the target domain pseudo-label through a collaborative learning mechanism. Performing bidirectional knowledge migration on the target domain pseudo-label and the data with the noise source domain, and training the initial classification model to obtain a target domain prediction entropy value; screening a high-confidence pseudo label according to the target domain prediction entropy value, replacing the target domain pseudo label, and returning to the step of executing bidirectional knowledge migration on the target domain pseudo label and the data with the noise source domain through the collaborative learning mechanism until the model is converged to obtain a target classification model; and classifying the to-be-classified remote sensing image by using the target classification model to obtain a classification result. By adopting the method, the accuracy of cross-scene classification of the remote sensing image is improved.

The invention relates to a data set distribution difference-oriented electronic medical record data representation learning method and system, and the method comprises the steps: pre-training a source domain teacher model through a source domain data set of an existing disease, and extracting the health state representation information of a source domain disease patient; training a domain invariant feature extractor as a transition model, modeling general features through an adversarial training strategy, and establishing an independent extraction channel for private features to realize feature alignment among different domains; and migrating parameters of the transition model to a target domain emerging disease prediction model, and performing fine tuning in combination with target domain data to realize accurate prediction of emerging diseases. Through domain invariant feature extraction and model migration optimization, the generalization ability and prediction precision of the model on different data sets are significantly improved, clinical results of patients can be accurately predicted, accurate support is provided for medical decision, and a reliable foundation is laid for the cross-domain prediction problem in medical data analysis.

The invention discloses a vehicle fine granularity detection method based on a three-dimensional grid and YOLOv11 transfer learning. The method mainly comprises three components: a source domain prediction network structure, a target domain prediction network structure and a transfer learning module. Wherein the source domain prediction network and the target domain prediction network are dual-channel deep networks fusing two-dimensional images and three-dimensional grids, and efficient migration of source domain knowledge in a target domain is realized by aligning feature distribution of the source domain and the target domain. Through deep fusion of three-dimensional grid information and two-dimensional image features, the method can maintain robust detection performance under adverse conditions of vehicle attitude change, illumination interference, shielding and the like, can effectively reduce large-scale data annotation and training cost, improves the rapid adaptation capability of the model in a new scene or a new vehicle type, and improves the robustness of the model. And a high-precision, extensible and rapid-iteration fine-grained identification and detection solution is provided for intelligent traffic monitoring, unmanned driving perception, military equipment identification and digital twin systems.

The invention provides an event monitoring model training method and an operation video anomaly detection method, which can be applied to the technical field of video processing. The method comprises the following steps: training a feature extraction module, an event memory unit and a source domain classifier by using a source domain sample video set to obtain a pre-training model; inputting the target domain sample video into a feature extraction module and outputting target domain sample features; inputting the target domain sample features and the source domain sample features into a scene memory unit and an event memory unit, and respectively outputting mixed domain event enhancement features and scene enhancement features; inputting the mixed domain event enhancement feature, the target domain sample feature and the source domain sample feature into a target domain classifier, and outputting a target domain prediction result; and iteratively adjusting the event feature prototype, the scene feature prototype and the network parameters according to the target domain prediction result and the pseudo tag until a first iteration stop condition is met, and obtaining a trained event monitoring model.

The invention discloses an aircraft body surface acoustic load time domain prediction method and system, and belongs to the technical field of aircraft detection. In order to solve the problem of high-precision solving calculation of the sound scattering problem of the aircraft, an aircraft flow field calculation grid and a sound field grid are established; the grids are initialized; calculating a Jacobian determinant and grid cell measurement, and completing grid coordinate system conversion; judging the convergence of the numerical solution of the grid unit; a Runge-Kutta method is adopted to iteratively solve a linear Euler equation under each time step; obtaining flow field data and sound field data of each grid unit; filtering processing is carried out by adopting a spatial filtering format, numerical pseudo waves are eliminated, and flow field data and sound field data of each grid unit after filtering processing are obtained; and performing time updating iteration on the flow field data and the sound field data until the obtained acoustic load data is stable, and outputting aircraft surface acoustic load time domain data, a watershed sound field cloud picture and a flow field cloud picture. The method gives consideration to calculation efficiency and precision.

The invention provides a turbine engine residual life prediction method based on cross-domain migration. The method comprises the following steps: training a residual life original prediction model by using source domain training data; the residual life original prediction model is composed of a multi-scale feature extraction module and a prediction module, and the multi-scale feature extraction module comprises a Transform network, a CNN network and a feature coupling unit; performing cross-domain learning on the pre-training prediction model by using the target domain data to obtain a residual life target prediction model corresponding to the target domain; and acquiring operation data corresponding to a to-be-predicted turbine engine, and inputting the operation data into the residual life target prediction model for residual life prediction to obtain the residual service life corresponding to the to-be-predicted turbine engine. Through the method and the device, the problem of performance reduction caused by data distribution difference in cross-domain prediction is effectively relieved, and accurate prediction of the residual life result of the target domain is realized.

The invention provides a multi-source domain invariant acoustic feature extraction method and system for an equipment operation state, and belongs to the technical field of equipment maintenance, and the method comprises the steps: constructing a multi-source domain invariant acoustic feature extraction network based on a DANN model, and the network comprises a feature extractor, a classifier, a domain discriminator and a multi-domain acoustic feature class boundary constraint module; the feature extractor extracts high-dimensional features of sound signals, the classifier carries out fault mode recognition, and the domain discriminator realizes domain prediction. The multi-domain constraint module generates an embedding space, and calculates the maximum mean value difference, the local maximum mean value difference and the Euclidean distance among different source domain features. The network constructs a loss function by taking minimization of inter-domain difference and classification loss and maximization of inter-domain distance and domain discrimination loss as targets, and carries out adversarial training through multi-source tagged acoustic data. According to the method, domain invariant features are extracted by using adversarial learning and hidden space constraint alignment of multi-source domain acoustic features, so that the influence of feature offset under a cross-working-condition condition is effectively reduced, and the fault mode recognition accuracy is improved.

The invention discloses a whole blood donation adverse reaction risk prediction method and system, and relates to the technical field of medical informatization and intelligent risk prediction. The prediction method comprises the following steps: performing data import and integration on demographic information, blood donation history and adverse reaction records of blood donors; performing preprocessing and variable definition on the data; the method comprises the following steps: respectively establishing a plurality of machine learning models by taking a serious blood donation adverse reaction as a main outcome variable and an adverse reaction type as a secondary outcome variable, screening an optimal main outcome variable prediction model and an optimal secondary outcome variable prediction model through a plurality of evaluation indexes, and verifying the models by adopting a cross validation method. And finally, realizing risk prediction and hierarchical management of blood donation adverse reactions by using the selected optimal model. According to the method, layered identification and multi-model training of the primary and secondary result variables can be realized, the accuracy and intelligent level of blood donation adverse reaction prediction are effectively improved, and the method is suitable for risk prevention and control and management of large-scale blood donation crowds.

The invention discloses an enzyme kinetic parameter and domain label prediction method and device, equipment and a medium, and relates to the technical field of enzymes, and the method comprises the steps: training an initial enzyme kinetic parameter prediction model comprising a feature extraction sub-model and a domain adversarial neural network through employing an intra-domain data set and an extra-domain test set, obtaining a target enzyme kinetic parameter prediction model; inputting the target reaction substrate and the target enzyme sequence into a target enzyme kinetic parameter prediction model so that the target enzyme kinetic parameter prediction model performs enzyme kinetic parameter prediction to map to a corresponding enzyme kinetic parameter prediction value, and performing domain tag mapping to obtain a corresponding domain prediction tag, outputting an enzyme kinetic parameter prediction value and a domain prediction tag corresponding to the target reaction substrate and the target enzyme sequence. Through a special training process, the characteristics of a substrate and an enzyme sequence can be extracted more accurately, so that the prediction accuracy of enzyme kinetic parameters and domain tags is improved.

A defect detection method and a model transfer method. The model transfer method comprises: acquiring a source-domain image and a corresponding annotation, and a baseline model obtained from a source domain; acquiring target-domain images, which are fully annotated, partially annotated, or unannotated; inputting the annotation of the source-domain image into a denoiser to obtain a denoised annotation; inputting the unannotated target-domain images into an initial baseline model to obtain second target-domain prediction results, performing data augmentation on the unannotated target-domain images to obtain second target-domain augmented images, and using the second target-domain prediction results as pseudo-annotations of the second target-domain augmented images; and using the source-domain image and the corresponding denoised annotation, the annotated target-domain images and the corresponding annotations, and / or the second target-domain augmented images and the corresponding pseudo-annotations to train the baseline model, so as to obtain a transfer model. The method can complete training by using a small number of annotated target-domain images, thereby solving the problem of model transfer performance being poor in the case of insufficient target-domain images.

A decoder for decoding a digital time-varying signal from a data stream is presented. The decoder is configured to decode the digital time-varying signal from the data stream in non- overlapping temporal blocks by decoding each of transform-coded temporal blocks of the non-overlapping temporal blocks of the digital time-varying signal by predicting the respective transform-coded temporal block using a selected prediction mode out of a set of prediction modes to obtain a prediction signal, decoding coefficients from the data stream, the coefficients representing a prediction residual signal of the respective transform-coded temporal block in a transform domain, subjecting the coefficients to a predetermined re-transformation from the transform domain to time domain to obtain a time-domain prediction residual signal representing a prediction residual signal of the respective transform-coded temporal block in a time domain, and correcting the prediction signal using the time-domain prediction residual signal, wherein the predetermined re-transformation is a non-overlapping transform.

The invention relates to a prediction modeling method of a multi-source migration adaptive enhancement network, which comprises the following steps of: source domain modeling: after samples of a multi-source domain and a target domain are respectively subjected to deep migration learning based on confrontation, extracting consistency information of the multi-source domain, and inputting the information into a domain discriminator to distinguish whether data is from the source domain or the target domain; inputting the source domain features into a nonlinear regression device to complete a source domain prediction task; simultaneously training an adversarial-based deep transfer learning network, a domain discriminator and a nonlinear regression device based on a domain discrimination error and a regression prediction error, and stopping training when the domain discriminator cannot correctly divide received data into source domain features or target domain features and the nonlinear regression device can predict source domain data; and target domain prediction: performing prediction by using the prediction network trained by the source domain and inputting new information of the target domain, performing transfer learning on data of each source domain, constructing a multi-source transfer prediction model, and better performing accurate prediction by using low-value density data.

The invention relates to a self-adaptive cross-domain battery pack health state probability prediction method, and belongs to the technical field of battery health management. The method comprises the following steps: S1, data preprocessing and trajectory normalization; s2, carrying out adaptive step length difference modeling based on local curvature; s3, multi-modal noise modeling; and S4, cross-domain migration probability prediction based on GMM-UT. Quantizing the nonlinear characteristics of the degradation track through local curvature, and dynamically adjusting the difference step length so as to accurately extract the degradation characteristics; a Gaussian mixture model is adopted to fit multi-modal noise, and accurate quantification of uncertainty is achieved; multi-modal noise statistical characteristics and unscented transformation are fused, cross-domain migration and uncertainty propagation are optimized, and battery pack degradation track multi-modal probability distribution is output. According to the method, the defects that an existing method is poor in fixed step length adaptability, inaccurate in noise modeling and insufficient in cross-domain prediction robustness are overcome, the health prediction precision and reliability of the battery pack are improved, and the method is suitable for health management of the battery pack under complex working conditions.

The invention discloses a quality parameter detection model construction method based on multi-source domain adaptation, and relates to the technical field of spectral analysis, the method comprises the following steps: constructing a quality parameter detection basic model, and constructing a multi-source spectral data set for training the quality parameter detection basic model; during model training, extracting source domain features and domain invariant features of the spectral data, and respectively predicting to obtain a source domain prediction result and a domain invariant prediction result; dynamically adjusting the contribution weight of each source domain training set to model training based on the performance of the domain invariant prediction result on each source domain training set; and determining a loss function of model training based on the domain invariant feature, the source domain prediction result and the domain invariant prediction result in combination with the contribution weight, and training by using the loss function to obtain a quality parameter detection model. According to the method, the problem of multi-source spectrum cross-domain regression prediction can be effectively solved under the condition of no target domain spectral data, and the robustness and accuracy of quality parameter detection are improved.

This invention provides a medical ultrasound image recognition method based on generator domain alignment, relating to the field of image classification technology. The method includes: creating labeled generated medical ultrasound image samples for fine-tuning using a conditional diffusion model, where labels characterize the category corresponding to the generated medical ultrasound image sample; aligning each labeled generated medical ultrasound image sample to the generator domain using an unconditional diffusion model of the generator domain, obtaining a generator domain medical ultrasound image set; fine-tuning the source domain model using the generator domain medical ultrasound image set, obtaining a generator domain model; aligning a target medical ultrasound image to the generator domain using the unconditional diffusion model of the generator domain, obtaining a generator domain target medical ultrasound image; and inputting the generator domain target medical ultrasound image into the generator domain model to obtain a first predicted classification result for the target medical ultrasound image. This transforms a cross-domain task into an intra-domain prediction task, aligning both the source domain model and the target data to the same generator domain, thereby achieving accurate predicted classification results.

The invention discloses a multi-industry load joint prediction method based on time-frequency alignment, and solves the problems of large error and low reliability of a load prediction result in the prior art, and the method comprises the steps: obtaining historical load data of a to-be-predicted industry and corresponding external data; carrying out preprocessing and normalization processing on the data; extracting frequency domain information of the load data based on short-time Fourier transform; according to the time domain information and the frequency domain information of the load data, a multi-industry load time domain prediction model and a multi-industry load frequency domain prediction model are constructed based on an LSTM neural network; and on the basis of time-frequency alignment, forming a multi-industry load joint prediction model by the multi-industry load time domain prediction model and the multi-industry load frequency domain prediction model, and predicting the multi-industry load. According to the invention, the accuracy and reliability of the load prediction result are improved.

The invention provides an equipment full operation cycle fault diagnosis method and system based on layered dynamic perception, and the method comprises the steps: inputting the vibration signal samples of a source domain and a target domain of a complex equipment full operation cycle into an adversarial training model, and carrying out the three-stage training, combining the minimum classification loss, the Cauchy-Schwarz divergence loss and the conditional Cauchy-Schwarz divergence loss, and aligning intra-domain and inter-domain feature distribution through a prototype guide normalization mechanism; in the second stage, a feature extractor is fixed, a double classifier is optimized to maximize target domain prediction difference, and a decision boundary fuzzy region is identified; in the third stage, the double classifiers are fixed, the feature extractor is optimized to minimize conditional divergence, and target domain features are driven to converge towards a source domain decision boundary; and inputting the target domain features into the trained model, and outputting a fault diagnosis result under a full-period working condition, thereby improving the robustness and generalization ability of fault identification, and ensuring reliable operation of complex equipment in a complete operation period.

The present application relates to the technical field of geophysical exploration and artificial intelligence, and in particular provides a three-dimensional fault identification method based on dynamic domain difference perception and adaptive alignment. The method comprises constructing a fault segmentation network, performing intensity normalization and size alignment processing on source domain three-dimensional seismic data and target domain three-dimensional seismic data, and inputting the fault segmentation network in a sub-block manner for joint training; in the joint training process, a bidirectional frequency domain adaptive mechanism is introduced, the source domain three-dimensional seismic data and the target domain three-dimensional seismic data are mapped to the Fourier frequency domain, and bidirectional spectral style exchange is performed between the source domain and the target domain; a dynamic domain difference perception module is introduced, and the strength parameter of the frequency domain style exchange is adaptively adjusted based on the difference measurement result; a progressive unsupervised entropy regularization strategy is designed, and an entropy minimization constraint on the target domain prediction result is gradually introduced through a dynamic weight scheduling mechanism, which effectively alleviates the domain offset and realizes the cross-domain high-precision generalization of fault identification.

The invention relates to the technical field of dynamic modeling, in particular to a dynamic linearization dynamic modeling method and device for tank-liquid coupling of a tank truck, and the method comprises the steps: selecting a concerned degree of freedom from six spatial degrees of freedom of a tank body of the tank truck, and compiling an orthogonal working condition table; establishing a fine computational fluid dynamics model of the tank body, and inputting the orthogonal working condition table into the fine computational fluid dynamics model to establish a computational fluid dynamics (CFD) simulation database; establishing a nonlinear high-precision mechanical equivalent shaking model; selecting the most concerned degree of freedom to establish a linearized equivalent pendulum model; and calibrating a nonlinear high-precision mechanical equivalent shaking model and a linearized equivalent pendulum model by using a CFD simulation database to train a neural network so as to output the output characteristics in a prediction time domain step under various concerned degrees of freedom and various working conditions. Therefore, the problems that in the prior art, shaking of fluid in multiple modes cannot be expressed, and precision in long-time-domain prediction cannot be effectively guaranteed are solved.

A communication device in a communications network that includes a network node can generate (1620) a report based on a mobility related time-domain prediction of a neighbor cell of a plurality of neighbor cells The report can include information associated with a subset of the 5 plurality of neighbor cells. Generating the report can include selecting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. Generating the report can further include sorting the subset of the plurality of neighbor cells based on the mobility related time-domain prediction of the neighbor cell. The communication device can further transmit (1630) the report to the network node.

The present invention provides a weighted multi-source domain transfer learning method for bearing remaining life prediction, comprising: obtaining vibration signals over the entire bearing life cycle to determine a bearing degradation dataset; dividing the bearing degradation dataset into a source domain and a target domain, and dividing the target domain into a target domain training portion and a target domain test portion, thereby constructing a multi-source domain transfer learning task dataset; training a source prediction model based on the source domain; fine-tuning the source prediction model based on the target domain training portion to obtain a bearing target prediction model; and obtaining a final bearing remaining life prediction, final prediction uncertainty, and root mean square error based on the target domain test portion and the bearing target prediction model. Taking into account the data scarcity of the target domain, the present invention incorporates a calibration term into the loss function to calibrate the prediction uncertainty during model training. This calibration term can be used to identify negative transfer and quantify the contribution of different prediction models to the target domain prediction task, thereby achieving better remaining life prediction.

The invention discloses a long-tail radiation source individual identification method and device based on field generalization and a storage medium. The core of the method is that a plurality of types of source domain data sets distributed in a long-tail mode are obtained; sampling to obtain batch training data; after extracting feature representation by using a feature extractor, inputting the feature representation into a classifier and a domain discriminator at the same time; based on a classification prediction result, combining a field prediction result of a gradient inversion mechanism and class balance loss to construct an integrated overall objective function; all model parameters are jointly optimized by minimizing the target function, and an optimized recognition model is obtained and is finally used for performing high-precision individual recognition on target domain radiation source signals. Through collaborative optimization of classification precision, category balance and domain generalization ability, the problem of insufficient recognition performance of the model on an unknown target domain in a complex scene with training data long tail distribution and domain offset is effectively solved.

This invention relates to the fields of digital image processing and digital printing control technology, specifically a digital printing color calibration method and system based on real-time image processing. The method comprises: constructing a coordinate set of feature points on the fabric surface using hybrid feature tracking technology; applying an adaptive time-domain prediction algorithm to solve and predict the target deformation field of the fabric at the printing position; and combining this target deformation field with a real-time analyzed fabric texture feature map to dynamically interpolate the original digital image pixel by pixel to generate the final printed image data. By combining highly robust tracking, high-precision adaptive prediction, and high-fidelity image generation technologies, the method significantly improves the geometric accuracy and visual quality of printed products.

The invention discloses a carrying equipment bearing fault diagnosis method and device based on time-frequency dual-domain prediction, and the method comprises the following steps: collecting a bearing acceleration signal, carrying out the normalization preprocessing of the bearing signal, dividing the data into a training set, a fine tuning set and a test set, carrying out the enhancement processing of the data, and carrying out the fault diagnosis of the bearing. And inputting the time domain representation and the frequency domain representation into an encoder for feature extraction, performing model pre-training according to a set predefined proxy task, constructing a target diagnosis model based on a pre-training model, performing fine tuning on the target diagnosis model by using a fine tuning set, and finally inputting a test set into the fine-tuned target diagnosis model for diagnosis. According to the method, diversified data can be generated, potential feature information in unlabeled data can be efficiently extracted, diagnosis is rapid and accurate, and bearing fault diagnosis of intelligent carrying equipment can be realized.

The invention discloses a milling chatter stability domain prediction method and system based on an approximate analysis method, and the method comprises the steps: employing a Fourier approximation method to represent a milling steady-state response based on a milling direction coefficient and a time delay characteristic in a milling dynamic model; applying small disturbance to the steady-state response, substituting the steady-state response into the milling dynamic model, and obtaining a milling stability analysis expression by applying a Galerkin process; substituting the bifurcation condition into the stability analysis expression based on the characteristics of the stability domain boundary corresponding to the milling stability bifurcation condition, and deducing an approximate implicit analytical expression of the milling stability boundary; solving the implicit analytical expression by adopting a numerical continuation method to obtain a stability boundary expressed by rotating speed-cutting depth; according to the method, the stability lobe graph is obtained, the problems of initial continuation point selection and transition between lobes with different stability in the solving process are solved, complete and accurate construction of the stability lobe graph is achieved, theoretical guidance of flutter-free working conditions is provided for milling, and the method has important engineering application value.

The invention discloses an equipment remaining service life prediction method, equipment and a medium, and the method comprises the steps: firstly obtaining operation monitoring data of source domain and target domain equipment, then constructing according to the source domain data to obtain a first input time sequence, and inputting the first input time sequence into a source domain prediction model framework; a feature extraction model and a probability prediction model are arranged in the model framework. Performing feature processing on the sequence through a feature extraction model to obtain comprehensive degradation features, training a probability prediction model by using the degradation features, and obtaining a trained model framework and corresponding posterior parameters based on the trained prediction model; and after a target domain prediction model is constructed according to the target domain monitoring data and the parameters, predicting the to-be-detected equipment through the model to obtain a prediction result of the equipment and corresponding uncertainty information. According to the method, robust prediction in a cross-domain scene can be realized, and meanwhile, corresponding uncertainty information is provided, so that a more comprehensive reference basis is provided for an equipment maintenance decision.

The application provides a building group energy consumption prediction method and system based on space-time graph convolution and adversarial domain adaptation, comprising: obtaining source domain related data and target domain related data, preprocessing to obtain initial input data, etc.; constructing a space-time graph convolution network model and pre-training to obtain a pre-trained space-time graph convolution network model, splitting the model into a space-time feature extractor and a predictor; performing weight initialization and adversarial training on the target domain space-time feature extractor and the target domain predictor to obtain a trained target domain space-time feature extractor and a trained target domain predictor; inputting a target domain test set into the trained target domain space-time feature extractor and the trained target domain predictor to obtain a target domain regional building group energy consumption prediction result; and the application captures the spatial dependence relationship between buildings based on graph convolution and graph attention mechanism, adopts a transfer learning strategy to transfer source domain prediction related knowledge to a target domain, and accurately predicts energy consumption by using a small amount of data.