1528 results about "Perceptron" patented technology

The invention provides an inventory optimization method and system based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining the multi-dimensional data of a commodity, constructing a dynamic convolution kernel through employing a graph neural network, constructing a soft clustering distribution matrix in combination with a multi-layer perceptron, and obtaining a long-term dependence feature fusing a space dimension and a time dimension. And fusing the short-term and long-term features, inputting the fused short-term and long-term features into a bidirectional long-short-term memory network to generate a sales prediction sequence, and calculating a prediction confidence interval to obtain a final sales prediction result. And calculating a safe inventory level based on a sales prediction result, generating a replenishment decision by using a dynamic planning model based on deep reinforcement learning in combination with an inventory cost function, and finally sending the decision to a supply chain execution system. The sales volume is accurately predicted through the deep learning model, the inventory management is optimized, the storage cost, the stockout cost and the overstock cost are reduced, and the supply chain efficiency is effectively improved.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

The invention relates to a power distribution network state estimation method based on an adaptive space-time diagram neural network, and the method mainly comprises the following steps: S1, collecting historical and real-time measurement data of a power distribution network, and carrying out the preprocessing of the data, so as to guarantee the integrity of the data, provide high-quality input data for a model, and improve the estimation precision and stability of the model; s2, discrete wavelet transform is carried out on historical measurement data, multi-scale decomposition is achieved, and low-frequency and high-frequency components are extracted; a double-branch time sequence fusion module is constructed, global trend and local fluctuation features are respectively captured through a dynamic attention mechanism and a time convolution network, and the features are efficiently fused by means of adaptive weights. S3, in the real-time data processing process, branch measurement features are extracted through a multi-layer perceptron (MLP) and mapped to nodes of the whole network, dynamic integration of historical data and real-time data is achieved, and therefore the real-time performance and accuracy of state estimation of the power distribution network are improved.

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention relates to the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention discloses a hanging basket transportation safety management method and system, and relates to the field of damage detection. Collecting weight data of the hanging basket; establishing a steel wire rope health state prediction model by using a multi-layer perceptron (MLP) algorithm, predicting the damage degree of the steel wire rope, and obtaining a health state prediction result of the steel wire rope; according to the health state prediction result of the steel wire rope, a bidirectional long-short-term memory neural network BiLSTM is used for prediction, and a damage trend prediction result of the steel wire rope in a future period of time is obtained; comparing the collected weight data of the hanging basket with a preset safety weight range to obtain a judgment result of whether the weight of the hanging basket exceeds the limit or not; according to the health state prediction result of the steel wire rope, the damage trend prediction result and the judgment result of whether the weight of the hanging basket exceeds the limit or not, a decision tree algorithm is adopted to generate a hanging basket operation control scheme; aiming at low safety of a hanging basket in the prior art, the safety of the hanging basket is improved.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention provides a power equipment fault prediction method and system. The method comprises the steps of performing multi-modal fusion on acquired historical data of power equipment to obtain feature vectors of all modals; fusing the feature vectors by adopting principal component analysis to obtain fusion vectors; constructing the fusion vector into a graph to obtain a space-time diagram; learning spatial features of the spatial information extraction data by using a space-time diagram in a graph convolution mode to obtain a spatial feature representation vector; fusing the spatial feature representation vector in combination with the attention weight to generate an attention fusion vector; learning time sequence information by using a gating loop unit to obtain a time characteristic representation vector; performing weighted feature fusion on the spatial feature representation vector and the time feature representation vector to obtain a space-time fusion representation vector; and processing the space-time fusion representation vector by using a multi-layer perceptron, and outputting power equipment fault prediction. Based on the method, the invention further provides a power equipment fault prediction system. According to the invention, the accuracy and robustness of power equipment fault prediction are improved.

The invention discloses a multi-modal sentiment analysis and interactive adjustment method and system based on a large model, and relates to the technical field of artificial intelligence and natural language processing. Comprising the steps of 1, collecting dialogue video data and preprocessing the video data, 2, respectively carrying out image feature extraction, voice feature extraction and text feature extraction according to video frames, audios and texts, 3, carrying out interactive fusion on every two modal features through a cross attention mechanism, and step 4, generating a corresponding dialogue strategy according to the emotion label in the recognition result and the requirement of appointed emotion adjustment, and carrying out interaction according to the dialogue strategy, so as to obtain the emotion recognition result of the multi-layer perceptron after the feature after the interaction fusion is subjected to average pooling processing and then is input into the multi-layer perceptron to carry out emotion classification, and obtaining a classification corresponding to the highest probability as the emotion label recognition result. Dynamically adjusting a dialogue strategy according to the change of the emotion label; the method is suitable for enhancing the emotion understanding and response ability of the intelligent dialogue system and improving the user experience.

The invention discloses an opportunity network link prediction method based on a time sequence diagram neural network, and particularly relates to the technical field of the time sequence diagram neural network, and the method comprises the following steps: S1, data collection and network snapshot generation; s2, weighting feature coding clusters; s3, GAT and LSTM combined processing embedding is carried out; s4, the probability is calculated through a multi-layer perceptron; s5, constructing a loss function according to the link; s6, drawing sets and training parameters; and S7, multi-index evaluation of the optimal model. The method accurately captures the spatial-temporal characteristics of nodes, collaboratively operates embedding, coding and decoding layers, improves the accuracy and stability of link prediction, stabilizes the reliability of network communication and the success rate of data transmission, innovates a loss function and an optimization strategy, solves the problems of data sparseness and over-fitting, enhances the generalization ability, adapts the model to a multi-field network, and improves the data transmission efficiency. And links are accurately predicted in scenes such as intelligent traffic and disaster rescue, resource configuration and communication efficiency are optimized, and innovative development of multi-industry network application is powerfully promoted.

The invention discloses an interference resource planning method based on a knowledge graph and space-time diagram convolutional neural network. A head entity, a relation and a tail entity gt; constructing an interference pattern decision knowledge graph in a triple form; constructing an interference pattern decision knowledge graph embedding representation model, and generating embedding features representing semantic association; constructing interference pattern decision knowledge fusion based on a multi-layer perceptron, and combining attribute characteristics of interference pattern decision with a historical space-time network interference efficiency evaluation sequence; constructing a space-time diagram convolutional neural network to model embedded features, extracting space correlation characteristics between nodes, capturing a time evolution rule of node attributes, and dynamically predicting interference efficiency; and reasoning an optimal interference pattern in combination with the interference efficiency prediction result and the real-time situation information of the target equipment. The method can effectively predict the interference efficiency evaluation of different interference devices, significantly reduces the prediction error of the interference efficiency evaluation, and improves the prediction precision and model stability.

The invention provides a scientific and technological achievement intelligent matching and transaction recommendation method and system based on a knowledge graph, and relates to the technical field of science and technology, and the method comprises the steps: extracting a technical gene sequence based on a scientific and technological achievement data set, generating a technical state vector, constructing a technical association intensity matrix and a technical iteration path graph, and calculating a technical potential energy distribution field; and forming an initial knowledge graph. And monitoring the fluctuation of the knowledge graph state feature set through the intelligent sensor, and dynamically optimizing the knowledge graph according to the fluctuation condition. Converting technical requirements of a user into a query state, performing state similarity calculation with the knowledge graph, identifying an optimal technical unit combination, predicting a state evolution trajectory, evaluating a regional adaptation degree, and generating a technical implementation scheme. By dynamically optimizing the knowledge graph and accurately matching the user demands, the efficiency and accuracy of scientific and technological achievement matching and transaction recommendation can be effectively improved, and scientific and technological achievement conversion is promoted.

The invention relates to the technical field of electric drive transmission system service life prediction, and discloses an AI-based electric drive transmission system comprehensive service life prediction method, which comprises the following steps: acquiring electric drive system operation parameters and mechanical part degradation data; arranging the data into a system operation full-period input tensor and a component degradation full-period input tensor, and respectively extracting multi-order feature tensors through a bidirectional recurrent neural network and a gated time convolutional network; performing cross-modal fusion by using a cross attention fusion network in combination with physical constraints to obtain a system-component degradation fusion feature vector; and after dimension reduction, inputting a multi-layer perceptron network output life prediction value, and generating a residual service life interval with confidence in combination with historical cases. The method integrates multi-source data, realizes cross-domain feature dynamic association and physical constraint fusion, improves the precision and reliability of life prediction, is suitable for intelligent operation and maintenance of the electric drive system, and provides a scientific basis for equipment maintenance.

The invention discloses a medical image generation method and device based on bimodal fusion, equipment and a medium, and the method comprises the steps: respectively extracting the visual features of a medical image and the semantic features of text description through an image encoder and a text encoder; mapping the two types of features to a shared semantic space by adopting comparative learning to realize cross-modal alignment; a first path captures hierarchical semantic information through a convolutional neural network, a second path retains local significant features through maximum pooling, and two paths of outputs are fused layer by layer to construct spatial context features; projecting the cross-modal features into a spatial feature map through a multi-layer perceptron, splicing the spatial feature map with coding features, and inputting the spliced spatial feature map into a decoder for up-sampling reconstruction; and performing joint optimization on the comparison loss and the structural similarity loss to realize end-to-end training. And finally, the SSIM index of the generated medical image is improved.

The invention provides a road monitoring multi-mode sensing method and a road monitoring multi-mode sensing system adapting to a dynamic environment, and belongs to the field of road monitoring. Performing multi-modal weighted fusion to obtain a feature set after weighted fusion; performing classification processing on the fusion features by using a multi-layer perceptron network; generating a comprehensive evaluation result; dynamically adjusting the working priority of each sensor; alignment is carried out in time and space; automatically adjusting a data fusion strategy; and carrying out data cooperative processing by utilizing edge computing and cloud computing to generate a local model generated by the edge computing and a global model and an optimization result generated by the cloud computing. According to the method, the defects of a traditional road monitoring system in the aspects of environmental adaptability, sensor fusion, calculation efficiency and compatibility are overcome, the overall performance and reliability of the system in a complex and changeable environment are remarkably improved, and the method has wide application prospects especially in the fields of automatic driving and intelligent traffic.

The invention discloses a rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

Techniques are disclosed for generating photorealistic images of objects, such as heads, from multiple viewpoints. In some embodiments, a morphable radiance field (MoRF) model that generates images of heads includes an identity model that maps an identifier (ID) code associated with a head into two codes: a deformation ID code encoding a geometric deformation from a canonical head geometry, and a canonical ID code encoding a canonical appearance within a shape-normalized space. The MoRF model also includes a deformation field model that maps a world space position to a shape-normalized space position based on the deformation ID code. Further, the MoRF model includes a canonical neural radiance field (NeRF) model that includes a density multi-layer perceptron (MLP) branch, a diffuse MLP branch, and a specular MLP branch that output densities, diffuse colors, and specular colors, respectively. The MoRF model can be used to render images of heads from various viewpoints.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention discloses a traffic infrastructure full life cycle carbon emission assessment method based on a knowledge graph. The method comprises the steps that knowledge is acquired through multi-source data acquisition, entities, attributes and relations related to carbon emission are defined, and knowledge graph ontology design and graph construction are carried out; establishing a carbon emission evaluation model based on an artificial neural network on a framework of the knowledge graph through data preprocessing, multi-layer perceptron network structure design and model training; and in combination with the carbon emission evaluation result and the optimization knowledge in the knowledge graph, generating a specific optimization strategy, and performing combinatorial optimization of the optimization strategy to formulate a recommendable optimization scheme for execution of an optimization decision. According to the scheme provided by the invention, carbon emission can be accurately quantified, a scientific and reasonable optimization strategy is generated, dynamic adjustment is supported, and a systematic and intelligent solution is provided for green and low-carbon development of traffic infrastructures and other engineering projects.

The invention provides an intelligent collaborative management method and system for network security operation and maintenance work, and relates to the technical field of network security, and the method comprises the steps: obtaining operation and maintenance work order information containing a work order priority and a processing time limit, and converting the operation and maintenance work order information into a work order feature vector; inputting the skill score, the historical completion rate and the workload of the operation and maintenance personnel into a multi-objective optimization model, and calculating a work order matching degree score and an emergency degree score; performing state coding on the work order feature vector, the personnel feature and the constraint condition by adopting a multi-layer perceptron, extracting spatial correlation by utilizing a double Q network, and calculating a target Q value; and generating a work order distribution scheme based on a Pareto optimal solution algorithm, monitoring a processing state in real time through a task collaborative scheduling model, and generating a collaborative scheduling strategy. The intelligent level of work order distribution is improved, collaborative management of operation and maintenance tasks is realized, and the operation and maintenance efficiency is improved.

The invention provides a whole vehicle point cloud windage coefficient prediction method and system based on multi-scale learning and convolution, and relates to the technical field of windage coefficient prediction, and the method specifically comprises the steps: obtaining point cloud data of a whole vehicle model, and carrying out the preprocessing of the point cloud data; sampling the preprocessed point cloud data through a farthest point sampling method so as to reserve geometric key points; constructing a convolutional neural network model, inputting the point cloud data into the network, sequentially carrying out two times of multi-scale convolution operations, extracting local details and global structure features through convolution kernels of different scales, mapping the features from low dimensions to 512 dimensions and expanding the features to 1024 dimensions, and completing feature aggregation; the feature expression capability is further enhanced through two-layer convolution; utilizing a maximum pooling layer to aggregate global features, and performing dimension reduction on the features through a multi-layer perceptron; introducing a physical guidance attention mechanism to physically constrain the spatial weight of the features; and establishing a mapping relation between the point cloud features and the wind resistance coefficient through a full connection layer, and outputting a wind resistance coefficient prediction result. According to the invention, the accuracy and efficiency of wind resistance coefficient prediction can be improved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention discloses a neurodegenerative disease diagnosis method for constructing a dynamic brain network based on adaptive graph structure learning, and belongs to the technical field of artificial intelligence. The invention provides a self-adaptive graph structure learning multi-level nerve disease diagnosis framework, which can automatically learn and discover complex and deep connection modes among a plurality of brain areas so as to construct a dynamic brain network. According to the method, a global structure of a brain network is embedded into brain network construction based on a time window through a window mapping module, so that richness and accuracy of brain network topology feature representation are improved; in the fusion process of window features, a learnable time graph convolution module is provided to automatically capture time connectivity across time windows and effectively integrate high-order dynamic topological features extracted from different time windows. And finally, disease diagnosis and classification are realized by using a multi-layer perceptron. According to the method, the identification and classification accuracy of the neurodegenerative diseases under the fMRI data can be effectively improved.

The invention relates to the technical field of data processing, and particularly discloses a financing lease risk prediction method and system based on a knowledge graph, and the method comprises the steps: obtaining structured data, unstructured data and real-time transaction flow data from a financing lease service system to form an initial knowledge unit set; identifying entities and semantic relationships from the initial knowledge unit set, performing entity alignment and knowledge fusion, and constructing a dynamically updated financing lease knowledge graph; capturing dynamic association features between entities in the financing lease knowledge graph, extracting a time sequence risk evolution mode of historical transaction data, generating a composite risk feature vector fusing static attributes and dynamic behaviors, and inputting the composite risk feature vector to an integrated model fusing a multilayer perceptron and XGBoost; the lessee default probability, the equipment asset depreciation rate and the industry risk conduction intensity are output; a lessee financing lease risk prediction result is further generated based on a model output result; and scientificity, accuracy and comprehensiveness of financing lease risk prediction are improved.

The invention discloses a physical information fused aero-engine residual life prediction method, and belongs to the field of neural network residual life prediction. The method comprises the following steps: acquiring sensor data of the whole process from engine simulation operation to fault, and performing standardization and windowing preprocessing; establishing a time-space dual-channel multi-layer perceptron model, extracting time dimension and feature dimension information of the signal, performing self-attention fusion with original data, completing mapping to a life prediction value, and calculating an error with a real life; establishing a physical information neural network model, learning a hidden partial differential equation among a predicted life value, time and a hidden state, and performing error calculation with an equation in an equilibrium state; and performing self-attention fusion with the life error to obtain comprehensive loss so as to continuously train the model until the error is minimum, and finishing final life prediction. Physical information is fused on the basis of the multi-layer perceptron, the complexity of the model is simplified, and the prediction precision of the residual life of the engine is improved.

The invention discloses an intelligent data management system and method based on multi-source data collection, and relates to the technical field of data processing. Multi-source data from multiple data sources are obtained, an original data set with credibility marks is generated, data records in the original data set are classified according to data types, and for time series data, data records in the original data set are classified according to credibility marks. Extracting trend features by applying a segmented multi-model time sequence feature extraction framework; for the data of the unstructured data, extraction of unstructured features is carried out through a feature extraction model based on a multi-layer perceptron; for structured data, a knowledge graph technology is adopted to establish an adaptive association relationship, association relationship data is generated, trend features, unstructured features and the association relationship data are used as input of a data value evaluation model, and data asset performance evaluation of dynamic weight is generated; and intelligent management and value evaluation of the multi-source heterogeneous data are realized.

The invention discloses a metal casting process quality monitoring system for intelligent manufacturing and a method thereof, and relates to the technical field of metal casting quality monitoring. According to the system, a Bayesian method and multi-model hypothesis testing are used for monitoring the casting process in real time, whether deviation or abnormity exists or not is recognized, and corresponding adjustment is made, so that it is ensured that the quality of a casting always meets the preset standard; performing parallel processing through four pre-training models, performing feature extraction and fusion on the sensor data after standardization processing, and performing fine adjustment on the pre-training models through transfer learning; and performing classification prediction or regression prediction on the fused feature vectors based on an integration method of a multi-layer perceptron model, and evaluating the uncertainty of a prediction result in combination with an uncertainty quantification method. By means of the system and method, various physical parameters in the casting process can be monitored in real time, accurate prediction can be achieved, problems can be found in time, waste in production is reduced, and the overall quality of castings is improved.

A system and method for controllable lossy data compression employing a joint learning framework to efficiently compress and reconstruct input data while balancing compression ratio and reconstruction quality. The system comprises an encoding system, a temporal modeling system, and a decoding system, which are jointly optimized to minimize a combined loss function. The encoding system, such as a Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input data into a compact representation, while introducing a controllable degree of lossy compression based on adjustable compression parameters. The temporal modeling system, such as a Multilayer Perceptron Long Short-Term Memory captures temporal dependencies in the compressed representation. The decoding system, such as a VQ-VAE decoder, reconstructs the input data from the compressed representation. By providing control over the trade-off between compression ratio and reconstruction quality, the system offers flexibility for diverse applications.

The invention belongs to the technical field of machine learning and pre-stress, and discloses a pre-stress release loss value prediction method and system based on machine learning, and the method comprises the steps: carrying out the multi-working-condition modeling and simulation of a pre-stress beam through finite element numerical software, and extracting the working parameters and design parameters of the pre-stress beam, carrying out data preprocessing, distribution check and feature importance analysis to obtain an initial data set; dividing the initial data set into an initial training data set and an initial test data set, and processing the initial training data set and the initial test data set to obtain a processed training data set and a processed test data set; constructing a full-connection multi-layer perceptron neural network model, defining training, verification and monitoring functions, training the full-connection multi-layer perceptron neural network model by using the processed training data set, and testing the trained model by using the processed test data set to obtain a prediction model; real parameters of the prestressed beam are obtained, the prediction model is used for predicting the prestress release loss value, and a prediction result is obtained.

The invention relates to the technical field of wind power generation, and discloses a wind power plant wind speed correction method and system based on dynamic space-time modeling, and the method comprises the steps: obtaining a whole power curve, obtaining the whole wind speed of a historical period, and constructing a multi-modal training data set; inputting a convolutional neural network to extract local features, inputting a long-short-term memory network, calculating the correlation of each time step feature, obtaining an attention weight, and finally obtaining global feature representation; setting two multi-layer perceptron branches to carry out wind speed prediction correction to obtain a common weather branch prediction value and an extreme weather branch prediction value; constructing a correction curve of each sector and obtaining a correction curve prediction value; and according to the common weather branch prediction value, the extreme weather branch prediction value and the correction curve prediction value, carrying out weighted fusion to obtain a final wind speed correction value. According to the method, the correction precision and robustness are improved, and the interpretability and applicability of the model are enhanced.