2111 results about "Backpropagation" patented technology

The invention discloses a power load prediction method and system based on a meteorological prediction model, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical power load data and corresponding multi-dimensional meteorological data, constructing a meteorological coupling effect analysis model, calculating a dynamic coupling weight matrix among the meteorological parameters through an attention mechanism network; performing feature fusion on the coupling weight matrix and the multi-dimensional meteorological data through a gating circulation unit to generate a space-time coupling feature vector; establishing a power load prediction model, and extracting a short-term fluctuation feature and a long-term trend feature of the power load from the space-time coupling feature vector based on a deep residual network architecture and a parallel convolution channel; the dynamic coupling weight matrix is updated based on prediction error back propagation, and adaptive adjustment of the meteorological parameter coupling relation is achieved. According to the method, dynamic fusion of the spatial-temporal characteristics is realized through the cross gating unit, so that the model can perform power load prediction by using spatial-temporal information, and the prediction precision is improved.

The invention discloses an artificial intelligence-based engineering resource allocation optimization method and system, and relates to the technical field of artificial intelligence and resource management crossing. The problems of resource configuration dynamic change and resource conflict coordination are solved. And processing the multi-modal heterogeneous data in the target engineering scene through space-time alignment and semantic coding to generate a dynamic data stream. A task, resource and environment node relationship is constructed based on a dynamic heterogeneous graph network, and a node dependency weight is updated through an event-driven mechanism. Two-stage collaborative optimization is executed, in the first stage, a baseline scheme is generated through Monte Carlo sampling, and in the second stage, resource conflicts are eliminated through back propagation negotiation. The resource dynamic entropy is monitored in real time, a rebalance algorithm is triggered during local overload, and a distribution scheme is adjusted in combination with security constraints. And finally, outputting the optimization scheme to an engineering management system for execution, dynamically returning updated graph network parameters, forming a data acquisition, optimization decision and feedback closed loop, and improving the intelligence and robustness of resource allocation.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based image segmentation and dynamic target recognition method, which comprises the following steps of: accurately positioning a candidate region through multi-modal space-time fusion and dynamic confidence coefficient screening; strengthening spatial-temporal feature expression in a layering manner through a multi-level feature decoupler, and generating a multi-dimensional feature enhanced spatial-temporal candidate region; through a deformable segmentation network, a deformation convolution kernel and edge motion matching loss are combined, joint optimization of a geometric boundary and motion continuity is realized, and the segmentation robustness of a flexible target is improved; through optical flow back propagation dynamic correction and confidence coefficient propagation, high-precision segmentation masks with consistent time and space are output; and through a target trajectory re-identification and completion mechanism driven by a graph attention network, and in combination with optical flow deformation prediction, stable tracking in a shielding scene is realized.

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention discloses an underwater small target detection method based on YOLOv8 improvement, and aims to solve the problem of small target detection caused by illumination variation, background complexity and target scale difference in an underwater environment. According to the method, optimization is carried out based on a YOLOv8 architecture, firstly, a star convolution StarsBlock in a StarNet backbone network is combined with a C2F module of the YOLOv8, an improved C2FStarsBlock module is obtained, and the diversity and robustness of feature extraction are remarkably improved. Secondly, a CARAFEEnhanced module is adopted to replace up-sampling operation of a YOLOv8 neck, and dynamic convolution, grouping convolution and a nonlinear feature enhancement strategy are introduced, so that the calculation efficiency and adaptability of the model are optimized; and finally, integrating a gradient-guided multi-scale convolution attention mechanism GMSAA, introducing the gradient-guided attention mechanism in a back propagation stage, accurately focusing on a target area, and reducing background interference. The method effectively improves the detection precision of the underwater small target, and is especially suitable for efficient automatic detection of small underwater targets such as marine organisms.

The invention provides a computer document intelligent compliance detection system based on deep learning, and relates to the technical field of data processing, and the system comprises the steps: carrying out the topological structure analysis of an adjacent matrix through a graph neural network, so as to detect an abnormality, obtaining a structure deviation degree, and analyzing a decision path node sequence through a pre-training language model to generate semantic conflict features; fusing the structure deviation degree with the semantic conflict feature to generate a risk feature vector; generating an augmented rule set according to the risk feature vector, and updating the formalized rule base; updating parameters of the feature coding component through gradient back propagation; optimizing a differentiable logic layer judgment threshold value and the weight of an analysis module; and obtaining the updated feature coding component, the rule base version and the risk quantification parameter. According to the invention, the efficiency of document compliance detection is effectively improved.

The invention discloses an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

The invention discloses an image label noise learning method based on granular ball calculation and contrast learning, and the method comprises the steps: obtaining image data, carrying out the preprocessing of the image data, inputting the preprocessed image data into a trained image label noise learning model, and obtaining an image classification result. The training process of the image label noise learning network comprises the following steps: acquiring an image training data set, and performing preprocessing and data enhancement on the image training data set; respectively extracting feature representations of the strong data enhancement sample and the weak data enhancement sample by adopting a contrast learning framework SimSiam, inputting the feature representation of the weak data enhancement sample into a granular ball calculation layer, inputting a feature matrix of the granular ball sample and a label thereof into a classifier after clustering to obtain an image classification result p, and calculating classification loss; predicting the feature representation of the strong data enhancement sample through a prediction layer, and calculating comparison loss by taking an image classification result p as an indicator function; and the network is optimized through back propagation of the comparison loss and the classification loss. According to the invention, the network performance can be obviously improved.

The invention discloses a three-dimensional scene re-coloring optimization method based on Gaussian splashing and deep learning, and the method comprises the following steps: S1, constructing a multi-view data set of a given scene, and generating an initial point cloud; s2, based on the initial point cloud, extracting a representative color set from all input pixels, generating a palette and taking the palette as initial setting; s3, rendering a multi-view result picture by using the palette and a Gaussian splash algorithm, calculating a loss function, and generating an optimized Gaussian radiation field through back propagation optimization parameters; and S4, re-rendering the scene and generating a re-coloring result based on the optimized Gaussian radiation field and the palette color modified by the user. According to the method, the Gaussian splash algorithm and the deep learning technology are combined, efficient re-coloring optimization of the three-dimensional scene is achieved, and the method has the advantages of being visual in editing, accurate in illumination expression and real in rendering.

The invention provides a Monte Carlo tree search and multi-agent cooperation-based knowledge graph question and answer method, which comprises the following steps that: a plurality of agents are initialized, and each agent has an initial process, a state and a target; a graph query statement is generated through multi-agent cooperation, and a complete track with scores is obtained; and utilizing a Monte Carlo tree search method, optimizing a query track through the steps of selection, expansion, simulation and back propagation according to the query track generated through multi-agent cooperation, generating an executable graph query statement, and finally outputting an answer set of the question. According to the method, the advantages of an end-to-end method and a step-by-step reasoning method are combined, meanwhile, the defects that the knowledge graph environment perception ability is insufficient and the search space is too large are overcome, dynamic generation, evaluation and optimization of the query path are achieved by designing a division and cooperation mechanism of the expansion agent, the simulation agent and the valuing agent, and the search efficiency is improved. Therefore, the accuracy and robustness of the question answering system are improved.

The invention provides an automatic heuristic algorithm planning method based on a large language model, and the method comprises the following steps: carrying out the initialization and problem modeling, starting from a basic heuristic mode through guiding the large language model, generating a candidate algorithm set in combination with a plurality of cognitive perspectives, and providing diversified starting points for a search space; configuring core parameters of Monte Carlo tree search; in each iteration process, planning is started in a heuristic space by utilizing Monte Carlo tree search, and the process is composed of five core stages of selection, reflection, expansion, simulation and back propagation; and after all iterations are completed, the path with the highest average reward and the corresponding optimal heuristic algorithm are returned, and the global optimality of the final solution is ensured. According to the method, the effective experience can be automatically extracted from the heuristic strategy generated historically, and real-time feedback adjustment and strategy induction optimization of the heuristic structure are realized, so that the knowledge migration and generalization ability in the search process is remarkably enhanced.

The invention relates to the technical field of data processing, in particular to a multi-source target trajectory fusion method and system based on deep learning. Comprising the following steps: constructing a space-time alignment training set containing radar echo data, satellite telemetry data and truth value trajectory data; designing a multi-channel deep learning network architecture which comprises a radar echo data feature extraction channel and a satellite telemetry data feature extraction channel, and establishing dual-channel feature association through an attention mechanism; optimizing network parameters through back propagation to obtain a weight prediction model; radar echo data and satellite telemetry data are received in real time, and space-time alignment of sampling data is achieved through a sliding window mechanism and coordinate system conversion; inputting the preprocessed double-source features into the trained weight prediction model, and generating a radar-telemetering fusion weight coefficient; and performing weighting processing and optimization on the double-source trajectory coordinates, and outputting a fused target motion trajectory. The problem that a traditional single-source track measurement system is insufficient in precision in a complex environment is solved.

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

The invention discloses a database index data prediction method based on double attention and static fusion GCN. Multiple kinds of database index data are collected to serve as a data set, and the data set is divided into a training set, a verification set, a test set and a static index embedding vector. Scale layers with different lengths are obtained through continuous down-sampling. The time expansion double attention layer extracts time dimension information of different receptive fields; a static feature fusion dynamic GCN layer captures information of a spatial dimension, and global dependence of data is obtained in combination with an attention layer between fragments. And aggregating the information of each time slice to calculate a predicted value. And calculating a loss rate, carrying out back propagation by using an Adam optimization algorithm, continuously updating model parameters, carrying out repeated training until the training loss rate converges, and storing the weight as an optimal model. And inputting test set data into the stored optimal model, and outputting a prediction value and prediction precision. According to the method, the defect of sharing the adjacent matrix is overcome, and the prediction accuracy of the model is improved.

Disclosed in the present invention is a lightweight defect detection method based on hybrid multi-scale knowledge distillation. The method comprises: constructing a dataset; constructing a teacher network model and a lightweight student network model; using the dataset to train the teacher network model, and saving a weight file of the trained teacher network model; and loading into the teacher network model the saved weight file of the teacher network model, inputting defect images in the dataset into the teacher network model and the student network model to respectively obtain first multi-scale features and second multi-scale features, respectively inputting the first multi-scale features and the second multi-scale features into a cascaded knowledge blending module to obtain final deeply fused first multi-scale features and final deeply fused second multi-scale features, then calculating a hybrid multi-scale knowledge loss, and in combination with the prediction loss of the student network model, using a backpropagation algorithm to update network parameters, so as to obtain a trained lightweight student network model for implementing defect detection of intelligent manufacturing products. The cognitive ability and recognition performance for defects of different scales are improved.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

One embodiment of the invention provides a computer-implemented method for optimization of a continuous production process. The method comprises receiving input data comprising a plurality of datasets each including one or more variables relating to a production equipment involved in the continuous production process. The method further comprises generating different prediction models based on the input data. Each of the prediction models is configured to output a target prediction relating to the production equipment. The method further comprises generating an objective optimization model based on each target prediction output from each of the prediction models. The objective optimization model comprises a deep neural network. The method further comprises generating a loss function corresponding to the objective optimization model, and optimizing weights for parameters of the prediction models using backpropagation of the deep neural network and the loss function, resulting in optimized weights for the parameters of the prediction models.

The invention provides a high-resolution remote sensing image semantic segmentation method based on multi-scale depth supervision, and the method comprises the following steps: 1, obtaining an ISPRS Potsdam data set and a Vaihingen data set; 2, building a multi-scale deep supervision network MDSNet, wherein the multi-scale deep supervision network MDSNet comprises a spatial branch network, a semantic branch network and a multi-scale feature aggregation part; each of the spatial branch, the semantic branch network and the multi-scale feature aggregation part is of a four-layer structure; step 3, inputting the training set into a multi-scale deep supervised network MDSNet for training, calculating a loss function and performing back propagation, updating network parameters, and obtaining a trained parameter model; and step 4, inputting the test set into the trained parameter model, and outputting a segmentation image of the target image. According to the method provided by the invention, the feature extraction capability is improved, and meanwhile, the semantic segmentation accuracy, the training efficiency and the stability are improved.

The invention relates to a training method, a data processing method, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The training method comprises the steps that a training sample is input into a to-be-trained model, forward propagation is executed, output of the to-be-trained model is obtained, the to-be-trained model comprises a plurality of expert layers, and a specific activation value is not stored in the forward propagation process; determining the value of a loss function according to the output of the to-be-trained model; according to the value of the loss function and the loss function, back propagation is executed, in the back propagation process of each expert layer, a recalculation task of a specific activation value and a first communication task are executed in an overlapping mode, and for each expert layer, the first communication task comprises a communication task of data interaction among multiple devices corresponding to the expert layer.

Systems and techniques are described herein for training a machine learning (ML) model. For instance, a process can include obtaining, from a teacher ML model, a first prediction based on an input. The process can further include obtaining, from a student ML model, a second prediction based on the input. The process can include determining a loss based on a difference between the second prediction from the first prediction. For instance, the loss can include a variance reduced total variation distance (TVD) loss based on an unbiased gradient estimate of the loss. The process can further include backpropagating the loss through the student ML model to train the student ML model.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention discloses an unmanned aerial vehicle aerial image target detection method based on improved YOLOv8, and relates to the technical field of target detection, and the method comprises the steps: collecting image data of unmanned aerial vehicle aerial photography, inputting the image data into a backbone network of an improved YOLOv8 model, and extracting features; transmitting the feature map to a neck network for feature processing and fusion, and carrying out target classification and bounding box prediction in a head network by using a detection head specially aiming at a small target and a multi-scale feature map; and calculating the difference between a predicted bounding box and a real bounding box by using an MPDIOU loss function, updating model parameters in a back propagation manner, and outputting a detected target category and bounding box coordinates. According to the method, a C2f module is replaced by RepNCSPELAN4, a GAM attention mechanism is introduced, a small-scale feature detection head is added, an MPDIOU loss function is adopted, feature extraction and fusion are optimized, and the detection precision and bounding box regression accuracy are improved.

The invention discloses a network intrusion detection method and system based on edge attention learning, and a storage medium, and the method comprises the steps: converting an original network flow into a network flow diagram, and constructing a training diagram and a test diagram under the condition that a coarse-grained label and a fine-grained label are reserved; edge embedding representation is obtained through edge feature reservation, adaptive weight distribution and multi-layer feature extraction of the training graph and the test graph; based on the edge embedding representation, performing coarse-grained detection to identify a basic attack category, and performing fine-grained classification by using multi-scale feature fusion related to global graph attributes; and adversarial training: through initializing adversarial disturbance and optimizing disturbance based on loss function gradient iteration, superposing final disturbance into the training graph, and based on loss function back propagation updating, obtaining a trained network intrusion detection model. The method provided by the invention can effectively capture the depth characteristics of the key attack and maintain the stable detection performance in the confrontation environment.

The invention provides an underground long-distance pipeline three-dimensional mapping method and system based on three-dimensional Gaussian sputtering. The method comprises the steps that a color map, a depth map and IMU data are collected; pixel coordinate conversion is carried out to obtain point cloud coordinates; inputting the depth map, the color map and the IMU data into a pose estimation algorithm, and processing to obtain a predicted pose of the IMU data, visual pose estimation and visual pose estimation information; initializing three-dimensional Gaussian, performing back propagation through a three-dimensional Gaussian loss function, and optimizing to obtain an applicable three-dimensional model; on the basis of the initialized three-dimensional Gaussian, using a GS-SLAM algorithm to update the applicable three-dimensional model and visual pose estimation information, and using a three-dimensional Gaussian loss function to process to obtain an optimized camera pose; the initial camera pose is updated through Kalman filtering, and pipeline mapping operation is completed. The technical problems that the equipment memory is large, the labor cost and the time cost are high, errors are easy to accumulate, pose deviation is easy to occur, and the model is difficult to maintain relatively high accuracy are solved.

The embodiment of the invention provides an attention mechanism calculation optimization method and device, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: executing a forward re-calculation operator based on a query block and a key block, and obtaining a forward normalization result. Executing a first matrix multiplication operator based on the output gradient block and the forward normalization result to obtain a value intermediate result, and executing a point multiplication summation operator in parallel based on the forward output block and the output gradient block to obtain an intermediate tensor; executing a first type of transformation operator on the value intermediate result to obtain value gradient blocks; and meanwhile, a gradient back propagation operator is executed in parallel based on the output gradient block, the value block, the intermediate tensor and the forward normalization result, and a reverse normalization result is obtained. And finally, on the basis of a reverse normalization result, the key blocks and the query blocks, obtaining query gradient blocks and key gradient blocks, so that effective pipeline processing is performed on different operators, and the overall performance of attention mechanism calculation is improved.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention discloses an image recognition method based on a deep neural network optimization algorithm, and relates to the technical field of image recognition, and the method comprises the steps: carrying out the preprocessing of an image data set, such as adaptive normalization and Hash coding label; coding a network structure according to a graph embedding technology, and mixing and initializing a weight threshold value; algorithm optimization features are fused, particle swarm initial determination is carried out, and then channel characterization fine tuning is carried out; using the training set to improve back propagation and adaptive moment estimation algorithm training, fusing a loss function and dynamically adjusting a learning rate; calculating evaluation and optimization of multiple indexes in the verification set, such as a genetic algorithm search structure and a Bayesian modulation learning rate; and inputting a to-be-recognized image into the optimized network, and obtaining a result by using an integrated recognition method. According to the invention, by optimizing the links of image preprocessing, neural network and recognition process, the image quality and data processing efficiency are improved, the features are accurately determined, the network training is accelerated, the network is comprehensively evaluated and optimized, the reliability of the recognition result is improved, and the image recognition precision and efficiency are improved.

The invention provides a lithium battery SOH estimation method based on a time sequence diagram neural network under physical constraints. The method comprises the following steps: collecting battery charge-discharge cycle data; time-related statistical characteristics and IC curve characteristics are extracted from the battery charge-discharge cycle data, and analysis characteristics are obtained after PCC analysis; calculating a time sequence distance between analysis features based on a sliding window and multi-channel collaborative dynamic time warping, and constructing a graph structure; constructing a time sequence diagram neural network, firstly extracting spatial features through diagram convolution, and then modeling a degradation law through time convolution to obtain a preliminary SOH predicted value; defining a physical residual error loss and a monotonicity regular term, and performing weighted combination on the physical residual error loss and the monotonicity regular term and data fitting loss to construct a joint optimization objective function; based on the optimization target, a final constrained SOH predicted value is obtained through a back propagation training model; therefore, in combination with a physical information constraint mechanism, high-precision and physically consistent SOH prediction is realized.

The invention discloses a sparse view angle three-dimensional reconstruction method based on 3DGS. The method comprises the following steps: providing a Gaussian field initialization model; according to the method, the FPN, the 2D U-Net and the MLP are used for forming a Gaussian parameter initialization network, the network integrates high-level and low-level semantic information, and the understanding of the network on multi-scale features is enhanced. And then a complete Gaussian field model is obtained by combining initial point cloud coordinates generated by DUSt3R. And then performing projection and rasterization under different visual angles on the 3D Gaussian ball by taking three-dimensional Gaussian splashing as a main body frame of an algorithm, performing adaptive adjustment of cloning and pruning on the 3D Gaussian ball with relatively large gradient change, and outputting an output result which is optical information and depth information of each visual angle image after rendering. Then, in gradient back propagation, pixel value absolute value deviation between the RGB image obtained by rendering and the RGB true value is supervised through L1 norm loss; the scene rendering quality is high, calculation is simple, and engineering implementation is easy.