123 results about "Early stopping" patented technology

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention provides an intelligent unmanned cluster autonomous task planning scheme based on a large language model, the scheme mainly aims at the task planning problem of an unmanned cluster in a dynamic environment, the method adopts a Monte Carlo tree search method guided by the large language model and a global memory library, the search space is effectively constrained, the task planning efficiency is improved, and the task planning efficiency is improved. Specifically, a global memory library used for storing historical decision experience is constructed to realize reuse of historical experience so as to reduce the exploration cost of task planning, in addition, an early stop strategy and a reflection mechanism are introduced to correct the decision of a large language model according to errors generated in the search process, and the search efficiency is improved. The problem of decision errors caused by thinking circulation of a large language model is avoided, the search speed and decision robustness of an unmanned cluster task in a complex and changeable scene are further improved, and therefore an efficient and reliable technical scheme is provided for unmanned cluster cooperative combat, search and rescue monitoring and other multi-task scenes.

The invention relates to the technical field of intelligent data processing, in particular to a model training and content auditing management system based on intelligent data acquisition, which comprises an acquisition processing unit, a model training unit and a content auditing unit. The model training unit constructs a multi-modal fusion model, a joint training mechanism of the model learns modal association, adaptive learning rate strategy optimization training and early stop technology anti-overfitting, the trained model is used for content auditing, the content auditing unit receives multimedia content, extracts key frames, audio voice content and subtitle text, and performs feature representation and fusion to obtain a subtitle text. Whether violation occurs or not is judged through the trained model, all the units cooperate with one another, the collecting and processing unit provides a data basis for model training, the model training unit provides a precise model for content auditing, effective auditing of multimedia content is jointly achieved, and legitimacy and health of the content are guaranteed.

The invention discloses a multi-satellite task scheduling method based on a genetic algorithm, and belongs to the technical field of satellite task scheduling. Establishing a scheduling model based on satellite and ground station visible window data, and integrating a scheduling period constraint, a task uniqueness constraint, an equipment protection time constraint and a frequency band and orbit type matching constraint by taking task income maximization as a target; a simulated annealing local search mechanism is introduced into the genetic algorithm, local optimum is jumped out through a probability acceptance inferior solution, and a self-adaptive strategy of mutation probability is dynamically adjusted according to population fitness change; setting an early stop mechanism to terminate iteration in advance when the fitness is continuously not improved; and finally generating a scheduling scheme. According to the method, the problems that a traditional genetic algorithm is prone to falling into local optimum and constraint processing is rigid are solved.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention discloses a PINN-based limited space internal explosion damage parameter prediction method and system. The method comprises the steps of completing data acquisition and constructing a time sequence data set; model construction is completed, and the time sequence modeling capability is enhanced; determining a total loss function and adding boundary condition constraints to enable model prediction to accord with physical laws; firstly optimizing data loss, then introducing a physical residual error, and finally activating a boundary speed suppression loss item and adjusting a learning rate; in combination with a learning rate dynamic scheduling and early stop mechanism, the training efficiency and stability are improved through adaptive residual weighted balance data and physical constraint loss; and predicting parameters such as pressure, temperature and speed of each point in the limited space in multiple time frames by using the trained model, calculating impulse based on a pressure time history, evaluating personnel damage, and completing damage zoning in the limited space. And efficient modeling under a complex boundary condition and a limited space environment is completed.

The invention discloses a visibility regression prediction method based on multi-modal transfer learning and time coding, and relates to the technical field of artificial intelligence. The method comprises the following steps: S1, dividing a data set in different periods according to illumination characteristics, and splitting each period into a training set, a verification set and a test set; s2, preprocessing the data set; s3, constructing an initial model containing a pre-training deep learning network, a time coding module and a multi-layer perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the test set to determine a final model. According to the method, complementarity of image and time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a multilayer perceptron combination technology enhances expression, and overfitting is effectively prevented.

The invention discloses a self-adaptive Gaussian mixture mechanical arm path planning method considering collision escape, and aims to solve the problems of low sampling efficiency, low convergence speed and poor path quality of mechanical arm path planning in a complex environment. The method mainly comprises the following steps: introducing a Gaussian mixture model for dynamic weight adjustment, wherein the model fuses four sampling strategies of target guidance, obstacle avoidance, uniform exploration and collision escape; the weight of each component is adaptively adjusted by evaluating the number of continuous collision times, the distance from the target and the like, so that blind search and heuristic exploration are dynamically balanced, and the sampling efficiency in a complex environment is remarkably improved. In addition, adjacent point search is accelerated by using two-dimensional spatial index, calculation resource allocation is optimized in combination with an early stop mechanism, and the quality of a final path is ensured through path simplification and smoothing. The result shows that the method has remarkable advantages in the aspects of path sampling efficiency, convergence speed and path smoothness.

The embodiment of the invention discloses an air conditioner maintenance data classification method and system based on machine learning, and the method comprises the steps: integrating multi-source heterogeneous maintenance records generated in the maintenance process of air conditioner equipment, building a correlation index through a common identification field, and fusing dispersed data into a maintenance data set in a uniform format; performing hierarchical semantic analysis on unstructured texts in the set to generate structured semantic features, and performing time sequence feature extraction on structured data; then constructing a hybrid classification model training framework fusing semantic and time sequence features, and generating a maintenance data classification model through feature space alignment, dynamic weight distribution, hyper-parameter optimization and an early stop strategy; and finally, classifying newly-added maintenance records by applying the model, checking by combining an expert knowledge base, manually rechecking conflict results, and returning corrected data as an incremental sample back to the model to realize continuous optimization.

The invention discloses a multivariable time sequence prediction method and system based on an implicit neural network. The method comprises the following steps: 1) collecting data and preprocessing the data; 2) performing window division on the standardized or normalized multivariable time sequence and determining the length of a to-be-predicted window; 3) performing variable correlation coding on the input window to obtain a variable-level feature vector; 4) the implicit neural network based on time attention predicts target parameters by using the variable features in the step 3), and implicit neural representation of the target sequence is modeled through the parameters; 5) taking the output of the implicit nerve representation and the original input window as the input of the multi-head attention predictor, and obtaining a prediction result through cross-sequence cross attention calculation performed in the implicit space and multi-layer perceptron conversion output dimension; and 6) training and optimizing model parameters, calculating a mean square error of a prediction result and a real result, taking the mean square error as a loss function, carrying out back propagation to optimize trainable parameters of the variable correlation coding module, the implicit neural network module multi-head attention predictor and the multi-layer perceptron, and then repeating the steps 3) to 6) to obtain the multi-head attention predictor. Until the preset number of iterations is reached or the error of the model on the verification set meets the requirement of early stop; and 7) performing prediction by using a model of training convergence, and performing reverse normalization on a prediction result to obtain a final prediction result. The method has good generalization, and meanwhile, the interpretability of the attention mechanism is remarkably improved by generating the hidden space characteristics of the trend component and the season component.

A welding width and fusion depth prediction method based on machine learning comprises the steps that S1, a data set containing welding parameters is read, and pre-operation is conducted on the data set; s2, constructing and training a machine learning model which comprises a neural network, a random forest regression device and a gradient lifting regression device, dividing the data set into a training set and a test set, and training the machine learning model by using the training set; s3, evaluating the prediction performance of the machine learning model, and using a mean square error (MSE), a mean absolute error (MAE) and a decision coefficient (R2) as evaluation indexes; and S4, using an early stop strategy to prevent overfitting, predicting the welding width and the fusion depth of the test set based on the trained model, and displaying the comparison between a prediction result and an actual value through a chart. The weld width prediction model is established on the basis of the BP neural network, the fusion depth prediction model is established on the basis of multiple integrated learning methods, the welding width and fusion depth are effectively predicted, and the prediction model has high precision and high practical value.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention discloses a deep learning feature matching method and device for a low-illumination environment, a medium and equipment. The method comprises the steps of obtaining feature points of pictures of high-speed moving objects in a power station / transformer substation in the low-illumination environment; the states of the feature points are updated by using a multi-layer self-attention and cross attention mechanism of the LightGlue network; executing a multi-layer self-attention and cross attention mechanism and trimming the feature points lower than the second confidence degree in the feature points after the state updating until the first confidence degree of each feature point left after trimming is greater than a preset threshold value, and obtaining each feature point left after trimming in each picture; the feature matching result of the high-speed moving object in the low-illumination environment is determined based on the final distribution matrix, and the problem of poor detection robustness of the high-speed moving object in a low-illumination power station / transformer substation in a traditional method is solved by using the LightGlue network, a relatively high early stop threshold and a relatively high trimming threshold.

The invention provides a denoising method for a magnetic resonance spectrum based on Transform, and relates to the technical field of signal processing and artificial intelligence, and the method comprises the steps: firstly obtaining a noisy free induction decay signal (FID) and a corresponding label signal, constructing a training data set, converting the signal into a frequency domain signal through fast Fourier transform, and carrying out the amplitude normalization; a real part and an imaginary part of a frequency domain signal are respectively projected to a high-dimensional feature space, and are respectively sent to a multi-head self-attention mechanism-based multi-layer Transform encoder through two channels, so as to capture the global correlation of a long sequence. And the coded output is separated and decoded, a real part and an imaginary part are recovered respectively, and a denoised complex frequency domain signal is obtained through combination. In the training process, a complex mean square error is used as a loss function, and dynamic learning rate scheduling and an early stop mechanism are combined to improve convergence stability and model training efficiency.

The invention belongs to the technical field of communication, and particularly relates to a multipath channel DOA estimation method based on a heterogeneous attention double-branch neural network, and the method comprises the steps: constructing a training set, a verification set and a test set through stratified sampling; introducing a multi-head attention mechanism to construct a heterogeneous attention double-branch neural network for realizing intelligent DOA estimation; designing a frequency weighted loss function as a loss function of neural network model training; a dynamic attention mechanism and an early stop mechanism are designed in a model training process to prevent an overfitting phenomenon. According to the method, the problem of serious DOA estimation model overfitting in a small sample and data non-uniform real acquisition data scene is effectively relieved, and the DOA estimation accuracy is improved.

The invention provides a visual-text cross-modal panda behavior recognition method based on an attention mechanism, and relates to the technical field of attention mechanisms, and the method comprises the steps: inputting a multi-modal data set into an initial model, extracting panda behavior features, introducing a customized cross-modal attention mechanism to achieve feature alignment, and enhancing the visual and text feature interaction depth; the bottleneck of insufficient semantic fusion is broken through; constructing video frame sequence-text description paired samples based on features, carrying out bidirectional matching learning in a unified embedding space through a cross-modal representation network, optimizing parameters through symmetric cross entropy loss, curing a model in combination with a verification and early stop mechanism, capturing video time sequence information, and solving the problem of incomplete behavior dynamic representation; and finally, the visual part of the target panda behavior data is optimized, a preprocessing strategy is adjusted according to quality parameters, an optimization result assists an attention mechanism to focus on key features, the current situation that preprocessing does not have a unified standard and cannot be fed back is improved, and finally the target behavior category is accurately recognized.

The invention discloses a Text2SQL large model fine tuning method and device based on group relative strategy optimization, equipment and a medium, and relates to the technical field of interaction between natural languages and databases. The method comprises the following steps: S1, constructing a training data set, loading a pre-trained Text2SQL base model as a strategy network, initializing the strategy network, and obtaining an initial strategy; s2, performing grouping sampling on the training data set to obtain a candidate set; and S3, according to the candidate set, performing multi-dimensional comprehensive evaluation on the candidate SQL of the candidate set by adopting a weighted combination reward function to obtain relative advantages. And S4, loss is calculated according to the initial strategy and the relative advantage, and parameter updating is carried out. And S5, iterating through a performance monitoring system, an adaptive training strategy, a convergence judgment mechanism and an early stop and model selection strategy to obtain a trained model. According to the method, efficient learning and generation of the model on complex SQL query are realized, and the accuracy and the performability of a Text2SQL task are improved.

The invention discloses a work order semantics and load curve linked metering anomaly positioning method, which relates to the technical field of electric power metering, and is characterized in that an OBIS event is used as a strong anchor, work order time is used as a soft anchor, and acquisition time is used as a sampling anchor to construct a unified time axis, and an optimal window, an operation mask, and machine account confidence and time alignment mapping are output. Decomposing the load curve into trend, period and disturbance residual errors in the optimal window, and decoding OBIS events and work order elements in combination with the morphological dictionary and neighborhood consistency to form three-domain evidence vectors; and fusing the reconstructed residual error, the density rare degree and the single-class interval to generate a candidate list, and solidifying an evidence chain by mask shunting and early stop noise reduction. According to the method, events and semantics are taken as priori, forms are taken as likelihood, a virtual repair operator is introduced to perform anti-fact verification, non-technical loss can be diffused in a transformer area map to cut off an overhaul reconstruction edge, an evidence card write-back closed loop is generated, and recall, cause determination and positioning are improved.

Accurate prediction of the river flow velocity is crucial in water resource management and water conservancy projects, but a complex time sequence mode caused by seasonal changes and weather conditions faces huge challenges. The traditional method is difficult to consider short-term fluctuation and long-term trend and cannot fully mine time sequence information. The invention proposes a river flow velocity prediction method based on time sequence detection and long and short term feature fusion, and the method comprises the steps: mapping a time feature into a low-dimensional vector through an embedded layer to enhance time sequence representation, capturing a short-term dynamic rule and a long-term period rule through employing a dual-branch LSTM, and screening key features in combination with an attention mechanism; the synchronously proposed dynamic learning rate adjustment and early stop mechanism effectively relieves overfitting, and a robust solution is provided for a data scarce scene. According to the invention, through lightweight architecture design and time sequence characteristic collaborative modeling, theoretical support and practical examples are provided for real-time deployment and long-term evolution requirements of a hydrological prediction system.

The invention relates to the technical field of space structure design and parameter optimization, in particular to a neural network model-based reticulated shell structure node parameter automatic optimization method, which comprises the following steps of: acquiring an input-output data pair of a single-layer cylindrical reticulated shell constructed by aluminum alloy plate type nodes, the input parameters comprise design parameters and initial node parameters of the single-layer cylindrical reticulated shell. According to the method, node optimization data under different parameters are obtained through interaction of a genetic algorithm, finite element software and a programming tool, an example is supplemented to construct a training data set covering a common design parameter range of a project, and then a three-layer full-connection neural network model containing two hidden layers is trained; and the model precision is ensured by matching an early stop strategy and a learning rate attenuation strategy. During application, target single-layer cylindrical reticulated shell design parameters are input, the model can automatically output optimized node parameters, repeated iteration and manual intervention of a traditional method are not needed, optimization time consumption is greatly shortened, and the multi-working-condition batch optimization requirement is efficiently met.

The invention discloses a method for constructing an energy storage optimal scheduling uncertainty set fusing estimation and optimization, and the method comprises the following steps: constructing a risk-avoiding energy storage optimal scheduling model, constructing a robust optimization model based on an electricity price uncertainty error vector, and enabling an uncertainty set to cover the error distribution under a preset probability threshold; constructing a double-layer optimization model taking a decision as a center, and jointly optimizing related parameters and variables to form a nested structure; executing a statistical feasible size calibration method, determining a minimum feasible set radius based on mahalanobis distance sorting of historical samples, and ensuring that coverage probability constraints are met; and calculating an objective function gradient, solving the gradient by applying an implicit function theorem and a KKT condition, iteratively optimizing parameters by utilizing a gradient descent algorithm after synthesizing a total gradient, and obtaining an optimal uncertainty set in combination with an early stop mechanism. According to the method, the problem that a model and a strategy are disjointed in a traditional two-stage method of estimation first and optimization second is solved, and the robustness and the economical efficiency of energy storage scheduling can be effectively improved.

The invention provides a communication radiation source individual identification method based on time domain and time sequence coding constellation diagram feature fusion. The method is used for realizing stable identification of different transmitters under non-ideal conditions of carrier frequency offset, noise interference and the like. The method comprises the following steps: firstly, carrying out normalization and fixed-length interception on a received I / Q signal, and calculating an oversampling multiple according to a symbol rate and a time resolution; then, a time domain and time sequence coding constellation diagram double-branch feature extraction network is constructed, a time domain structure feature is extracted from a time domain branch by adopting a one-dimensional modified ResNet18, and a constellation diagram with time information is generated from a time sequence coding constellation diagram branch through a time sequence coding constellation module and the feature that the phase changes along with time is extracted; and carrying out joint modeling on the two types of features through a trainable weighted fusion mechanism. In the training stage, the performance of the model is improved by adopting cross entropy loss, an Adam optimizer, learning rate scheduling and an early stop strategy. In the reasoning process, input signals are classified by using the model weight with the highest verification accuracy, and a communication radiation source individual identification result is output. Experimental results show that the method can effectively enhance the frequency offset robustness and generalization ability of the model, realizes high-precision identification of the individual communication radiation source, and is suitable for the fields of security access of the Internet of Things, spectrum monitoring, electronic countermeasures and the like.

The invention provides a ship ballast water system fault diagnosis model establishment method, belongs to the technical field of ship ballast water systems, and adopts a random noise data enhancement and maximum and minimum value standardization preprocessing method to improve data quality. Designing a CNN-I nformer hybrid architecture, fusing a probability sparse self-attention mechanism and distillation operation to extract time sequence features, and establishing a double-layer game optimization framework including an adaptive spiral flight sparrow search algorithm to perform joint optimization on model hyper-parameters; a softmax classifier is adopted to realize accurate identification of five states of sea chest filter filth blockage, ballast water pump bearing wear, valve blockage, pipeline leakage and system normal operation, and generalization ability and practicability of the model are ensured through an early stop strategy and multi-index performance evaluation. The technical problems that the ship ballast water system fault diagnosis precision is insufficient and the calculation complexity is too high are solved.

The invention provides a hyper-parameter optimization method based on meta-learning and an improved genetic algorithm for improving the precision of a neural network on the basis of determining a data set and not modifying the network in a real scene, and the method comprises the steps: extracting the sample features of each sample of the data set through a data sample feature extractor; using a K-means clustering algorithm to extract a clustering center as a feature centroid to obtain data set element features, calculating element feature cosine distances in pairs, and obtaining a data set element feature similarity matrix; calculating an optimal hyper-parameter combination by using an optimization genetic algorithm added with reverse learning and an adaptive crossover mutation rate; the execution time of the algorithm is reduced through an automatic early stop strategy. According to the method, the optimal hyper-parameter of the neural network can be searched under the condition of less time overhead, and the precision of the neural network is improved.

The invention discloses a deep learning-based bridge scour damage identification method and system, and solves the problems of poor universality and limited scour damage identification efficiency and precision of the existing bridge scour damage identification method. The method comprises the following steps: constructing an original data set; preprocessing the data; the method comprises the following steps: constructing a sequence-to-sequence multi-parameter model of an encoder-decoder architecture by adopting a double-layer long short-term memory network, and adding a Dropout layer and a LayerNorm layer in the model for standardization; a loss function of model training and optimization is defined, an Adam optimization algorithm is adopted, L2 regularization is added, an early stop method is utilized to train and optimize the Seq2Seq multi-parameter model, and a model with the minimum verification set loss is selected as an optimal prediction model to be used for recognizing the bridge scouring damage. According to the method, the complex scouring damage condition of the multi-span bridge can be rapidly identified, the scouring damage identification efficiency and precision are improved, the universality is higher, and the identification efficiency is higher.

The invention discloses a hydropower station reservoir runoff intelligent forecasting method based on a large language model, and belongs to the technical field of hydrological forecasting, and the method comprises the following steps: S1, data collection and preprocessing; s2, constructing a BERT-Hydrology deep learning model for the runoff forecast of the reservoir of the hydropower station; s3, designing a weighted mixed loss function, and introducing an optimizer, learning rate scheduling and an early stop mechanism to improve the convergence speed and numerical stability of model training; and S4, establishing an evaluation system, performing rolling forecasting, and outputting an evaluation index and a significance test result. According to the intelligent hydropower station reservoir runoff forecasting method based on the large language model, the accuracy and the stability of runoff forecasting are remarkably improved by constructing the runoff forecasting model, excavating the hidden rule in the hydrological sequence and establishing the multi-step forecasting mapping, and the forecasting accuracy and the forecasting stability are improved. And technical support is provided for intelligent scheduling of a hydropower station reservoir and efficient management of water resources.

The invention belongs to the field of resource allocation, particularly relates to a multi-chain resource allocation technology based on a neural network, and provides the following scheme that the multi-chain resource allocation technology comprises a block chain multi-chain system and further comprises the following steps: S1, preparing training data by using a neural network training module: preparing the training data by using the neural network training module; s2, training a node optimization distribution module: dynamically adjusting network parameters through an adaptive optimization algorithm based on the training data set processed by the neural network training module, and controlling an iteration process by adopting an early stop mechanism and a learning rate attenuation strategy until a loss function is converged to a preset threshold value, according to the method, node attributes and global load information are dynamically fused through the graph neural network, intelligent optimal distribution of nodes is achieved, meanwhile, the over-smoothing problem caused by multi-layer stacking can be avoided while light weight of the model is guaranteed, and through intelligent decision making and dynamic adaptation capacity, the optimal distribution of the nodes is achieved. And the problems of resource mismatching, system bottleneck and expansibility are solved.

A method for early stop using dynamic mode decomposition (DMD) includes determining a best fit linear operator that relates two sets of observability metrics (also referred to herein as "states") associated with the training of a machine learning model, such as an LLM. The best fit linear operator, in turn, is used to predict future values of the observability metrics at future iteration steps in the training. The predicted values are then evaluated against an early stop criterion for each respective metric to assess whether training should continue, training should be terminated, or a corrective action should be applied, e.g., by adjusting one or more hyperparameters related to the training. The methods disclosed herein may be executed using various computing systems. Additionally, a non-transitory computer-readable storage medium (CRM) may store instructions, which when executed by a processor, performs the inventive methods.

The invention relates to a multi-channel multi-mode space-time 3D convolution and Transform fused satellite-borne GNSS-R wave height inversion model, and aims to solve the problems that a traditional empirical model is poor in adaptability to complex oceans and existing deep learning cannot fully excavate space-time correlation. According to the method, three modules are innovatively fused for cooperative processing: firstly, a 3D CNN-ConvLSTM is utilized to extract local spatial-temporal characteristics of multichannel GNSS-R data and capture a dynamic time sequence; secondly, performing deep coding on the sea surface environment parameters through a multi-head attention mechanism of Transform, and establishing global dependence between features; and finally, realizing cross-modal fusion by adopting a weighted summation and feature splicing strategy. And through training of an Adam optimizer and control of an early stop strategy, optimization is carried out by taking a mean square error as a loss function. Experiments show that compared with a traditional model and a machine learning model, the method has the advantages that the significant wave height estimation error is reduced by 40-53%, the correlation coefficient reaches 0.84-0.91, the precision and generalization ability under the complex sea condition are remarkably improved, and reliable support is provided for ocean remote sensing monitoring and disaster early warning.

The invention provides a multi-modal long text vulnerability classification method based on transfer learning and loss adjustment, belongs to the technical field of computers, and solves the technical problems that an existing method is low in classification accuracy and poor in generalization ability. Comprising the following steps: S1, preprocessing a data set; s2, dividing a training set, a verification set and a test set; s3, splicing the bimodal information of the vulnerability description and the code information into long text input, and designing a sliding window segmentation fusion method; s4, finely adjusting the pre-training language model to complete a vulnerability assessment task, and adopting an early stop strategy; s5, migrating knowledge learned in the evaluation task to a vulnerability classification task; s6, designing a dynamic loss function adjustment mechanism to carry out weight adjustment; and S7, inputting test set data into the trained model, and performing vulnerability classification prediction. The method has the beneficial effect that the performance of the vulnerability classification task is improved through transfer learning and loss adjustment.