58 results about "Mean absolute percentage error" patented technology

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention provides a power distribution network line loss analysis method and device based on data and model dual drive. The method comprises the steps that S1, original data, collected by SCADA / AMI equipment, of a power distribution network are acquired and preprocessed into a feature matrix; s2, calculating theoretical line loss by using a multi-target algorithm library, and calculating a residual error between the theoretical line loss and actually measured line loss; s3, splicing the feature matrix and the residual error into an input vector; s4, inputting the input vector into a pre-trained LSTM network to generate a parameter correction amount; s5, dynamically adjusting the current algorithm parameters of the multi-target algorithm library based on the correction amount; s6, the theoretical line loss is recalculated through the updated parameters, and a calibrated line loss value is obtained; and S7, evaluating the average absolute percentage error of the calibrated line loss value by using a sliding window, if the average absolute percentage error exceeds a preset threshold value, re-executing the steps S2 to S6, otherwise, outputting the line loss value. According to the method, high-precision and adaptive analysis is realized, manual intervention is reduced, efficiency and reliability are improved, and intelligent management of a power system is supported.

The invention discloses a tunnel ventilation system and a control method thereof, and relates to the technical field of tunnel ventilation energy-saving control. According to the method, traffic data, environment data and equipment starting data in a tunnel are preprocessed and divided into data sets, a traffic prediction model based on LSTM and an environment prediction model based on full connection are constructed, and training is carried out through a root-mean-square error and an average absolute percentage error; and sequentially predicting traffic and environment data by using the model, and solving an optimal ventilation equipment combination through a sequential quadratic programming algorithm by taking energy consumption minimization as a target and environmental standard reaching as a constraint, and adjusting operation. The method has the advantages that advanced regulation and control are achieved through the two-stage prediction model, pollutants are prevented from exceeding the standard, and the air quality is guaranteed; dynamic training, multi-parameter optimization and a fault tolerance mechanism are combined, energy conservation and environment regulation and control are accurately balanced, meanwhile, the operation reliability and the intelligent level of the system are improved, and manual intervention is reduced.

According to the logging-while-drilling data lag compensation method based on deep learning provided by the invention, the prediction precision of the lag logging-while-drilling data is improved by combining the drilling engineering data and the historical logging-while-drilling data. According to the method, an encoder-decoder neural network model based on a self-attention mechanism is utilized, lag logging-while-drilling data is predicted in combination with drilling engineering features, and prediction precision is improved through a convolutional layer. Results show that when the method is used for predicting the gamma curve in the logging-while-drilling data, the root mean square error within the prediction range of 30 meters is lower than 0.589, and the average absolute percentage error is lower than 1.587. The problem of lag of the logging while drilling data is effectively solved, the operation risk under the complex stratum condition is reduced, the drilling strategy can be optimized in real time, and the safety and efficiency of the drilling process are improved.

The invention relates to the technical field of lithium ion battery prediction and health management, and discloses a lithium ion battery early life prediction method for a marked data scarcity scene, and the method comprises the steps: extracting the degradation characteristics of a preset dimension through the charging and discharging data of the first 70 cycle periods of a lithium ion battery, and carrying out the subsequent prediction; training a plurality of prediction models by using the labeled samples, and obtaining pseudo labels of the unlabeled samples by using the trained prediction models; clustering the unlabeled samples to obtain corresponding cluster labels; calculating a confidence score of each unlabeled sample based on the pseudo labels and the cluster labels, and selecting the unlabeled samples with high scores and the corresponding pseudo labels to add into an initial training set; and meanwhile, through the change of the average absolute percentage error of the training set, a preset confidence threshold and a preset sampling proportion are dynamically adjusted, and the training set is updated, so that a more accurate target prediction model is trained, and the early life of the lithium ion battery is predicted.

The invention discloses a flight landing time prediction method based on machine learning, and relates to the technical field of flight pre-judgment, and the method comprises the steps: carrying out the preprocessing of data based on historical flight data and historical meteorological data, and outputting feature sample data; performing data cleaning and standardization processing on the feature sample data, and outputting a training data set and a test data set in combination with actual landing and planned landing moment information of historical flights; establishing a landing prediction model, and performing optimization by combining reinforcement learning and a generative adversarial network; using the average absolute percentage error to test the landing prediction model and output a trained landing prediction model; and outputting a dynamically updated landing prediction model by adopting a concept drift detection algorithm, online gradient lifting and adaptive weighted fusion. Through combination of the deep Q network and multi-agent reinforcement learning, the prediction result is optimized in real time, and dynamic adjustment can be performed according to changes of actual flights.

The invention relates to a Nusselt number prediction method, program and equipment based on a heat transfer associated hybrid model of a shell-and-tube heat exchanger and a storage medium, and belongs to the technical field of intelligent design and performance optimization of industrial heat exchange equipment. The objective of the invention is to solve the problems of low prediction precision, poor adaptability and lack of physical interpretability of a traditional empirical correlation under complex working conditions. Through an adaptive weight mechanism, the final hybrid model can automatically select an optimal prediction strategy according to different working conditions, and in four typical working condition cases, the average decision coefficient is up to 0.9930, and the average absolute percentage error (MAPE) is only 1.66%, which is significantly superior to that of a single model. According to the system, high-precision and high-robustness prediction of the heat transfer performance of the shell-and-tube heat exchanger is achieved, more importantly, the corrected correlation formula has clear physical significance and high interpretability, and a revolutionary tool is provided for intelligent design, real-time performance monitoring and energy efficiency optimization of the heat exchanger.

The invention relates to the technical field of ocean remote sensing, in particular to an offshore water quality remote sensing inversion and classification method based on small sample deep learning, which comprises the following steps: acquiring offshore in-situ observation, satellite remote sensing and ocean reanalysis data; carrying out atmospheric correction, carrying out space-time reconstruction on the missing remote sensing reflectivity by adopting a data interpolation empirical orthogonal function, carrying out mathematical transformation on input features, and screening out an optimal feature subset by adopting a strategy based on an average absolute percentage error; constructing a deep learning network based on a generative small sample to invert the offshore soluble inorganic nitrogen and inorganic phosphorus concentration, and determining the water quality grade; and analyzing an inversion mechanism by using an SHAP method. According to the method, the problem of data space-time discontinuity is solved through the data interpolation empirical orthogonal function, the capture capability and inversion precision of the nonlinear relation under the small sample condition are remarkably improved by utilizing the generative network, the model interpretability is realized in combination with SHAP analysis, and scientific support is provided for offshore water quality fine management.

The invention relates to a numerical control machine tool reliability modeling method and system considering a fault trend. The method comprises the following steps: checking the homogeneity of a numerical control machine tool based on the fault trend; constructing an AMSAA model of the multiple samples subjected to truncation at unequal time; evaluating the precision of the reliability model; aiming at the problem of insufficient reliability model precision of a multi-sample condition of a numerical control machine tool, a multi-sample AMSAA model modeling method of the numerical control machine tool considering a fault trend is provided, sample data classification is carried out through trend inspection and homotype inspection, and fault data of same-type machine tools are preprocessed by adopting a fault total time method; adopting a maximum likelihood method to estimate AMSAA model parameters of the same-type machine tool, and using Cramer-Von Mises to test the goodness of fit of the model; evaluating the precision of the reliability model by taking an average absolute percentage error (MAPE) of an instantaneous MTBF point estimation value and an MTBF observation value as an index; compared with a multi-sample AMSAA model established by a direct maximum likelihood method, the method is higher in prediction precision.

The present application belongs to the technical field of photonic device design, and specifically relates to a micro-ring resonator reverse design optimization method based on deep learning. The present application comprises: obtaining different micro-ring resonator structure parameters and corresponding free spectral range and quality factor, and processing and dividing the data set; building a cascade neural network model, wherein the reverse network takes the target performance index as the input to predict the structure parameter, the forward network inputs the structure parameter and outputs the performance index, and is used to constrain and correct the reverse prediction result; after training, the test set is evaluated to measure the reverse effect by the average absolute percentage error index, the weight in the loss function is changed, and multiple rounds of training are performed to determine the optimal weight configuration and final model of the reverse design. The present application has the advantages of high calculation efficiency, excellent design precision and strong physical realizability, can reduce the dependence on large-scale simulation and multiple iteration optimization, effectively shorten the device design cycle, and has high application value.

The present invention discloses a configuration generation method of an adjustable phononic crystal based on deep learning, a computing device, a computer program product and a storage medium. The configuration generation method of an adjustable phononic crystal based on deep learning is executed in a computing device. The method includes: inputting a first eigenfrequency vector indicating the dispersion relation of the phononic crystal configuration to be generated into a configuration generation model for processing to obtain at least one candidate configuration image; performing image preprocessing on each candidate configuration image to obtain multiple similar images; inputting each similar image into a dispersion relation prediction model for processing to obtain a second eigenfrequency vector representing the dispersion relation of each similar image; calculating the average absolute percentage error value of the first eigenfrequency vector and each second eigenfrequency vector respectively, and selecting the similar image indicated by the second eigenfrequency vector corresponding to the smallest average absolute percentage error value as the configuration image of the phononic crystal to be generated.

The application discloses a cold-rolling friction coefficient prediction method based on a physical graph topology and a graph convolutional neural network, and comprises the following steps: defining a graph topology structure establishment rule for friction coefficient analysis in plate strip cold continuous rolling; selecting analysis parameters according to plate strip cold continuous rolling process characteristics and production experience, determining a node set, a physical relationship function set and an edge set in the graph topology structure; constructing an adjacency matrix and a corresponding self-adjacency matrix of the graph convolutional network with physical weights; introducing a degree matrix to perform normalization processing on the self-adjacency matrix to generate a normalized self-adjacency matrix; constructing a physical feature graph convolutional neural network based on the above settings and a basic form of the graph convolutional network; selecting cold-rolling product samples, obtaining analysis parameters and friction coefficient prior values, and training the physical feature graph convolutional neural network; and adopting a determination coefficient, a mean square error, a mean absolute error and a mean absolute percentage error to evaluate the performance of the physical feature graph convolutional neural network.

A method for electricity consumption forecasting based on small sample data is described. This method first preprocesses historical electricity consumption data and uses four-interval dynamic discretization binning to optimize data characterization. Subsequently, ridge regression is used to construct #imgabs0# submodels with differentiated hyperparameter configurations, and weights are calculated for each submodel. During the weight calculation process, if boundary constraints are not met, the weights are readjusted. For a rolling window, initial values are set, and the mean and variance of the data within the window are calculated. If the mean or variance is abnormal, the window length is adjusted. After this processing, the data is input into the model, and predictions are made using the initial regularization parameters. The regularization parameters are adjusted based on the feedback relationship between the mean absolute percentage error (#imgabs1#) and a set threshold. If #imgabs2# exceeds the threshold three times, a feedback adjustment mechanism is triggered to optimize the weights, window length, and regularization parameters until the conditions are met and the final prediction result is output.

The invention discloses an eggshell spot detection method based on multi-scale feature fusion and an attention mechanism, and relates to the technical field of eggshell spot detection, and the method comprises the steps: collecting an eggshell surface image, carrying out the data labeling, generating a spot region binary mask, and calculating a spot area proportion true value; carrying out data preprocessing on the image, wherein the data preprocessing comprises size adjustment, data enhancement and standardization; an end-to-end deep convolutional neural network model is constructed, the model extracts and fuses multi-scale features through a texture feature encoder, key region features are enhanced through a speckle attention module, and a speckle area proportion prediction value is output through a regression prediction module; training the model, and optimizing parameters by adopting a smooth average absolute percentage error loss function; and inputting a to-be-detected eggshell image to the trained model, and outputting a spot area proportion evaluation result. According to the invention, automatic, high-precision, high-efficiency and lightweight prediction of the eggshell spot area proportion is realized.

This application provides a data- and model-driven distribution network line loss analysis method and device. The method includes: S1: obtaining raw data collected by SCADA / AMI equipment from the distribution network and preprocessing it into a feature matrix; S2: using a multi-objective algorithm library to calculate theoretical line loss and calculate the residual between the theoretical line loss and the measured line loss; S3: concatenating the feature matrix and the residual into an input vector; S4: inputting the input vector into a pre-trained LSTM network to generate parameter corrections; S5: dynamically adjusting the current algorithm parameters of the multi-objective algorithm library based on the corrections; S6: recalculating the theoretical line loss using the updated parameters to obtain a calibrated line loss value; S7: using a sliding window to evaluate the mean absolute percentage error of the calibrated line loss value. If it exceeds a preset threshold, S2 to S6 are re-executed; otherwise, the line loss value is output. This method achieves high-precision, adaptive analysis, reduces manual intervention, improves efficiency and reliability, and supports intelligent management of power systems.

The invention relates to the technical field of power systems and intelligent power grids, and discloses a photovoltaic power station generation power prediction method based on a Q learning combination model, and the method comprises the steps: collecting meteorological data, power station operation data and time sequence data, carrying out the preprocessing of the collected original data, and screening out the stronger correlation characteristics; a photovoltaic power prediction model based on an XGBoost model is established according to the processed data, a photovoltaic power prediction model based on an LSTM model is established according to the processed data, and after preliminary prediction results of the XGBoost model and the LSTM model are obtained respectively, a Q learning algorithm is adopted to optimize the weight of the combined model to realize accurate prediction of photovoltaic power. And finally, selecting four indexes of a decision coefficient, a root-mean-square error, a mean absolute error and a mean absolute percentage error, evaluating the obtained prediction result, and integrating the steps to finally obtain the prediction method with dynamic optimization combination parameters, adaptive data distribution change and enhanced small sample generalization ability.

The present application relates to the technical field of lithium ion battery prediction and health management, and discloses a lithium ion battery early life prediction method for a labeled data scarce scene, which comprises the following steps: using the charging and discharging data of the first 70 cycle periods of the lithium ion battery, extracting degradation features of a preset dimension, and performing subsequent prediction; using labeled samples to train a plurality of prediction models, using the trained prediction models to obtain pseudo-labels of unlabeled samples; clustering the unlabeled samples to obtain corresponding cluster labels; based on the pseudo-labels and the cluster labels, calculating the confidence score of each unlabeled sample, and selecting the unlabeled sample with a high score and its corresponding pseudo-label to add to the initial training set; and simultaneously, by the change of the average absolute percentage error of the training set, dynamically adjusting the preset confidence threshold and the preset sampling ratio, updating the training set, and training a more accurate target prediction model to predict the early life of the lithium ion battery.

The invention discloses a part quality dynamic early warning system based on a hierarchical federal learning framework, and the system comprises a part quality dynamic early warning platform which comprises a data processing unit, a part quality prediction unit and a dynamic early warning unit, and the data processing unit is used for preprocessing data; the part quality prediction unit is used for performing part life prediction by utilizing a CNN-Informer model on the basis of the data preprocessed by the data processing unit, and optimizing model hyper-parameters by taking a minimum symmetric average absolute percentage error SMAPE as a target function and adopting an improved big sugarcane-mouse algorithm; the dynamic early warning unit is used for deciding whether to give an early warning or not based on the prediction result; the system security protection platform is used for providing computing support through the cloud computing platform, verifying a user and guaranteeing data security; the enterprise user interaction platform is used for receiving the early warning result of the dynamic early warning unit and giving user feedback suggestions; the method can effectively improve the comprehensiveness and accuracy of part life prediction.

The invention relates to a digital data processing technology of a power management system, in particular to a power demand analysis method and system under multi-source data fusion. The analysis method comprises the following steps: collecting and converging multi-source heterogeneous data; performing data fusion and standardization processing; carrying out multi-dimensional scene load characteristic modeling and trend analysis; performing layered and classified electricity demand prediction; and outputting a hierarchical classification prediction report. The analysis system comprises a multi-source data acquisition module, a data fusion processing module, a feature modeling and analysis module, a hierarchical prediction and optimization module and a visual display module. Compared with the prior art, the method has the advantages that a complete power utilization analysis data panorama is constructed, and the load feature recognition integrity is improved by more than 40%; the average absolute percentage error MAPE of short-term load prediction can be reduced to be within 3% and is improved by about 35% compared with a general modeling analysis method; and the efficiency of converting the prediction result into the action can be improved by 50%.

This invention proposes a ship heave prediction system and method based on a Conv-Bi-LSTM model. The method involves acquiring historical ship motion data, establishing a motion information dataset, preprocessing the dataset, randomly splitting it into training and test datasets, constructing a ship heave prediction model using a Conv-Bi-LSTM model, training the Conv-Bi-LSTM network with the training dataset, and inputting the test dataset into the ship heave prediction model for prediction. The prediction accuracy of the test data is then obtained, and the model's performance is evaluated using mean error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). This invention uses historical motion information with multiple degrees of freedom as input to the model for comprehensive prediction of ship heave, fully utilizing the forward and reverse time state information of the motion time series, thus improving the prediction accuracy of heave motion.

The present invention provides a method and system for long-term prediction of cutterhead torque of a full-face tunnel boring machine, including: collecting cutterhead torque signals during the tunneling process of the full-face tunnel boring machine and preprocessing them to obtain a cutterhead torque sequence; decomposing the cutterhead torque sequence into high-frequency and low-frequency parts by using a wavelet packet decomposition matrix; decomposing the low-frequency part into several subsequences and a residual sequence; decomposing the high-frequency part into several subsequences; constructing a multi-step long-term prediction neural network model for cutterhead torque and training it; normalizing several subsequences respectively by using the min-max method and transmitting them to the trained multi-step long-term prediction neural network model for cutterhead torque to obtain several prediction results; adding the several prediction results to obtain the cutterhead torque value at the predicted time t; calculating the mean absolute percentage error, root mean square error and mean absolute error respectively according to the multiple cutterhead torque values obtained by prediction, and evaluating the prediction performance of the cutterhead torque.

This invention discloses a tunnel ventilation system and its control method, relating to the field of tunnel ventilation energy-saving control technology. The method first preprocesses and divides the tunnel's traffic data, environmental data, and equipment operation data into datasets. It then constructs an LSTM-based traffic prediction model and a fully connected environmental prediction model, trained using root mean square error and mean absolute percentage error, respectively. The models sequentially predict traffic and environmental data. With energy minimization as the objective and environmental compliance as the constraint, a sequential quadratic programming algorithm is used to solve for the optimal combination of ventilation equipment and adjust its operation. Its advantages lie in the two-level prediction model's ability to achieve proactive regulation, preventing pollutant exceedances and ensuring air quality. Combined with dynamic training, multi-parameter optimization, and fault tolerance mechanisms, it accurately balances energy saving and environmental control while improving system reliability and intelligence, reducing manual intervention.

The invention discloses a time series prediction method fusing a bilinear layer and bidirectional normalization, and belongs to the technical field of time series prediction. The method comprises the following steps: generating a global level coefficient and a fluctuation amplitude coefficient through a lightweight neural network, carrying out normalization processing on an input sequence, and constructing a multi-level feature extraction module comprising a bilinear layer and a nonlinear activation function; then, a weighted constraint condition is constructed based on a sliding window mean value and a target sequence mean value, and a prediction result is mapped to an original distribution space through a reverse normalization module; designing a mixed loss function and training a model; constructing a joint optimization target by combining prediction error loss and priori knowledge supervision loss, and updating parameters by adopting a self-adaptive optimization algorithm; predication is realized by dynamically loading the optimal model, and performance quantification is performed based on a mean square error, a mean absolute error, a root-mean-square error and a mean absolute percentage error.

The present invention discloses a high-dimensional feature reconstruction and fusion method based on SMT quality big data, which mainly solves the problems of low data utilization, too few feature dimensions and low accuracy of prediction models in the prior art. Its implementation scheme is: preprocessing the SMT production line text data set and structured data set; constructing and training a text data feature extraction model to obtain text data extraction features; constructing a structured data feature extraction model to obtain structured data extraction features; merging and deduplicating features extracted from text data and structured data; using a stacked autoencoder and a method based on the combination of mean square error and mean absolute percentage error to reconstruct and fuse the extracted features. The present invention improves the utilization rate of SMT enterprise data, realizes the fusion of text data and structured data, increases the data dimension to more than 50 dimensions, improves the accuracy of the model, and can be used for multimodal data processing of SMT production line quality big data.

The invention relates to the technical field of machine learning, and discloses a training method for an electrical performance prediction model of a semiconductor tape-out, and computer equipment, and the method comprises the steps: obtaining training data which comprises the technological parameters of a plurality of semiconductor tape-out pieces and the real electrical performance parameters of the semiconductor tape-out pieces; and inputting the training data into a preset electrical performance prediction model, and training the electrical performance prediction model by using a fusion loss function composed of a mean square error, a mean absolute error and a mean absolute percentage error to obtain the trained electrical performance prediction model. The method has the beneficial effect that the prediction accuracy and robustness of the electrical performance prediction model are improved.

The invention relates to a traffic flow prediction method based on compressed space attention and aggregation redistribution. A spatio-temporal embedding layer based on multi-scale fusion spatio-temporal information features is constructed, and multi-dimensional traffic flow data is received and flows into a spatio-temporal coding layer through the spatio-temporal embedding layer. In a space layer, compression space attention and space dependence attention are included, a method of compressing space information is adopted for compressing the space attention, and meanwhile detail enhancement is conducted on the space information. The spatial dependency attention extracts a dependency relationship based on a geographic position by introducing a mask matrix. In a time layer, feature extraction is carried out through fusion of time information and a multi-scale convolution kernel. And finally, splicing the two attention results of the space layer and the result of the time layer to integrate space and time information. According to the method, the spatial-temporal characteristics are combined, the traffic flow prediction result of the future time step is output, and the mean square error, the average absolute percentage error and the root-mean-square error of the scheme prediction result are all remarkably improved.

The application provides a laser cladding process molten pool morphology information prediction method and system based on VMD and improved DA-RNN, comprising the following steps: S1: continuously collecting molten pool morphology information in the molten pool dynamic video to obtain molten pool morphology information data; S2: pre-processing the molten pool morphology information data by VMD (variational mode decomposition) to decompose the original data into K different subsequences; S3: taking the decomposed different subsequences as data characteristic variables to construct a VMD data set, and dividing the VMD data set into a training set, a validation set and a test set; S4: establishing a DA-RNN prediction network model, and training and predicting on the data set; S5: quantitatively evaluating the DA-RNN prediction network model by using mean absolute percentage error and mean square error; and S6: predicting the laser cladding molten pool morphology information by using the prediction network model. The application can not only effectively extract data characteristics and reduce the complexity of data, but also effectively improve the molten pool morphology prediction model precision.