583 results about "Ensemble learning" patented technology

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention discloses a shale gas well dessert prediction method and device based on integrated learning. The method comprises the steps of obtaining main control factors and feature data of a shale gas well to be predicted, and inputting the main control factors and the feature data into a trained dessert prediction model to obtain dessert prediction data; the model training process comprises the following steps: determining main control factors influencing the dessert according to the correlation between each parameter in the prediction data of the sampled shale gas well in the target area and the dessert; the prediction data comprises first data and second data, the first data comprises geological data, perforation data and oil and gas production data, and the second data comprises logging data and fracturing construction data; performing feature extraction on each parameter in the second data to obtain feature data; constructing a training data set according to the main control factors and the characteristic data of the shale gas well; and training the dessert prediction model based on the training data set. The accuracy of a prediction result can be improved, and fracturing design is effectively guided.

The invention discloses a stacking-based steel hot-rolled product mechanical property prediction method, and relates to the technical field of steel product quality prediction. The method comprises the following steps: constructing a steel hot rolling actual production data set and carrying out missing value interpolation on the data set; performing normalization processing on the complete data set, and dividing a part of data from the normalized data set as a training set; establishing a stacking ensemble learning model based on a regression chain; taking the yield strength, the tensile strength and the elongation in the training set as training labels, taking other data types as training features, and training the learning model by using the training set to obtain a steel hot-rolled product mechanical property prediction model; and the steel hot-rolled product mechanical property prediction model is practically applied to perform real-time prediction on the mechanical property of the steel hot-rolled product. According to the method, the coupling relation among the multiple target variables can be effectively processed, and the accuracy and stability of mechanical property prediction of the steel hot-rolled product can be effectively improved.

The invention discloses an underground water seepage analysis and prediction method based on physical-data cooperative driving, and belongs to the field of underground water seepage, and the method comprises the following steps: establishing a hydrogeological numerical model, carrying out seepage simulation calculation, and carrying out comparison verification with field monitoring data to realize accurate mapping; meanwhile, a machine learning data set is constructed by utilizing a numerical simulation result, a prediction model based on a Stacking integrated learning structure is established in combination with field data of a construction roadway, a water curtain layer and an oil storage cavern layer, and the water seepage amount or the underground water level after excavation of a rock mass in front of a tunnel face is predicted by taking geological, hydrological and construction parameters as input. According to the method, a physical mechanism and data driving are fused, the prediction precision and interpretability are remarkably improved, and a scientific basis is provided for cave depot project grouting and excavation optimization.

The invention discloses a method and system for improving the energy utilization rate by utilizing light storage resource prediction, and the method comprises the steps: collecting irradiance temperature historical output data and load consumption feature data through a distributed sensor and an edge gateway, processing time sequence features through a sequence prediction model, and integrating multi-dimensional input through the fusion of an integrated learning model; a future prediction result of photovoltaic output and load demand is obtained; further acquiring real-time parameters of the charge state and the charge and discharge efficiency of the energy storage equipment, constructing an energy storage full life cycle state evaluation model to calculate dynamic indexes, and determining an available capacity boundary; and if the boundary exceeds a preset threshold value, peak-valley period characteristics are extracted, and an optimal strategy sequence including photovoltaic peak charging and load peak discharging is generated by adopting an intelligent optimization algorithm in combination with power grid peak-valley electricity prices and carbon emission constraints. According to the invention, the photovoltaic consumption rate is obviously improved, the operation cost is reduced, the carbon emission requirement is met, and intelligent and efficient management of the optical storage system is realized.

The invention discloses a drainage basin monitoring network layout optimization method based on mutual information active learning. The method comprises the following steps: data processing; constructing an integrated learning prediction model composed of a plurality of LSTMs, initializing each LSTM model parameter by setting different random number seeds, and iteratively updating the model parameters; constructing a covariance matrix of prediction results of the potential monitoring points by using the trained integrated learning prediction model; on the basis of an active learning algorithm of mutual information maximization, mutual information gains of all candidate sites added into the current monitoring network are calculated, candidate points enabling the mutual information gains to be maximum are selected, and information value sorting is carried out on potential monitoring points according to the sizes of the mutual information gains; and outputting a monitoring station optimization suggestion list. According to the method, uncertainty is scientifically quantified by adopting an ensemble learning method, and global information value is evaluated by adopting a mutual information maximization strategy, so that point selection decision does not depend on subjective experience any more, but strict calculation based on data and information theory, and the scientificity of decision is remarkably improved.

The invention discloses a multi-model fusion process quality prediction method based on ensemble learning, and relates to the technical field of process industry quality prediction. The method comprises three stages of data preprocessing, model construction and model fusion. In the data preprocessing stage, cleaning, normalization and feature screening are performed on multi-process production data; in the model construction stage, an overall prediction model and a segmented prediction model are constructed respectively, the overall prediction model adopts RF, LightGBM and KNN algorithms, and the segmented prediction model adopts an LSTM-KAN combined neural network; in the model fusion stage, prediction results of the two models are fused through an XGBoost ensemble learning algorithm, and advantage complementation is achieved. According to the method, the quality prediction problem caused by high process coupling degree and remarkable nonlinear relation in complex process manufacturing is effectively solved, the prediction precision and generalization performance are improved, and the method is suitable for a multi-process complex production scene.

The invention discloses an intelligent illegal behavior identification method and system based on multi-algorithm fusion, and relates to the technical field of image analysis. The method comprises the steps of collecting a region image sequence, identifying real-time personnel density information, adjusting the scale of a laser curtain wall region based on the personnel density information, forming a self-adaptive monitoring boundary, when a laser sensor detects an intrusion event, recording the intrusion moment and intrusion characteristics, calling an intrusion region image set, and obtaining the intrusion region image sequence; and analyzing the intrusion area image set by adopting an ensemble learning-based violation behavior recognition network group to obtain an interactive violation behavior recognition result, fusing the interactive violation behavior recognition result with the intrusion characteristics, introducing personnel density information to carry out environment adaptive compensation calculation, and outputting a final violation behavior recognition result. According to the invention, through fusion of image analysis, laser triggering and environment perception, high-robustness and high-accuracy intelligent identification of illegal behaviors in scenes such as subway security check is realized.

The invention relates to the technical field of spectral analysis and biomedical detection, and discloses an SERS (Surface Enhanced Raman Scattering) spectrum quantitative detection method and system based on interpretable stacked ensemble learning. The method comprises the following steps: acquiring SERS spectral data of serum tumor marker standard substances with different concentration gradients; performing baseline correction and normalization preprocessing on the data, performing sparse feature selection by using an LASSO algorithm, and screening out key spectral features to construct a sample data set; constructing an interpretable stacking integration model, wherein the model adopts a base learner layer and a meta learner layer; training the model by using the training set, optimizing model hyper-parameters by using a cross validation strategy, and establishing a mapping relationship between spectral features and tumor marker concentrations; and collecting SERS spectral data of a to-be-detected serum sample, extracting key spectral features, inputting the key spectral features into the trained interpretable stacked integrated model, and outputting a concentration predicted value of the tumor marker in the to-be-detected serum sample. The method has the advantages of high precision, universality and molecular level interpretability.

The invention discloses a carbon emission model prediction cross validation and fusion optimization improvement system. The system comprises a model construction module which constructs a plurality of prediction models according to preprocessed historical energy consumption data and corresponding historical carbon emission data; the cross validation optimization module divides a training set and performs hyper-parameter optimization by applying a cross validation technology to obtain an optimal hyper-parameter set of each prediction model and each optimized prediction model; the model fusion module adopts Stacking ensemble learning to divide all the optimized prediction models into a base learner and a meta learner, the base learner trains a training set to obtain preliminary prediction carbon emission and combines the preliminary prediction carbon emission into a feature matrix, and the meta learner trains through the feature matrix; and dynamically adjusting the parameter configuration of the base learner and the meta learner by using a particle swarm optimization algorithm, and finally obtaining a prediction model after fusion optimization. According to the method, the carbon emission prediction precision is improved, and the carbon emission prediction accuracy and stability are improved.

The invention relates to the technical field of energy storage battery capacity, and discloses a liquid metal battery capacity prediction method, system and device based on a Stacking model and a medium, and the method comprises the steps: selecting a gradient boosting decision tree, a random forest and support vector regression as a base learner, and linear regression as a meta learner; constructing a stacking model through Stacking ensemble learning, and training the model by adopting a cross validation method to prevent overfitting; carrying out a liquid metal battery aging experiment to obtain historical capacity data; and inputting historical capacity data in a battery circulation process into the trained stack model, and predicting future capacity change. The method gives full play to the advantages of the selected basic model, effectively fuses the sensitivities of different models to the aging characteristics of the liquid metal battery, and comprehensively improves the accuracy of capacity prediction of the liquid metal battery through the comprehensive capture of the aging characteristics.

The invention discloses a cerebral hemorrhage patient tracheotomy risk prediction method and system based on machine learning, and the method comprises the steps: obtaining an initial clinical data set of a target patient, calculating laboratory inspection data according to a predefined rule, and constructing a composite physiological state index to generate a feature vector for prediction; inputting the feature vector for prediction into a risk prediction model pre-trained based on an ensemble learning algorithm to obtain a risk quantitative index; the model interpretation module generates an individualized prediction contribution decomposition result based on an SHAP value calculation framework, and explains the specific influence of each feature on the risk index; and finally comprehensively generating a risk prediction report. According to the method, the feature representation and model prediction capability is enhanced by constructing the composite indexes, and meanwhile, the decision process is transparent and credible by utilizing interpretability analysis, so that clinical risk assessment and decision support are effectively assisted.

The invention discloses a rainstorm flood risk early warning method and system based on machine learning and multi-source data fusion, and belongs to the technical field of flood prediction.The method comprises the steps that multi-source sample data is constructed based on flood disaster historical data; the multi-source sample data type comprises rainfall characteristic data, hydrological characteristic data, landform characteristic data, earth surface attribute characteristic data and social economic characteristic data; an XGBoost ensemble learning algorithm is adopted to construct a risk prediction model, and training and evaluation are carried out based on multi-source sample data; historical rainstorm flood event records are taken as labels in the training process; performing quantitative and application verification on the risk prediction model, and calibrating a risk level probability output by the risk prediction model based on a risk level distribution probability of historical disaster situation data; performing real-time estimation based on the optimized risk estimation model; and generating a spatial refined rainstorm flood risk grade early warning map in a future preset time period according to an estimation result in a rolling manner.

The invention relates to a multi-resolution geological data conversion method and system based on ensemble learning, and belongs to the technical field of geological information processing, and the conversion method comprises the steps: obtaining a low-resolution geological data set containing a target element, and a high-resolution geological data set not containing the target element; performing spatial grid aggregation processing on the high-resolution geological data set to generate a predictive variable matrix which is spatially aligned with the low-resolution geological data set; training a Stacking integrated regression model by taking the predictive variable matrix as input and a target element value in the low-resolution geological data set as an output target; inputting the high-resolution geological data set into the fusion prediction model, and outputting a preliminary prediction value of a target element; and performing spatial error correction on the target element preliminary prediction value based on the target element actual value of the low-resolution geological data set to generate corrected high-resolution target element data. According to the invention, multi-scale and multi-source heterogeneous geological data can be effectively integrated.

An airspace flow prediction method and device based on an ensemble learning algorithm are provided. The method includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node; and applying the predicted airspace flow of the node to manage navigation of traffic in the airspace network.

The invention discloses an ice-coated blade aeroelastic flutter and complete machine mistuning identification method based on multi-dimensional dynamic feature engineering, and belongs to the technical field of wind power. Comprising the steps of collecting original multivariable time series data, preprocessing the original multivariable time series data, performing feature extraction on the preprocessed original multivariable time series data from three dimensions of time domain, time-frequency domain wavelet energy and control system domain to obtain basic physical features, performing feature screening on the basic physical features based on a Gini index, and obtaining the basic physical features. The method comprises the following steps: obtaining a fault seed feature, constructing an interactive feature and an adaptive feature based on the fault seed feature, fusing the basic physical feature, the interactive feature and the adaptive feature into a high-dimensional feature set, and inputting the high-dimensional feature set into a weighted ensemble learning classifier to realize the identification of the ice-coated blade health, aeroelastic flutter and complete machine detuning state. According to the method, the problems of poor fault identification robustness and low precision of a traditional method are solved, and reliable guarantee is provided for safe and stable operation of the wind turbine generator under the icing working condition.

The invention relates to the technical field of financial credit data evaluation, and discloses a user credit evaluation method based on multi-source credit data. The method comprises the steps of collecting multi-source heterogeneous credit investigation data in real time through a preset API interface group, and performing fusion after cleaning and standardization to form a user credit investigation data cube; extracting static, behavior sequence, association network and time sequence evolution four-dimensional features based on the cube; processing corresponding features by using a gradient boosting tree, a graph neural network and a time sequence convolutional network sub-model by using a multi-modal integrated learning framework, and generating a basic credit score through adaptive weighted fusion; the score is dynamically calibrated in combination with the real-time data flow and the macroeconomic factor, and high-risk user judgment and early warning are carried out based on dynamically adjusted behavior permission parameters by utilizing a real-time risk early warning engine; and finally, model retraining is triggered based on historical and recent default rate differences. According to the method, comprehensive, accurate and dynamic credit assessment is realized, and the assessment accuracy and timeliness are remarkably improved.

The invention relates to the technical field of remote sensing information, and discloses a corn fine classification method and system based on multi-source time sequence remote sensing image feature fusion, and the method comprises the steps: obtaining a multi-source remote sensing image and digital elevation model data of a target region; arranging corn and non-corn sample points in the target area, calculating multi-temporal vegetation indexes, texture features and gradient features based on the preprocessed remote sensing image, and constructing time sequence features; carrying out feature selection on the constructed time sequence features by utilizing importance screening and high-correlation feature redundancy elimination to obtain a feature subset; fusing the final feature subset with the gradient features to form a final optimized feature vector of each sample point; and adopting a Boosting ensemble learning algorithm, taking the optimized feature vector and the sample label as input, training a corn classification model, and predicting classification. The method has the advantage that high-precision automatic identification and classification of corn crops in a large-scale farmland can be realized.

The invention discloses a rockburst grade prediction method based on high-order feature decoupling and multi-scale integrated learning, and belongs to the technical field of geotechnical engineering. The method comprises the following steps: acquiring original features, and performing multi-dimensional feature engineering on the original features to generate an extended feature set containing high-order information; a plurality of data scalers such as StandScaler, Robust Scaler and MinMaxScaler are adopted to carry out parallel processing on the extended feature set, and a plurality of scaled data sets with different scales are obtained; respectively training corresponding base learners by using the data sets; and after executing the same processing flow on a to-be-tested sample, inputting the to-be-tested sample into a plurality of trained base learners, and outputting a final prediction level by adopting a hard voting integration strategy. According to the method, through deep feature mining and multi-scale integrated learning, the accuracy and robustness of the prediction model are effectively improved, and meanwhile, the transparency and credibility of the model decision process are enhanced through quantitative interpretation.

Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

The invention discloses an intelligent avalanche susceptibility assessment method fused with a deep residual network, and relates to the technical field of geological disaster risk assessment. According to the method, the spatial precision is high, the 12.5 m high-resolution DEM and 10 multi-source evaluation factors are adopted, the influence of the microtopography on the stability of accumulated snow can be accurately recognized, and the avalanche point hit rate reaches 82.5% and is far higher than that of a traditional method (generally lt: 70%); the model is excellent in performance, the interpretability of a shallow model is reserved through a two-stage framework of an integrated learning primary model and a residual network, nonlinear interaction among factors is captured through a deep network, and AUC of ResMLP is equal to 0.8496 and is improved by more than or equal to 4% compared with the primary model; the method is high in generalization stability, the prediction variance is reduced by about 12% on the premise that the training cost is not increased by introducing a TTA mechanism with Gaussian noise added, the method is particularly suitable for a high-altitude small sample area, and the overfitting problem is effectively relieved.

The invention relates to the technical field of grouting control, in particular to an intelligent grouting control method and system.The intelligent grouting control method comprises the steps that multi-parameter time sequence data in the grouting process is collected in real time, and standardization processing is conducted to construct feature vectors; inputting the feature vectors into an integrated learning model, and synchronously executing grouting effect prediction based on time sequence analysis and abnormal working condition identification based on data distribution analysis; when an abnormal working condition is recognized, a control strategy corresponding to the abnormity is executed preferentially; when abnormity is not recognized, the control parameters are dynamically optimized according to the predicted grouting effect; issuing the control strategy or the optimization control parameter to grouting equipment for execution, and collecting feedback data after execution; on the basis of feedback data, online incremental learning is carried out on the integrated learning model, model parameters are adaptively optimized, prediction can be carried out in advance, collected data and final control form a closed loop, meanwhile, the method can also adapt to complex working conditions, and the intelligent grouting control efficiency is improved.

The invention discloses an intelligent grading method and system for tunnel face surrounding rock. According to the method, a two-dimensional image, a three-dimensional point cloud and environmental mechanics data of a tunnel face are synchronously collected through a sensor array integrated on tunneling equipment; after space-time registration fusion is carried out on the multi-source data, pixel-level identification and quantitative extraction of geological features are carried out by using a deep learning model, and three-dimensional reconstruction is carried out based on a motion recovery structure algorithm to obtain rock mass structural plane occurrence parameters; visual, geometric and environmental characteristics are fused, and accurate prediction of the surrounding rock grade is realized through an integrated learning model; and finally, dynamic excavation numerical simulation is carried out by combining the three-dimensional geologic model and a prediction result, the surrounding rock stability is analyzed, and safety early warning is generated. According to the method, automation and quantification of the whole process of surrounding rock grading are achieved, and the accuracy and timeliness of tunnel construction safety decision making are effectively improved.

The application discloses an operation and maintenance data anomaly detection device and method and a storage medium. An acquisition module acquires operation and maintenance data. A detection module is connected with the acquisition module and is used for receiving the operation and maintenance data of the acquisition module. According to the type of the operation and maintenance data, an anomaly detection algorithm is used to perform anomaly detection processing on the operation and maintenance data to obtain an anomaly detection result. According to the type of the operation and maintenance data, the operation and maintenance data is predicted to obtain a prediction result. The operation and maintenance data including indexes, logs and call chains can be uniformly accessed to the detection module. The interface calling and the input mode of the operation and maintenance data are unified. The anomaly detection processing and the prediction processing are uniformly performed through the detection module. The integration is not needed additionally. The output mode of the result is unified. The convenience is improved. In addition, the anomaly detection algorithm uses unsupervised learning and integrated learning voting. The cumbersome work of labeling and the reuse according to different specific businesses are avoided. The applicability is improved.

The present application relates to the technical field of ship transportation prediction, and is a ship transportation time intelligent prediction method and system based on multi-source data fusion.The method comprises the following steps: obtaining ship historical data, collecting weather data, tidal current data and AIS historical trajectory data respectively; extracting weather features of a route based on space-time weighting, and constructing a weather feature vector; extracting tidal current features of the route based on multi-site weighted fusion, and constructing a tidal current feature vector; extracting ship historical portrait features based on AIS trajectory, and constructing a ship historical portrait feature vector; constructing a sailing time prediction model based on an XGBoost model, and training the sailing time prediction model by using time sequence sliding window cross-validation; constructing an anchorage waiting time prediction model based on a Stacking ensemble learning algorithm; performing timing prediction service by using the sailing time prediction model and the anchorage waiting time prediction model, and calculating the total transportation time of the ship according to the prediction result.

The application discloses a layout hotspot detection method based on geometric feature analysis, and the implementation steps are as follows: an under-sampling and over-sampling method is used to generate a layout sample set; the number of corner points, short-circuit sensitivity and open-circuit sensitivity of the layout sample are extracted to form a feature vector of the layout sample; an integrated learning model is trained by using a feature vector training set; and the feature vector test set is input into the trained integrated learning model to output a detection result. The application overcomes the problems in the prior art that the imbalance of layout category samples leads to reduced model detection precision, and that the recognition rate of a layout mode that has never been seen before is low and the false positive rate is high. Geometric features are extracted for photolithography hotspots in the layout, so that the application can maintain high detection precision and low false positive rate when detecting a layout that has never been seen before and a complex layout mode.

The application belongs to the technical field of material performance prediction and uncertainty analysis, and proposes a beryllium aluminum alloy performance uncertainty quantification method based on Bayesian ensemble learning, which is innovative in constructing and training multiple independent performance prediction models to form the basis of ensemble learning. After the training of each model is completed, a performance prediction value can be output for a new input sample. After obtaining the prediction outputs of multiple independent models, a Bayesian fusion method is used to comprehensively process the prediction results on the probability level to obtain the fused performance prediction distribution; and the complete results of the beryllium aluminum alloy performance prediction are output in a clear, intuitive and convenient engineering application format. The application has the advantages that the robustness and generalization ability of the prediction results are improved, a decision basis is provided for material performance evaluation, and good adaptability is achieved for the case of limited data quantity, and important innovations are achieved in the aspects of material performance uncertainty quantification theory and engineering application.

Provided is an ensemble learning (EL)-based speaker verification method. The method includes: data acquisition and preprocessing; selecting and training a group of basic models, and optimizing model parameters; performing similarity scoring on an acquired pair of speaker feature embedding via the group of basic models; constructing a detection cost function (DCF); generating a weight, and performing weighted fusion on scoring results of the group of basic models based on the weight, to obtain a final ensemble model for speaker verification; based on a near-speaking or far-speaking test scenario of a voice sample, the scenario is distinguished and input into the ensemble model, to obtain a final similarity score after weighted fusion; and determining, based on a threshold, whether there is a same speaker, where if the similarity score is greater than the threshold, it is determined that there is a same speaker.

The application belongs to the technical field of medical image processing and diagnosis, and particularly relates to an improved RCN-based alopecia typing and grading intelligent identification method and system, wherein alopecia image data is acquired, a U-Net++ model is trained, hair probability distribution is obtained, a hair mask is generated, an alopecia region is segmented, and continuous frame images are generated through SIFT feature matching and affine transformation; spatiotemporal joint features are extracted by using an improved RCN network, combined with a ResNet-50 backbone network, to generate alopecia local feature maps, spatiotemporal self-attention encoders and double-path attention architectures are fused, through a dynamic weight distribution mechanism, key hair growth regions are located and hair growth directions are analyzed, and density change trends are obtained; through an ensemble learning model, alopecia density and grade are identified, health is scored, and a heat map is generated to label hair follicle atrophy regions. Thus, the problems of weak feature expression ability, poor scene adaptability and insufficient positioning accuracy in the prior art are solved.

The invention discloses a high-precision tin-based soldering lug component optimization method and system for microelectronic interconnection based on machine learning, and belongs to the technical field of material design and artificial intelligence. According to the method, a multi-dimensional feature database of alloy components and performance indexes is constructed; machine learning ensemble learning is adopted to train a performance prediction model, and SHAP interpretability analysis is introduced to quantify the contribution degree of each alloy element to the performance; on the basis, a multi-objective optimization model is established, and optimal Sn-Ag-Cu-X multi-component alloy components are intelligently recommended by taking oxidation resistance, wettability, cost and the like as constraints. According to the method, through a closed-loop optimization framework of calculation design, experimental verification and model iteration, rapid, accurate and low-cost design of tin-based soldering lug components is achieved, the research and development period is remarkably shortened, and the bottleneck problem of a traditional trial and error method in multi-element alloy system optimization is solved.