717 results about "Gradient boosting" patented technology

The invention discloses a data security and privacy management method and system based on a block chain technology, and the method comprises the following steps: S1, collecting original data in a target system, and carrying out the preprocessing of the original data; s2, constructing an access control strategy set; s3, converting into a structured strategy configuration file, and deploying a smart contract; s4, matching and judging the access request and a preset strategy by the intelligent contract; s5, judging whether the access request is legal or not; s6, performing structured processing on the access log, and writing the access log into a block chain account book; s7, intelligently selecting an encryption mechanism, and providing access return data to the access party; and S8, carrying out modeling prediction on the access mode by adopting a federal gradient lifting tree algorithm. The block chain intelligent contract and the federal gradient boosting tree algorithm are fused, differential privacy, zero-knowledge proof and homomorphic encryption technologies are combined, data intelligence and refined privacy protection are realized, and the method has the advantages of high credibility, high intelligence and high traceability.

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention relates to the crossing field of material design and artificial intelligence, in particular to a solid electrolyte intelligent inverse design method fusing a graph neural network and confidence analysis. According to the method, a prediction framework integrating multiple models is constructed, support vector regression, gradient boosting regression, a deep neural network and a graph neural network are included, component, process and structure parameter characteristics are fully fused, and the nonlinear mapping relation between input variables and performance parameters such as resistivity and conductivity is efficiently learned. In order to improve the credibility, a Bayesian neural network and a Monte Carlo method are further introduced, a confidence interval corresponding to each group of prediction results is output, and quantitative evaluation of the credibility of the prediction value is realized. In the inverse design module, high-dimensional submerged space parameters are generated based on a variational auto-encoder, and intelligent recommendation of parameter combination driven by target performance is realized in combination with strategies such as Bayesian optimization and a genetic algorithm. The design efficiency of the solid electrolyte and the success rate of material discovery can be effectively improved.

The invention belongs to the technical field of artificial intelligence, and discloses a PCB use fault early warning system based on artificial intelligence, which comprises a data acquisition module, a data processing module, an intelligent analysis module, a decision level fusion module, a self-adaptive modeling module, a multi-model cooperation module and a fault early warning module. The intelligent analysis module realizes double breakthrough of nonlinear feature capture and adaptive anomaly discrimination ability through a dynamic error threshold mechanism of an LSTM time sequence prediction engine and a depth autoencoder; an XGBoost-1DCNN hybrid classifier is constructed, a gradient boosting tree and multi-scale convolution features are fused in fault mode recognition, and the complex fault classification precision is remarkably improved; and the decision level fusion module constructs a multi-model decision conflict resolution mechanism based on an improved D-S evidence theory, and realizes great optimization of a false alarm rate through a confidence interval dynamic synthesis algorithm, thereby forming a closed-loop system with real-time response, multi-dimensional root cause analysis and intelligent hierarchical early warning.

The invention relates to the technical field of artificial intelligence and insurance, and discloses an intelligent insurance decision core system based on a dynamic dialogue strategy. The system comprises a user interaction module which collects user risk attribute data through voice and text multi-mode input; the insurance product database is used for storing multi-source insurance product structured data; and the intelligent decision-making module is used for generating personalized insurance suggestions by applying a random forest, a gradient boosting tree and other multi-model fusion technologies. A dynamic dialogue strategy engine is further arranged, and a dynamic interaction sequence is generated based on context-aware reinforcement learning. The data synchronization method of the system can obtain latest insurance product data, the multi-model fusion technology improves the decision accuracy, the dynamic dialogue strategy realizes intelligent interaction, the problems of incomplete information collection, inaccurate decision, inflexible interaction and the like in the traditional insurance decision process can be effectively solved, and more efficient and personalized insurance decision service is provided for users.

The invention relates to the technical field of internet risk control management, and discloses an internet risk control optimization method based on multi-dimensional data analysis, which comprises the following steps: acquiring internet user behavior data, transaction data, equipment information and environment data in real time, and carrying out data preprocessing on the data to generate a sample data set; key risk control features are extracted from the sample data set based on a feature importance analysis strategy, and a feature subset is generated; performing parameter optimization on the integrated gradient boosting tree and the deep learning network by using an improved sparrow search algorithm, and obtaining a risk control model after optimization; and inputting the feature subset into a risk control model for analysis, outputting a risk score, and generating a risk control decision in combination with a dynamic threshold adjustment strategy. According to the method, the model iteration period is shortened, the training convergence speed is improved, the overall prediction performance and generalization ability of the risk control model are improved, and more reliable technical guarantee is provided for risk identification.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

A machine learning model, such as a Gradient Boosting Machine model, is trained using historical data associated with different types of operations. The machine learning model extracts features from the historical data and learns which features result in a predefined outcome, such as approval by a predefined entity. After the machine learning model is trained and validated as being accurate, it is used to predict the likelihood of a prospective operation achieving the predefined outcome when submitted right away versus a future date. If the likelihood of achieving the predefined outcome is low if submitted right away, the submission of the prospective operation is temporarily suspended. The machine learning model calculates a future time during which to submit the prospective operation for approval, where the future time has a greater likelihood of achieving the predefined outcome. This reduces waste of electronic resources including computer processing power and network communication bandwidth.

The invention relates to the technical field of case retrieval and analysis, in particular to a method and a system for retrieving and analyzing unhealthy asset cases. The method comprises the following steps of performing cleaning, standardization and feature extraction on collected enterprise financial data, market information and historical cases; performing primary screening on the cleaned structured data based on a dynamic weight logistic regression rule, performing feature extraction on the high-risk candidate case set to obtain structured features # imgabs0 # of the high-risk candidate case set, and constructing a multi-layer knowledge graph based on an SPO triple; taking the spliced mixed feature # imgabs1 # as the input of a graph enhanced mixed gradient lifting model, and obtaining a finely screened high-risk case; and a progressive training strategy is adopted to train the graph enhanced mixed gradient lifting model, and the contribution of the structured features and the graph features to the prediction result is balanced. A multi-layer knowledge graph with space-time attributes introduced is constructed, the risk propagation weight is dynamically calculated in combination with a space-time diagram attention network, and the dynamism and accuracy of risk path identification are improved.

The invention discloses an intelligent injection molding product quality prediction method, which comprises the steps of collecting product structure information, mold parameters and injection molding equipment operation parameters, and constructing a scientific mold testing process window based on simulation analysis; designing an orthogonal experiment scheme to generate a simulation data set, and forming a multi-source data training sample in combination with field test model data; training a quality prediction model by using a distributed gradient boosting tree model, inputting process parameters, and outputting quality indexes such as product gram weight, volume shrinkage and buckling deformation; the model interpretability is realized through a shop value analysis method, and the influence weight of the process parameters on the quality indexes is evaluated; the model is deployed in a production control system or a cloud platform, the product quality is predicted in real time, a dynamic feedback mechanism is established, and when the error between actual detection data and a predicted value exceeds a threshold value, model retraining and parameter correction are automatically triggered to form closed-loop control. The method effectively improves the accuracy, transparency and engineering adaptability of quality prediction of the injection molding product, and can be popularized.

The invention discloses a data security protection method and system combined with big data analysis, and relates to the field of data security protection, and the method comprises the steps: building a multi-dimensional data collection and fusion data set according to API access records, external threat intelligence and context metadata association data; based on an isolated forest algorithm, calculating a sample path length to evaluate a behavior anomaly probability score; risk indexes of external threats, data sensitivity and permission exceptions are quantified respectively; constructing a data security risk comprehensive assessment model based on a weighted summation algorithm, and updating and optimizing the weight in the model by using a gradient boosting tree algorithm; and according to an output result of the data security risk comprehensive assessment model, setting a security risk level, and according to the security risk level, dynamically responding to a protection measure. The method has the advantages that continuous and dynamic risk assessment and automatic response to third-party data access behaviors are realized by fusing multi-source data and an intelligent algorithm.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses a machine learning assisted polyethylene reaction performance prediction model training method, prediction method and device. The method comprises the following steps: acquiring a training set; screening feature items used for model training; obtaining a gradient boosting regression model for catalytic activity, a gradient boosting regression model for molecular weight and a gradient boosting regression model for molecular weight distribution; extracting feature items for model training from the data of the training set so as to obtain feature vectors; and respectively inputting the feature vectors into each model so as to train each model, thereby respectively obtaining hyper-parameters of the trained gradient-boosted regression model for catalytic activity, hyper-parameters of the trained gradient-boosted regression model for molecular weight and hyper-parameters of the trained gradient-boosted regression model for molecular weight distribution. According to the method, a model relationship between input characteristics and polymerization results (including catalytic activity, molecular weight, molecular weight distribution and the like) is established through training set learning.

The invention belongs to the technical field of hyperspectral imaging and machine learning, and discloses a plant carotenoid rapid determination method based on hyperspectrum and machine learning. The measuring system comprises a hyperspectral imaging device, a spectrum preprocessing module, a characteristic wavelength extraction module, a machine learning modeling module and a carotenoid content prediction module which are sequentially connected through a data processing unit. According to the method, spectral data of a plant sample is obtained in combination with a hyperspectral imaging technology, key wavelengths are screened through spectrum preprocessing and a feature extraction algorithm, and a carotenoid content prediction model is established by using machine learning models such as random forest regression, partial least squares regression or a gradient elevator. According to the method, rapid and nondestructive detection of the plant carotenoid can be realized without a chemical reagent, the determination precision and efficiency are improved, and the defects of complex operation, long time consumption and strong destructiveness of a traditional detection method are overcome.

The invention discloses an emission reduction system for chloride ions in waste incineration fly ash based on machine learning, relates to the technical field of solid waste resourceful treatment, is used for data-driven fly ash multi-stage dosing collaborative removal, and is divided into fly ash pretreatment and data acquisition, online ion monitoring and dosing decision, multi-stage reaction and dynamic feedback, OTA upgrading and cross-plant migration. And removing the multi-dimensional pollutants synergistically. The dosing ratio and the reaction stage are optimized in real time through a gradient boosting tree-reinforcement learning model, and the concentrations of chloride ions, heavy metals and dioxin precursors are monitored on line. According to the system, monitoring and decision-making paths of all stages are stored in a cloud knowledge graph and can be upgraded through OTA, the chloride ion removal rate is increased by more than three percent, the agent dosage is reduced by 25%, the scheme can adapt to the difference of fly ash components in different factories, economy and environmental protection are both considered, and the system has remarkable popularization value in large-scale waste incineration and hazardous waste disposal.

The invention discloses an English writing grammar error detection system based on deep learning. The system comprises an original data acquisition module, a data preliminary processing module, a grammar error positioning model construction module, a grammar error classification model construction module and a grammar error detection module. The invention relates to the technical field of text grammar error detection, in particular to an English writing grammar error detection system based on deep learning. A data preliminary processing method of text data trimming, text vectorization, data standardization and data set segmentation is adopted; a lightweight gradient elevator model is adopted as a grammar error positioning model, and the error positioning precision is improved through adaptive fusion of rule conflicts and statistical mode anomalies; a transformer model is adopted as a grammar error classification model, and classification accuracy is improved by modeling dependency relationships among words and introducing graph structure constraints.

The invention discloses a water supply system scheduling optimization method and device, electronic equipment and a storage medium, and relates to the field of intelligent scheduling of water supply systems. The method comprises the steps of collecting and preprocessing multi-dimensional data to obtain a data set; the pressure data is analyzed from the space-time dimension, and the unfavorable points and the pressure requirements thereof are accurately identified; the method comprises the following steps: constructing a water volume prediction model by adopting a time sequence model, and constructing a total water head difference prediction model by adopting a Light GBM gradient boosting tree in combination with a MultiOutputRegressor multi-output regression framework; constructing a minimum total water production cost objective function based on a prediction result, and outputting an optimal scheduling scheme by combining water volume and pressure constraint iterative optimization; and establishing a model updating mechanism to ensure dynamic adaptation of the strategy. According to the method, the problems of insufficient pressure guarantee, extensive cost control and weak model practicability and generalization ability are solved, the inherent contradiction that a traditional mechanism model is high in complexity and a pure data driving pressure prediction model is poor in generalization and lacks physical significance is overcome, and safe, stable and efficient intelligent technical support is provided for a water supply system.

The invention relates to the technical field of charging infrastructure management, and particularly discloses a charging pile appointment scheduling and path planning cooperation system and a charging pile appointment scheduling and path planning cooperation method. According to the method, continuous wavelet transform and fast Fourier transform are adopted to extract a charging rate fluctuation characteristic value and a power grid power supply stability characteristic value for quantitatively evaluating the service stability of a charging pile and the power supply reliability of a regional power grid, and the system fuses the characteristic values into a comprehensive characteristic vector as the input of a gradient boosting tree model so as to obtain a gradient boosting tree model; and training the prediction model to output a charging planning score so as to judge whether the user can smoothly complete the charging task, and if a prediction result does not meet a set threshold value, dynamically adjusting a reservation scheduling strategy and re-planning an optimal charging path.

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 belongs to the technical field of power distribution networks, and discloses a load prediction and early warning method and system for a transformer area containing distributed resources, and the method comprises the steps: combining a two-dimensional time sequence data sample set of a high-risk transformer area with a transformer area feature operation data set according to the transformer area and a timestamp, and generating a prediction model training sample data set; building a lightweight gradient boosting tree as a main prediction model, inputting a training sample data set for training, and optimizing hyper-parameters of the main prediction model by adopting a Bayesian optimization algorithm; and establishing a residual error correction model based on local weighted Gaussian process regression, superposing a load prediction result of a prediction day of the main prediction model of the to-be-predicted transformer area with a residual error correction value of a prediction day of the residual error correction model to obtain a final load prediction result, and outputting transformer area weight / overload early warning information. According to the method, the LGBM is adopted as the main prediction model for load prediction, the residual error correction model is adopted for residual error correction, and the robustness and adaptability of the model are improved.

The invention discloses a memory anomaly prediction method based on multi-algorithm decision, and belongs to the technical field of fault prediction. The method comprises the steps that data information generated in the running process of a server is obtained, preprocessing and feature extraction are conducted on error data, a feature set is obtained, and the feature set comprises counting dimension features, component dimension features, statistical dimension features and equipment configuration dimension features; the feature set is input into a trained basic prediction model, a first prediction probability, a second prediction probability and a third prediction probability are obtained, and the basic prediction model comprises a random forest sub-model, a lightweight gradient elevator sub-model and an extreme gradient lifting sub-model; obtaining a first prediction probability value based on the first prediction probability, the second prediction probability and the third prediction probability; inputting the feature set into a depth time sequence extraction network, and obtaining target features based on a feature masking method; inputting the target features into a gating circulation unit to obtain a fourth prediction probability value; obtaining a decision matrix based on the first prediction probability value and the fourth prediction probability value; and inputting the decision matrix into the trained decision optimizer logistic regression model to obtain a memory anomaly prediction result. According to the method, the accuracy and reliability of memory anomaly prediction are improved.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention discloses a machine learning-based low-voltage transformer area electric leakage risk intelligent identification method and system. The identification method comprises the following steps of 1, multi-dimensional electric leakage feature construction and transformer area information aggregation analysis; 2, dynamically evaluating the electric leakage risk and adaptively adjusting a threshold value; and step 3, an electric leakage type intelligent identification and confidence degree determination mechanism. The electric leakage risk identification and classification method has the beneficial effects that by introducing a graph nerve enhanced gradient boosting tree model (G-GTBoost) and an improved residual error convolution-time sequence neural network (Res-CNN-LSTM) model and cooperating with a multi-modal feature fusion and dynamic threshold adjustment mechanism, the performance is remarkably improved in electric leakage risk identification and classification.

The invention relates to the technical field of water quality prediction, in particular to a river water quality prediction method and system based on a machine learning coupling hydrological model, and the method comprises the following steps: obtaining a data set, and dividing the data set into a training set and a test set; constructing a limit gradient lifting model, and performing hyper-parameter optimization on the model; performing correlation analysis on the water quality parameter set and the water quality index WQI value based on the trained limit gradient lifting model, and screening to obtain key water quality parameters influencing the water quality index WQI value; constructing an LSTM model and a soil and water evaluation tool model; simulating a future hydrological water quality process based on the soil and water evaluation tool model, and calculating and outputting a future water quality parameter result; and inputting a future water quality parameter result into the trained LSTM-WQI model to predict a future river WQI value so as to obtain a prediction result. According to the invention, the machine learning algorithm is coupled with the hydrological model to construct the water quality evaluation model which is convenient to use and adapts to local conditions, and efficient and accurate prediction of future water quality is realized.

Systems and methods disclose herein procedures for accelerated tree learning. In one class, the acceleration is based on self-adapting learning rates, while in another class, the acceleration is based on a plurality of learning rates, wherein each learning rate varies over the training; each learning rate increases linearly as a respective pseudo residual maintains a direction across sequential training iterations; and each learning rate decreases exponentially as the respective pseudo residual changes direction across sequential training iterations. The latter can be incorporated with other methodologies, such as momentum-augmented gradient boosting and Nesterov Accelerated Gradient Boosting. These systems and methods for accelerated tree learning exhibit a marked reduction in training time and resources required for gradient boosted trees.

The invention discloses a laser radar-assisted wind power generation wind shear modeling method, and relates to the technical field of wind shear modeling, and the method comprises the steps: collecting the wind speed measurement data of a laser radar at a plurality of heights in a target fan region, and constructing a measurement volume integral operator matrix; constructing a unit induction field correction model based on large eddy simulation in combination with a gradient boosting tree; coupling the measurement volume integral operator with the unit induction field correction model, and obtaining a high-precision wind speed vertical section and a corresponding wind shear parameter through inversion; on the basis of the high-precision wind speed vertical section and the corresponding wind shear parameters, physical constraints are fused, a wind shear model is generated, and uncertainty evaluation is carried out; according to the wind shear modeling method, the problems that the measurement error of the laser radar is difficult to correct, the wind shear modeling lacks the physical constraint and the result credibility cannot be quantified are solved by constructing the volume measurement integral operator and the unit induction field correction model for coupling inversion and fusing the physical constraint and the Bayesian uncertainty evaluation method.