205 results about "Extreme gradient boosting" patented technology

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.

The invention provides a highway tunnel decision-making maintenance system and a maintenance method thereof. The method comprises the steps of obtaining tunnel static and dynamic data and tunnel surrounding geological environment data; according to the geological environment data, carrying out geological risk analysis by adopting a geological statistical simulation algorithm and a time sequence decomposition algorithm to obtain a safety risk assessment result of the tunnel structure influenced by geological factors and an underground water dynamic change trend prediction result; according to the geological risk analysis result, fusing the tunnel technical condition data by adopting a heterogeneous integration model of a dynamic Bayesian network and an extreme gradient boosting tree, predicting the tunnel technical condition, and generating maintenance decision parameters; optimizing the maintenance decision parameters by adopting a cuckoo search algorithm so as to minimize the maintenance cost under the influence of the geological risk; and outputting decision results including the total maintenance cost distribution based on the geological risk, the technical condition improvement expectation, the rock stratum stability safety factor prediction value and the underground water seepage risk early warning level.

The invention discloses a wire harness product quality prediction system based on big data. An initial multi-source data set is acquired; core features in the initial multi-source data set are extracted based on the crimping height, the insulation resistance value and the environment temperature and humidity of the wire harness quality, high-weight features in the core features are screened through a PCA principal component analysis method, and wire harness feature data are obtained; processing time series data based on a long-short-term memory network, performing feature selection by using an extreme gradient boosting tree, and establishing a hybrid prediction model; using an improved IWOA whale optimization algorithm to optimize hyper-parameters of the hybrid prediction model; and inputting the wire harness characteristic data into the target hybrid prediction model for prediction, outputting a quality risk grade index, and if the quality risk grade index exceeds a set threshold, triggering an early warning signal. The limitation of traditional single data or simple model prediction is changed, so that quality prediction better fits an actual production scene, and the accuracy and reliability of prediction are remarkably improved.

The invention discloses a heart failure patient remote monitoring and early warning system based on the Internet of Things, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source physiological and behavior data, and generating an input sample set; the feature evaluation module is used for constructing an extreme gradient boosting tree algorithm model and obtaining feature importance scores; the feature screening module is used for executing a recursive feature elimination process and outputting an optimal feature subset; the risk judgment module is used for inputting the optimal feature subset into an extreme gradient boosting tree algorithm model to generate a heart failure risk score; the early warning triggering module is used for triggering early warning and sending early warning information to the patient and the platform; the feedback recording module is used for generating feedback information; and the dynamic optimization module is used for updating the input sample set based on the feedback information, retraining the extreme gradient boosting tree algorithm model and screening the features. According to the method, the extreme gradient boosting tree and recursive feature elimination are fused, and accurate evaluation and dynamic early warning of the multi-source data of the heart failure patient are realized.

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 an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

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 provides a crossing container truck flow prediction method, a crossing container truck flow prediction system, a medium and a crossing container truck management method, which are applied to the technical field of automatic container wharfs, and are used for dynamically adapting to a wharf operation scene through a data modal classification model, and performing parallel prediction in combination with a long short-term memory network (LSTM) regression model and an extreme gradient boost (XGBoost) regression model. And the prediction results are integrated in a weighted addition mode, so that the container truck flow of the wharf crossing in each time period in the future preset time period is accurately predicted, the problem of low wharf container transfer efficiency is effectively solved, a basis is provided for operation management of the wharf, resource allocation optimization, congestion reduction and operation cost reduction are facilitated, and the wharf transportation efficiency is improved. Therefore, the overall operation efficiency and the service quality of the wharf are obviously improved.

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.

The invention discloses a power system inertia estimation method based on feature extraction and regression learning separation, and the method comprises the following steps: S1, building a power system frequency response model SFR, and generating data for simulating the operation of a power system; s2, extracting characteristics of frequency deviation and power disturbance of the power system by using a convolutional neural network (CNN); s3, using an activation value extraction function to extract advanced features from a full connection layer of the convolutional neural network CNN as input of an extreme gradient boosting tree XGBoost, using a bird mating optimizer to optimize hyper-parameters of the extreme gradient boosting tree XGBoost, performing regression learning on the model, and outputting an inertia estimation value; according to the method, data features are extracted by using the powerful feature extraction capability of a convolutional neural network CNN, then the data features are processed by using the efficient regression learning capability of an optimized extreme gradient boosting tree XGBoost, and based on time sequence data of frequency deviation and power disturbance, through a mode of feature extraction and regression learning separation, the frequency deviation and the power disturbance are calculated. The inertia of the system can be accurately estimated.

The invention discloses an idle land recognition method, device and equipment and a storage medium, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining a first land feature set based on the land text data of a plurality of pieces of land; screening the first land feature set to obtain a second land feature set; semi-supervised learning cooperative training is carried out on a first classifier and a second classifier based on the second land feature set, the first classifier and the second classifier are used for idle land identification, the first classifier is realized based on a rotating forest algorithm, and the second classifier is realized based on an extreme gradient lifting algorithm; wherein in the semi-supervised learning cooperative training process, the prediction labels are screened based on a preset idle land classification rule. The method can realize accurate and efficient idle land identification.

The invention relates to the technical field of artificial intelligence fault diagnosis, and discloses a vehicle body gyroscope group fault prediction method based on IAOA-XGBoost, which comprises the following steps: carrying out dimensionality reduction and normalization preprocessing on acquired data through a KPCA algorithm, introducing a local chaotic mapping strategy in an AOA initialization stage to expand the range of an initial candidate solution position, and carrying out fault prediction on the initial candidate solution position; the method comprises the following steps: introducing a non-inertial control factor into a mathematical function accelerator of the AOA for improvement, judging whether the current AOA is in an exploration stage or a development stage according to the improved mathematical function accelerator, and updating a candidate solution position; and performing parameter optimization on the extreme gradient boosting tree XGBoost by adopting an improved arithmetic optimization algorithm IAOA, constructing an IAOA-XGBoost fault diagnosis model, inputting the test data set into the IAOA-XGBoost fault diagnosis model, performing fault diagnosis on the vehicle body speed gyroscope, and outputting a prediction result. According to the method, the defect of blindness of parameter selection in the training process is overcome, the prediction precision of the regression prediction model is improved, and higher prediction precision and practicability are achieved.

The invention discloses a remote driving risk early warning method based on communication time delay perception, which comprises the following steps: firstly, collecting related data of a target communication link, carrying out preprocessing and feature extraction on the data, fitting a nonlinear relationship between time delay and multiple features based on an extreme gradient lifting XGboost algorithm, and predicting a change condition of the time delay in real time; constructing a communication risk evaluation model, and quantifying a communication risk value based on the time delay change condition; predicting a vehicle trajectory according to the vehicle dynamics model and the driver execution instruction sequence; calculating a lane departure risk and an obstacle collision risk based on the vehicle trajectory, the environmental risk value being the sum of the two; and comprehensively considering an environment risk value and a communication risk value, and judging whether to trigger an alarm according to a designed safety threshold. According to the method, an environment risk evaluation mode is redesigned on the basis of considering the communication time delay, and the environment risk and the communication risk are comprehensively incorporated into an early warning framework, so that better safety guarantee is provided for a remote driving system.

The invention discloses a risk indicator construction method and system for new energy output prediction, and belongs to the technical field of data processing and power system management, and the method comprises the steps: obtaining the preprocessing data of a plurality of new energy stations, carrying out the spatial-temporal feature analysis, and generating a spatial-temporal feature set; obtaining meteorological prediction data, and performing medium and long term output prediction through an extreme gradient boosting tree based on the space-time feature set; comparing the output prediction result with historical real output data to obtain a prediction error, and performing probability coupling modeling and quantile regression prediction in combination with a meteorological element evolution sequence to generate a dynamic quantile value; and determining a dynamic risk interval, and constructing a graded early warning index. According to the method, probabilistic coupling modeling and quantile regression prediction are adopted, prediction errors are deeply associated with dynamic evolution of meteorological elements, predicted uncertain risks can be quantized and graded, and decision support is provided for optimal scheduling and risk management of a power grid.

The application provides a stone composition multi-modal prediction method and system based on urine analysis, and belongs to the technical field of medical detection and artificial intelligence. The method comprises the following steps: acquiring a multi-modal data set of urine of a stone patient; identifying a crystal type; generating a metabolic feature matrix; extracting features from the crystal type identification result to generate a crystal feature vector; performing standardization processing on the metabolic feature matrix and the crystal feature vector to obtain a fusion feature matrix; inputting the fusion feature matrix into an extreme gradient boosting tree model to output an initial probability of a stone composition; inputting the initial probability of the stone composition into a bidirectional long short-term memory network model to output a probability distribution of the stone composition, and completing stone composition prediction. The application breaks through the limitations of traditional technologies in terms of accuracy, efficiency, cost, applicability and the like, and provides a feasible solution for early diagnosis, personalized treatment and popularization of primary medical treatment of urinary stones.

The invention provides a stone component multi-modal prediction method and system based on urine analysis, and belongs to the technical field of medical detection and artificial intelligence crossing. The method comprises the following steps: acquiring a multi-modal data set of urine of a stone patient; identifying the crystal type; generating a metabolic characteristic matrix; performing feature extraction on the crystal type identification result to generate a crystal feature vector; standardizing the metabolism characteristic matrix and the crystallization characteristic vector to obtain a fusion characteristic matrix; inputting the fusion feature matrix into an extreme gradient boosting tree model, and outputting the initial probability of the calculus component; and inputting the initial probability of the calculus component into the bidirectional long-short-term memory network model, and outputting probability distribution of the calculus component to complete calculus component prediction. The method breaks through the limitation of the traditional technology in the aspects of accuracy, efficiency, cost, applicability and the like, and provides a feasible solution for early diagnosis, personalized treatment and basic medical popularization of urinary calculi.

The invention discloses an NEP estimation method based on feature selection and machine learning, and belongs to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: obtaining remote sensing image data and environment factors of a target region, and carrying out the preprocessing of the obtained remote sensing image data, and obtaining remote sensing factors; based on the remote sensing factor and the environmental factor, obtaining a net ecosystem productivity NEP feature influencing multi-source data fusion of the target area, performing screening, obtaining a preset number of key factors, and performing training through an extreme gradient lifting regression algorithm; analyzing the training result of the extreme gradient lifting regression algorithm by adopting a Shapril addition interpretation method; the extreme gradient lifting regression algorithm is verified through the evaluation indexes, and target area NEP estimation is completed; according to the method, high-precision estimation and driving mechanism analysis of the NEP of the complex mountain ecosystem are realized by fusing multi-source data and a machine learning technology, and clear mechanism explanation is provided for ecological research.

The invention discloses a soil texture remote sensing mapping method based on time window screening. The method comprises the following steps: step 1, collecting and testing a soil sample; 2, screening a remote sensing image time window; step 3, acquiring and preprocessing an environment covariable; 4, constructing a multi-source data set; 5, constructing a soil texture prediction model; 6, multi-temporal remote sensing covariable combination optimization modeling is carried out; and step 7, soil texture mapping based on the optimal multi-temporal remote sensing combination. And fusing the multi-source environment covariable and the multi-temporal remote sensing data, and constructing a soil texture prediction model based on the optimal multi-temporal model combination by adopting an extreme gradient lifting algorithm. The problems of insufficient representativeness and weak generalization ability of single-time-phase remote sensing data are solved. On one hand, the generalization ability of the model is improved, and on the other hand, quantitative analysis of the key environmental factors is realized in combination with the SHAP technology, the interpretability of the model is enhanced, and meanwhile, the soil texture prediction precision is effectively improved.

A computer-implemented system for short-term stock price prediction, consisting of: • a data acquisition module (101) configured to retrieve historical intraday stock data including closing prices, trading volumes and technical indicators; • a data preprocessing module (102) configured to normalize the data and generate temporally consecutive samples using a sliding window approach; • a neural network module (103) comprising a Gated Recurrent Unit (GRU) architecture configured to receive the temporally consecutive samples and output intermediate stock price predictions; • a gradient boosting module (104) comprising an Extreme Gradient Boosting (XGBoost) regressor configured to receive the output of the GRU module along with associated residual features and to refine the predictions by modeling nonlinear patterns and residual errors; • and an output module (105) configured to present the final refined stock price prediction, where the GRU module is trained to minimize a loss function for the mean squared error, and the XGBoost module is trained to minimize an objective function for the squared error, thereby providing improved predictive accuracy for financial time series forecasts.

The invention provides an equipment quality state evaluation method fusing data driving and QMU, and belongs to the technical field of equipment evaluation and detection. The method comprises the steps of collecting test data and task environment data of equipment to construct a data set, and dividing the data set into a training set and a test set; based on the data set, importance calculation is carried out on the multi-dimensional features through a limit gradient boosting tree, the features meeting the equipment quality state contribution degree requirement are screened out, and an observation list of the data set is generated; based on the observation list of the training set, a Gaussian process regression model is established, and hyper-parameters of the Gaussian process regression model are optimized; predicting the equipment state by using a Gaussian process regression model, performing uncertainty quantization on a prediction result, and outputting a mean value and a variance of an equipment state prediction value; on the basis of the performance threshold PTL or PTU of the equipment, a predicted value of the margin is calculated in combination with the average value of equipment state prediction; and calculating a confidence coefficient CF based on the variance of the predicted value and the predicted value of the margin, and affirming the quality state of the equipment.

The invention relates to the field of laser welding monitoring, and provides a laser welding penetration state prediction method based on a multi-modal composite machine learning model, which comprises the following steps: S1, acquiring a multi-modal signal in a laser welding process by multiple sensors; s2, carrying out preprocessing on the multi-mode signal; s3, establishing a multi-modal data set based on the preprocessed data, and dividing the multi-modal data set into a training set and a test set; and S4, establishing a machine learning model of a composite neural network (NN) and an Extreme Gradient Boosting (XGBoost) algorithm, training on the training set, and verifying the prediction performance of the model on the laser welding penetration state on the test set. The invention aims to fully fuse different modal information in the laser welding process and combine the advantages of different machine learning algorithms to improve the accuracy and robustness of penetration state monitoring.

The invention provides a loan risk assessment method which can be applied to the technical field of artificial intelligence. The method comprises the steps that under the condition of user authorization, structured data and time series data of a user are acquired, the structured data are identity attributes, financial qualification and loan application information of the user, and the time series data are historical credit behavior data of the user; inputting the structured data into a preset random forest model, and outputting a first predicted value; inputting the structured data into a preset limit gradient lifting model, and outputting a second predicted value; inputting the time sequence data into a preset long-short-term memory network model, and outputting a third predicted value; performing weighted fusion on the first predicted value, the second predicted value and the third predicted value to obtain a combined predicted value; and outputting a final loan risk assessment result according to the joint prediction value. The invention further provides a loan risk assessment device and equipment, a storage medium and a program product.

The invention discloses a spiral anchor bearing capacity prediction method based on machine learning, and solves the problems that in the prior art, numerical analysis consumes long time, actuarial precision is limited by precise description of soil constitutive, the operation difficulty is high, and large-scale application is difficult. According to the method, methods of algorithm and database establishment, model training and hyper-parameter optimization, model evaluation, characteristic important analysis and the like are adopted, an ensemble learning algorithm with high popularity is utilized to improve the limit gradient to establish a screw anchor bearing capacity prediction model, and the screw anchor bearing capacity is rapidly and accurately predicted based on a machine learning model.

The invention provides a population migration flow flow prediction method and system. The method comprises the following steps: step 1, constructing an improved depth gravity model; step 2, combining the improved depth gravitation model with an XGBoost model to construct an XGBoost-depth gravitation model; 3, constructing an index system influencing population migration and flow; 4, taking each influence factor in the index system as an original feature, and performing feature selection on the original feature to obtain a main influence factor; step 5, carrying out model training, inspection and screening on the XGBoost-depth gravitation model; and step 6, on the basis of the XGBoost-depth gravitation model, population migration flow prediction is carried out. According to the method, a limit gradient decision tree (XGBoost) is introduced on the basis of improvement of a deep gravitation model, important indexes are extracted through feature optimization, and prediction of population migration flow scale, flow direction and flow network structure is carried out in combination with key variables, so that model errors can be effectively reduced, and model prediction precision is improved.

The invention provides a deep learning-dominated stacked machine learning and deep learning electricity stealing detection method and system, and the method comprises the following steps: S1, obtaining a data set, and carrying out the cleaning of the data set; s2, carrying out normalization processing on the cleaned data; s3, dividing the obtained preprocessed data set into a training set and a test set; s4, balancing the two sample categories by adopting an SMOTE + Tomek-Links comprehensive sampling method for training a subsequent electricity larceny detection model; s5, constructing and training an electricity stealing detection model; the electricity stealing detection model comprises a deep learning feature extraction module, a support vector machine SVM, a random forest RF, a gradient boosting decision tree GBDT and an extreme gradient boosting tree XGBoost; and S6, inputting to-be-detected data into the trained electricity larceny detection model to obtain a detection result. According to the invention, the SMOTE + Tomek-Links is used for processing the unbalance degree problem of data, and the pre-classification output of machine learning is stacked through the feature extraction module of deep learning to serve as the input of XGBoost, so that the precision of electricity stealing behavior detection can be improved.

The invention provides a machine learning method for predicting compressive strength of various fiber reinforced concrete, relates to the technical field of civil engineering material and artificial intelligence crossing, and constructs a high-precision compressive strength prediction model by fusing limit gradient boost (XGBoost) and a bald eagle search optimization algorithm (BES) and combining k-means + + clustering and five-fold cross validation technology. The method comprises the following steps: firstly, clustering and partitioning a data set by using k-means + +, and ensuring that a training set and a test set are consistent in distribution; a BES algorithm is adopted to optimize hyper-parameters (including the number of iterations, the tree depth and the learning rate) of XGBoost, and input data are normalized to improve convergence efficiency. And finally verifying the performance of the model through evaluation indexes (R2, RMSE, MAE and the like). According to the method, the problem of insufficient prediction precision of a traditional method under the multi-type fiber feature coupling effect is solved, the generalization ability and robustness of compressive strength prediction are remarkably improved, and a reliable basis is provided for concrete mix proportion design and engineering application.

The present application relates to a kind of carbon emission space refinement simulation and driving mechanism analysis method based on multi-source data and machine learning, including obtaining night light remote sensing, population density data, multi-source data such as interest point density, nonlinear spatial fitting model is constructed and trained by using information entropy weight method to carry out multi-source data fusion and construct economic energy comprehensive index, use city land use classification data as industry space restriction, combined with the total carbon emission of each industry calculated to build carbon emission space simulation base map, based on extreme gradient boosting tree, introduce SHAP framework for post-analysis, from global, local and spatial three scales, the nonlinear driving mechanism of carbon emission of multi-source city form element is analyzed, overcome the over-saturation defect of single night light data in city core area, realize the leap from pure space fitting to mechanism explanation, provide scientific basis and method support for high-density city accurate locking emission hotspot and making low-carbon policy.

The present invention relates to a method for predicting the phosphorus removal performance of machine learning-driven metal-modified biochar, comprising the following steps: obtaining the original data under the loading of Mg or La on the biochar to form a data set, the data set including input features and output variables; performing data preprocessing and input feature analysis on the data set; dividing the data set into a training set and a test set, training six machine learning models including random forest (RF), gradient boosting (GBR), extreme gradient boosting (XGB), support vector machine (SVM), ridge regression, and artificial neural network (ANN), and determining the training model after evaluation; performing hyperparameter optimization on the training model through Bayesian optimization combined with five-fold cross-validation to obtain the optimized training model; using the optimized training model to predict the phosphorus removal performance of the metal-modified biochar, and predicting the corresponding preparation process parameters with the adsorption capacity and the remaining phosphate concentration as the targets, providing a new idea for the directional application of the biochar preparation process under different water quality management objectives.