239 results about "Gradient boosting decision tree" patented technology

The invention provides an enterprise intelligent finance and taxation system fusing supervised learning and a block chain, and solves the core technical problems of insufficient data credibility, transparent contradiction between privacy protection and supervision, AI model training data island and the like of a traditional finance and taxation system. According to the system, an innovative technical fusion scheme is adopted, bank API, OCR recognition, voice input and other multi-source financial data are integrated through a multi-modal data fusion unit, and high-quality data fusion is achieved through confidence evaluation and an intelligent conflict resolution algorithm; an intelligent classification unit based on BERT deep learning integrates a pre-training model with a business rule engine and a gradient boosting decision tree, and accurate and automatic classification of financial transactions is achieved. A complete technical solution is provided for enterprise finance and taxation digital transformation, intelligent, automatic and credible processing of finance and taxation businesses is achieved on the premise that data safety and privacy protection are guaranteed, and the method has wide market application prospects.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention discloses a method for predicting the mechanical property of a dissimilar metal friction stir lap joint welded joint by fusing physical information, and belongs to the technical field of crossing of material processing and artificial intelligence. The method comprises the following steps that firstly, welding samples under different technological parameters are prepared through experiments, and the mechanical properties of the welding samples are tested; meanwhile, establishing a finite element simulation model, and extracting physical field data such as a temperature field and a stress-strain field; fusing the process parameters and the physical field data to serve as input features, taking the measured mechanical property data as output features, and forming a machine learning training data set; a prediction model is trained by adopting algorithms such as a gradient lifting decision tree and limit gradient lifting; and finally, new process parameters and physical field data obtained through finite element simulation are input, and the mechanical property of the joint can be efficiently and precisely predicted through the model. According to the method, the defects that a traditional trial and error method is high in cost and long in period are overcome, and key technical support is provided for optimization and intelligent design of the dissimilar metal friction stir lap welding process.

The invention discloses a river and lake backflow recognition and driving mechanism analysis method based on interpretable machine learning, and relates to the technical field of hydrology and water resource analysis and artificial intelligence application, and the method comprises the steps: collecting long-sequence hydrology data of a river and lake system; taking operation nodes of the large hydro-junction project as boundaries, and dividing the time sequence into different characteristic periods; constructing a multi-dimensional input feature set; a class weight balance strategy and a Bayesian optimization algorithm are adopted to train a backward flow recognition model based on a gradient lifting decision tree; a marginal contribution value of each hydrological driving factor is calculated by using an SHAP interpretability method, a physical threshold for inducing backward flow is identified by combining an SHAP dependency graph with a binary box plot, analysis results in different periods are compared, and an evolution rule of a backward flow driving mechanism is revealed. The method effectively solves the problems that a traditional method is difficult to capture nonlinear hydrological response and the black box model lacks physical mechanism explanation, and can accurately recognize the backward flow event.

The invention discloses a silicon nitride ceramic thermal conductivity prediction method based on machine learning coding-free feature processing. The method comprises the following steps: acquiring a data set; declaring a chemical character string field of the sintering aid as a disordered data type, and preprocessing other numerical characteristics at the same time; keeping the field of the sintering aid as a disordered data type, and preprocessing other numerical values; selecting a gradient lifting decision tree model supporting native processing category features as a prediction model and performing optimization; a to-be-predicted formula-process parameter combination is input into the optimized prediction model, a thermal conductivity prediction value is output in real time, and the to-be-predicted formula-process parameter combination is directly input into the model through an original chemical formula character string. The invention further discloses a device for implementing the method, computer equipment and a readable storage medium. According to the invention, the original chemical formula of the sintering aid can be directly utilized, and the thermal conductivity can be predicted within millisecond response time without any code conversion.

The invention provides a high geothermal exploitation land subsidence data monitoring method and device. The method comprises the following steps that S1, a monitoring area is determined, and monitoring points are arranged; s2, corresponding monitoring devices are installed at the monitoring points and debugged; s3, collecting multi-source original data; s4, preprocessing the multi-source original data; s5, constructing a collaborative fusion model of the long short-term memory network and the gradient boosting decision tree, and outputting a final fusion settlement amount; and S6, carrying out time sequence analysis, spatial distribution analysis and anomaly judgment. Through the full-process design of multi-source data acquisition, intelligent fusion modeling and dynamic analysis early warning, high-precision and dynamic settlement monitoring is achieved, the problems of precision limitation and response lag of a traditional monitoring method are solved, and key technical support is provided for safe and sustainable development of high geothermal resources.

The invention belongs to the field of agricultural technology and ecological monitoring, and particularly discloses a Hainan island wild tea tree growth model construction method and system.The method comprises the steps that firstly, a microenvironment competition factor is obtained by quantifying a local competition index based on diameter-level distribution in a fixed quadrat around a target tea tree; selecting a preset cubic function growth mechanism model to calculate a theoretical growth index value according to a vegetation vertical zone corresponding to the altitude of the tea tree; then fusing the local competition index, the theoretical growth index value, the altitude, the canopy density and the growth environment type to construct a feature vector so as to train a gradient lifting decision tree model, and establishing a multi-factor growth state prediction model; and finally, predicting the growth state of an unknown tea tree by using the model. According to the method, the ecological mechanism and machine learning are effectively fused, the growth prediction precision is remarkably improved, and a quantitative basis is provided for precise conservation.

The invention discloses an electric power marketing metering equipment abnormity early warning method and system based on edge computing, and relates to the technical field of electric power system monitoring, and the method comprises the steps: collecting the original multi-mode operation data flow of target electric power marketing metering equipment in real time during the operation of the system; performing sliding window segmentation and parallel feature extraction on the data stream to obtain a mixed feature vector; inputting the mixed feature vector into an isolated forest model and a gradient boosting decision tree model in an integrated anomaly detection engine, and calculating a comprehensive anomaly confidence score; generating a local early warning event or cache feature data according to the confidence score, and triggering an adaptive communication scheduling process; on the collaborative decision-making platform side, receiving and aggregating reported information, carrying out Bayesian network reasoning by combining the multi-source data of the power grid, and completing global anomaly verification and final decision-making; and starting a model evolution process according to a global verification result, executing incremental learning and generating a parameter updating package.

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 method and a system for presuming a correction coefficient of the strength of a standard test piece by adopting a small core sample test piece, and belongs to the technical field of concrete strength detection. The method solves the problems of large error and high discreteness of a presumption result caused by factors such as size effect, aggregate constraint and process difference when the standard strength is directly presumed by adopting the strength of a small core sample in the prior art, constructs a historical sample library containing multi-dimensional characteristics, calculates an initial correction coefficient and a size ratio, and obtains the presumption result of the standard strength. Key factors such as the small core sample size, the coarse aggregate particle size, the water-binder ratio, the curing age and the height-diameter ratio are comprehensively considered, an intelligent correction model is established by utilizing algorithms such as a gradient boosting decision tree and a random forest, and a comprehensive correction coefficient can be scientifically and accurately output, so that the actually measured strength of the small core sample is reliably converted into the strength of a standard test piece; and the presumption precision and the result stability are greatly improved, so that reliable technical support is provided for safety and durability evaluation of an engineering structure.

The invention relates to the technical field of data processing, and particularly provides a building thermal insulation performance evaluation method and system based on building material big data, and the method comprises the steps: obtaining building material data and building structure data of a building and meteorological data of a region where the building is located, and analyzing the influence of the building structure data and the meteorological data on the building material data; and after obtaining the structure influence data and the climate influence data, carrying out weighted fusion to obtain comprehensive influence data, inputting the comprehensive influence data into a preset thermal insulation performance evaluation model, and obtaining a current thermal insulation performance score of the building. Through generation of structure influence data and climate influence data, feature extraction and normalization, time dimension decomposition and correlation analysis, and weighted fusion, comprehensive influence data is formed, and the comprehensive influence data is input into a thermal insulation performance evaluation model based on a gradient boosting decision tree to obtain a thermal insulation performance score of a building. The problems of poor adaptability and low flexibility in the prior art are solved.

The invention relates to a safety monitoring method for an irrigation and drainage project operation state and an intelligent system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting and marking irrigation and drainage project operation monitoring data through a sensor, and constructing a training data set; completing data normalization by combining quantile and median robust scaling with adaptive nonlinear transformation, and mining and screening high-order interaction features by combining a mutual information theory and a gradient boosting decision tree; constructing a deep classification network fusing physical prior and adaptive feature interaction, introducing physical constraint and multi-scale feature fusion, and optimizing a model through adaptive marginal classification loss and physical feature manifold alignment loss; real-time data is preprocessed and then input into the model, and operation state grade classification and graded alarm are achieved. According to the method, data noise can be inhibited, a multi-index coupling relationship can be mined, the interpretability and robustness of the model can be improved by integrating a physical rule, irrigation and drainage project abnormity can be accurately identified and early warned, and the method is suitable for intelligent safety monitoring of an irrigation area.

The invention relates to the technical field of tea processing, and discloses a tea processing auxiliary method and system based on intelligent decision making. The method comprises the following steps: acquiring a multi-source sensor data stream of tea processing equipment in real time, and identifying a critical time point of processing state switching; intercepting time window data by taking a critical point as a center, performing empirical mode decomposition on channel data of each sensor, extracting an intrinsic mode function component, calculating a sample entropy value, and constructing a multi-channel entropy value characteristic matrix; inputting the matrix into a graph attention network, learning a spatial dependency relationship between sensor channels, and outputting a graph embedding representation of a processing state; calculating an abnormal score and generating a processing quality deviation index; analyzing the influence of processing parameter adjustment on a quality index by adopting a gradient lifting decision tree model, and determining the contribution weight of a key sensor channel; high-weight sensor data are integrated, dynamic segmentation is carried out, timing sequence characteristics are fused by applying a gating circulation unit network, and an accurate processing control decision is output.

The invention relates to the technical field of corrugated board production, and discloses a corrugated board production optimization method and system based on data mining and a medium. The method comprises the steps that corrugated board production full-link multi-dimensional data are collected and stored in a classified mode; performing cleaning, integration and feature engineering processing on the multi-dimensional data in sequence to obtain a standardized data set; key features and rules influencing quality and efficiency are mined based on the data set; constructing a quality and efficiency prediction model by adopting a gradient lifting decision tree algorithm; by taking model output as a constraint, optimizing through an NSGA-II algorithm to obtain an optimal process parameter, and issuing and executing the optimal process parameter; and acquiring an execution result in real time and comparing the execution result with a threshold value, and if the execution result exceeds the threshold value, triggering secondary optimization. Through data mining and closed-loop optimization, the problems of existing production parameter solidification and optimization passivity are solved, and the production quality and efficiency are improved.

The invention discloses a method and a device for predicting an anode copper refining reaction endpoint, and relates to the technical field of anode copper refining process management. The prediction method comprises the following steps: S1, sensor deployment and data acquisition; s2, data preprocessing; s3, constructing a feature project; s4, model training and optimization strategy implementation; s5, carrying out online prediction and decision support; and S6, continuously improving closed-loop feedback. According to the method, the gradient boosting decision tree, the LSTM + Attention hybrid architecture and the Stacking integration are adopted, and the data interpretability and the nonlinear fitting capability are considered; bayesian hyper-parameter optimization and an early stop method are combined to prevent overfitting, and the generalization performance of the model is ensured.

The invention discloses an abnormal transaction identification method and device based on edge node collaboration and electronic equipment, and relates to the technical field of distribution.The method comprises the steps that an encrypted transaction feature vector transmitted by an infrastructure end is received, a transaction request is generated based on the encrypted transaction feature vector, the transaction request is sent to a plurality of adjacent edge nodes, and the edge nodes send the transaction request to the infrastructure end; and receiving collaborative verification results of a plurality of adjacent edge nodes on the transaction request based on a preset consensus algorithm, performing abnormal transaction identification based on the collaborative verification results of all adjacent edge nodes by using a gradient boosting decision tree model, and executing the transaction request under the condition that the transaction identification result indicates that the transaction is a normal transaction. And a transaction execution result is fed back to the basic equipment end. According to the invention, the technical problem that the protection capability of a localized single-node detection system is easy to reduce for large-scale network attacks or fault conditions of individual nodes in the prior art is solved.

The invention discloses a hoisting equipment carbon footprint model construction and optimization method based on machine learning, and the method comprises the steps: collecting historical multi-dimensional working condition time sequence sample data and real-time energy consumption sample data, and carrying out the training through a gradient lifting decision tree algorithm, thereby obtaining a dynamic carbon footprint intensity prediction model; a dynamic carbon emission factor corresponding to the current operation state is output in real time; a digital twinborn simulation environment is constructed based on a dynamic carbon footprint intensity prediction model, under a given operation constraint condition, an optimization search algorithm is adopted to generate a plurality of candidate operation strategies, carbon footprint forward simulation is carried out, and an optimal energy-saving operation strategy is selected by comparing total prediction carbon footprints. And finally outputting to an equipment man-machine interaction interface or a control system. According to the invention, the conversion of carbon footprint accounting from a static macroscopic factor to a dynamic equipment exclusive factor is realized, a closed loop from accurate perception to optimization decision is constructed, and the problems of low accounting precision and disjunction of monitoring and optimization caused by the use of a fixed carbon emission factor are effectively solved.

The invention discloses a textile production quality control method, relates to the technical field of textile production, and solves the problem that it is difficult to design double algorithms to calculate initial warp and weft density, correct the initial warp and weft density in combination with yarn physical characteristics and environmental parameters, and improve the production efficiency. The technical problem of lack of construction of an exclusive regression model for effectively counteracting interference of yarn physical characteristics and environmental parameters is solved. Comprising the following steps: acquiring comprehensive production data, and generating a multi-dimensional feature map by using a multi-channel convolutional neural network; a Transform model is adopted to extract a yarn path, the initial warp and weft density is calculated by combining fast Fourier transform and local extremum detection double algorithms, the density is corrected by combining yarn physical characteristics and environmental parameters through an exclusive gradient promotion decision tree regression model, and a verification mechanism is introduced to guarantee the precision; and a density correlation type digital model is constructed to realize production data real-time mapping and parameter visual linkage debugging, and a full-link quality tracing system containing a unique tracing code is established.

The invention provides a method and a device for predicting the effluent concentration of pollutants in a constructed wetland based on machine learning, and the technical key points are as follows: constructing a multi-feature data set by collecting data including environment operation conditions, water quality parameters and wetland conditions of the constructed wetland; an initial constructed wetland water quality prediction model is constructed, and the processed multi-feature data set is trained through four single-output machine learning models of a random forest, gradient lifting, limit gradient lifting and a gradient lifting decision tree; a plurality of indexes are adopted to evaluate the prediction performance of the four single-output machine learning models on the test set, and an optimal single-output model with the optimal comprehensive performance of the indexes is obtained; establishing a multi-target joint prediction model of multi-output regression by using the optimal single-output model, and predicting the water quality index of the effluent of the constructed wetland; and sorting the importance of different target variables which are explained by the multi-target joint prediction model and are influenced by the multi-target joint prediction model through an SHAP method.

The invention provides a water vapor chromatography method, system and device based on machine learning, and a medium, and the method comprises the following steps: constructing a historical training sample set, and employing the training gradient of the historical training sample set to promote a decision tree model, and generating an inclined path wet delay prediction model; establishing a virtual site coordinate in a blank area which does not cover a global navigation satellite system site, constructing a virtual physical feature vector in combination with a digital elevation model and meteorological reanalysis data, and obtaining a virtual oblique path wet delay observation value through the prediction model; and combining a real observation value of a global navigation satellite system station with the virtual oblique path wet delay observation value, establishing an enhanced chromatography equation set, solving by adopting an algebraic reconstruction algorithm, and performing inversion to generate a three-dimensional water vapor density distribution field of the target monitoring area. By implementing the technical scheme provided by the invention, high-precision virtual observation data can be generated in an area with sparse observation stations or uneven signal path distribution, and the stability and spatial resolution of a three-dimensional water vapor inversion result are improved.

The application discloses an outdoor cleaning robot and a positioning state evaluation and control triggering method thereof, and the outdoor cleaning robot comprises the following steps: collecting original data of multiple sensors of the outdoor cleaning robot in real time; calculating four types of features, i.e., coverage, geometric quality, multi-source consistency residual and motion consistency, based on the original data of the multiple sensors and a prior global high-precision point cloud map, and constructing a comprehensive feature vector; inputting the comprehensive feature vector into a pre-trained ordered multi-classification gradient boosting decision tree positioning state evaluation model to output a positioning state grade; and finally generating a control triggering code for controlling behaviors of the outdoor cleaning robot according to the grade and related feature values and a preset rule. The application realizes accurate evaluation of the positioning state and automatically generates a hierarchical control triggering code, thereby effectively improving the operation safety and running reliability of the outdoor cleaning robot in a complex outdoor environment.

This invention relates to the field of image recognition and detection technology for foodborne pathogens, and discloses a method for identifying Listeria monocytogenes based on image recognition. The identification method includes: acquiring images of chromogenic culture medium plates and converting them to Lab and HSV color spaces; performing adaptive color normalization based on the background region of the culture medium; performing colony instance segmentation on the normalized image and extracting extended regions of interest; extracting saturation distribution sequences along radial rays and detecting halo transition patterns using first-order difference; statistically analyzing halo angle coverage and average transition amplitude, and combining halo quantization feature vectors; inputting the feature vectors into a gradient boosting decision tree classifier to output colony identification results. This invention solves the technical problems of unstable classifier discrimination boundaries caused by color shifts between different batches and the difficulty in detecting halos caused by weak lecithinase reactions.

The invention relates to an EDXRF spectrum intelligent analysis method, system and device and a medium. The method comprises the following steps: acquiring a digital pulse waveform, performing multi-dimensional feature extraction on the digital pulse waveform, and generating a standardized multi-dimensional feature vector; inputting the vector into a pre-trained gradient boosting decision tree model to obtain an event classification result of the current pulse event, taking the event classification result as an event classification judgment result of a preset decision object, and associating the digital pulse waveform to obtain a structured decision object; according to an event classification judgment result in the structured decision object, performing differentiation processing to obtain an effective X-ray photon energy value; and mapping the energy value to a corresponding energy spectrum channel according to an energy-channel mapping relation, and carrying out counting accumulation to generate a high-fidelity energy spectrum. According to the method, through multi-dimensional feature extraction and pre-training model reasoning, the accuracy of pulse signal processing and the fidelity of the energy spectrum are improved, and the precision and stability of EDXRF spectrum analysis in a complex detection scene are enhanced.

This application relates to the field of civil aviation optimization technology, and in particular to a dynamic fuel consumption optimization system for civil aircraft based on real-time route data. The system includes modules for data storage, input, multi-source information fusion and feature extraction, intelligent evaluation, adaptive decision support, and output response. The system extracts multi-dimensional features by fusing real-time route and QAR data, constructs a correlation dynamic evaluation model using gradient boosting decision trees, simultaneously calculates flight performance index, fuel efficiency index, and correlation score, and generates a structured report based on a decision rule base, including cost index adjustment suggestions, altitude change schemes, and expected fuel savings predictions. This application enables dynamic and quantitative evaluation of the correlation between route environment and fuel consumption, overcomes the lag of traditional planning, and achieves precise and personalized flight strategy optimization, resulting in significant benefits in reducing costs, improving on-time performance, and reducing emissions.

The invention discloses a power system load prediction method based on a big data decision tree algorithm, and the method comprises the steps: collecting original power load data, carrying out the cleaning and normalization processing of the original data, and obtaining a standardized training sample data set; on the basis of the training sample data set, initializing a gradient boosting decision tree model, and setting iteration times M, a learning rate and a loss function to carry out secondary model iteration training; and after secondary iteration is completed, taking the final model as a power system load prediction model, inputting an attribute vector at a to-be-predicted moment, outputting a corresponding load prediction value, collecting a large amount of historical power load data, learning and classifying the data by using a decision tree algorithm, establishing a decision tree model, and predicting future power loads. Compared with a traditional power system load prediction method, the method has higher accuracy and real-time performance, and can better adapt to complexity and uncertainty of the power market.

This invention relates to a modulation and coding scheme and a method, system, device, and medium for predicting spatial stream counts. The method includes: acquiring received signal strength indication data and preset threshold information between at least two access points and a site; determining the synchronous / asynchronous communication status between each access point based on the dynamic relationship between the received signal strength indication data and the preset threshold information; classifying signals transmitted from adjacent access points to the site as interference signals and signals transmitted from adjacent sites as ambient noise based on the synchronous / asynchronous communication status; calculating the signal-to-noise ratio (SNR) of the site based on the signal classification results; inputting the SNR, synchronous / asynchronous communication status, and access point transmit power into a gradient boosting decision tree model; and outputting the modulation and coding scheme and spatial stream count prediction results of the target access point through the gradient boosting decision tree model. This method significantly improves the MCS / NSS prediction accuracy of high-density WLANs.

This invention discloses a carbon emission prediction system and method for papermaking processes based on BO-GBDT. The system collects process parameters and energy consumption data during papermaking production, preprocesses the input data, constructs a feature set containing key influencing factors, and obtains a carbon emission dataset for model training. A Bayesian optimization algorithm is introduced to intelligently search for the optimal hyperparameters of four gradient boosting models, constructing a hyperparameter optimization space and setting an optimization objective function. Model performance is evaluated through cross-validation, and the optimal model is updated and selected. BO-GBDT is selected as the final prediction model. The trained model is then used to accurately predict carbon emissions from the papermaking process. By automatically optimizing the hyperparameters of the gradient boosting decision tree model using Bayesian optimization and combining it with multi-source data feature engineering, high-precision and high-efficiency prediction of carbon emissions from the papermaking process is achieved, providing an effective tool for carbon management in the production process.