845 results about "Gradient boosting" patented technology

The present invention relates to a deep reinforcement learning-based cloud data center adaptive efficient resource allocation method. First, an operation (a scheduling job) is selected according to a score evaluated by critic (an evaluation operation) by using an actor parameterized policy (resource allocation). Then, a resource allocation policy is updated through gradient boosting, and a variance of a policy gradient is reduced by using an advantage function, to improve training efficiency; and wide simulation experiments are performed by using real data from a Google cloud data center. Compared with two advanced DRL-based cloud resource allocation methods and five classic cloud resource allocation methods, the method provided in the present invention has higher quality of service (QoS) in terms of a delay and a job dropout rate and has higher energy efficiency.

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 relates to the technical field of pipeline maintenance, and discloses a pipeline inner wall corrosion detection method and system suitable for hydraulic engineering, and the method comprises the steps: obtaining a plurality of sensor data from a sensor assembly arranged in a to-be-detected pipeline; performing feature extraction to obtain detection data features; performing time-space synchronization fusion to obtain fusion features; acquiring historical corrosion data and fusion features of the inner wall of the pipeline and inputting the historical corrosion data and the fusion features into the long-short-term memory network model to obtain a first corrosion prediction result; generating a second corrosion prediction result through a random forest algorithm; generating a third corrosion prediction result by a gradient boosting tree algorithm; and inputting the three prediction results into a preset detection model to obtain a detection report. According to the method, multiple sensor data are obtained and fused to obtain the fused features, multiple factors influencing corrosion are comprehensively referenced, prediction analysis is carried out through multiple algorithms according to the fused features to obtain three prediction results, the detection report is generated, and the accuracy of the detection result is effectively improved.

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 discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

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 artificial intelligence research, in particular to an intelligent desulfurization prediction system based on an FT-LSTM-XGBoost algorithm, which comprises a data acquisition module, a data preprocessing module, a feature engineering module, a model training module, a model fusion module and a control execution module, the data acquisition module monitors and acquires multi-dimensional parameters in a desulfurization process in real time; the data preprocessing module is used for preprocessing the acquired multi-dimensional parameters; the feature engineering module forms a final feature set related to desulfurization; the model training module adopts Fourier transform, a long-short-term memory network and a limit gradient boosting tree to train a desulfurization prediction model; the model fusion module obtains a fused prediction result; the control execution module is used for adjusting the overall operation parameters of the desulfurization system in real time; the method has high-precision prediction capability, so that the accurate control of the desulfurization effect is ensured.

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 discloses a new energy automobile charging cable aging performance test method and system, and relates to the technical field of cable aging detection, when the system operates, through comprehensive application of an environment acquisition module, a data processing module, a performance test module and an evaluation module, accurate evaluation and prediction of the cable aging performance are realized, and the cable aging performance is accurately evaluated and predicted. The environment acquisition module collects cable test data under temperature control, humidity control and water immersion conditions in real time, and the data processing module performs cleaning, standardization and feature extraction on the data to form a high-quality feature data set X. And the performance test module performs multi-model prediction by using a random forest, a gradient boosting tree and a deep neural network model, performs fitting through a meta-model, obtains a prediction result # imgabs0 # of a Stacking model, matches the prediction result # imgabs0 # with a preset evaluation threshold L, generates a cable aging performance test evaluation scheme, and performs cable aging performance test evaluation. The problems that a traditional method is slow in response, low in prediction precision and poor in adaptability are solved.

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 relates to the technical field of artificial intelligence fault diagnosis, and discloses an observation and aiming system fault prediction method based on IARO-GBDT, and the method comprises the steps: carrying out the preprocessing of collected data through an LPP algorithm, and screening the high-evaluation pin signal data as a model input data set; the artificial rabbit optimization algorithm ARO is improved, elite reverse learning is introduced in the ARO population initialization stage, and the fitness is calculated; an adaptive weight factor is introduced in an ARO circuitous foraging stage; t-distribution disturbance is introduced in the ARO random hiding stage to enhance the global search capability; and carrying out parameter optimization on the gradient boosting decision tree GBDT by adopting an improved artificial rabbit optimization algorithm IARO, constructing an IARO-GBDT fault diagnosis model, carrying out fault diagnosis on the observation and aiming system, and outputting a prediction result. According to the method, main parameters of the gradient boosting tree GBDT are optimized through the improved artificial rabbit optimization algorithm (IARO), the defect of blindness of parameter selection in the training process is overcome, and the prediction precision of the classification prediction model is improved.

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.

The invention discloses a chloride ion diffusion coefficient prediction method based on a hybrid machine learning model, and the method comprises the steps: widely collecting actual data, carrying out the preprocessing, carrying out the selection of parameters affecting a chloride ion diffusion coefficient through an LASSO regression feature selection method, and preventing the overfitting problem of a regression model on a training set; carrying out regression analysis on a plurality of parameters of the chloride ion diffusion coefficient on the training set by utilizing a distributed gradient boosting algorithm (LightGBM) based on a decision tree algorithm, taking data obtained by carrying out dimension raising processing on an obtained predicted value and an original parameter as input of GPR (Gaussian process regression), taking a true value as output, training a GPR model, analyzing a GPR prediction result, and obtaining a prediction result of the chloride ion diffusion coefficient. And a light GBM-GPR model is constructed by using a Bayesian parameter optimization method. According to the method, the characteristic that the LightGBM prediction error is small is reserved, the uncertainty probability interval is given, and the prediction capacity of the chloride ion diffusion coefficient is improved.

The invention discloses an aggregate three-dimensional shape detection method and system based on particle two-dimensional parameters, and relates to the technical field of aggregate detection and image processing. The method mainly comprises the following steps: acquiring two-dimensional form data of aggregate by adopting an aggregate image measurement system AIMS, acquiring three-dimensional form data of the aggregate by combining a three-dimensional blue light scanner and visual AVIZO software, and establishing association mapping of two-dimensional indexes and three-dimensional indexes by utilizing a gradient lifting regression tree method, so that rapid and accurate estimation of the three-dimensional form of the aggregate is realized. Fine management of mineral aggregate classification identification, storage and accurate calling can be realized, scientific support is provided for engineering design, and intelligent and automatic development of engineering management is promoted.

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 resource management, in particular to an operation resource allocation method and system, and the method comprises the following steps: integrating data of operation types, duration, equipment requirements and personnel requirements, combining the data with patient identity recognition information, creating an independent index, sorting the data into a queriable data set, and carrying out the data archiving processing, and obtaining a comprehensive resource data file. According to the method, dynamic adjustment of resources is effectively guided by learning historical data and predicting time distribution of equipment use frequency and personnel demands in resource management through a gradient elevator algorithm, the robustness and prediction accuracy of the model are improved through a random forest algorithm and an ensemble learning method, and the robustness and prediction accuracy of the model are improved. Therefore, the adjustment decision in the resource allocation process is more accurate, resource waste is reduced, the allocation efficiency of manpower and material resources is optimized, and the utilization rate of operation resources and the medical service quality are remarkably improved.

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 relates to the field of cold spraying, in particular to a cold spraying particle critical speed prediction method, electronic equipment and a medium. The cold spraying particle critical speed prediction method comprises the steps of inputting cold spraying particle sample data into a gradient lifting regression tree model and a correlation analysis model to obtain target features; the cold spraying particle sample data comprises cold spraying particle material parameters and critical speed; the target features are cold spraying particle material parameters input into the prediction model; inputting the target characteristics of the cold spraying particles to be measured into a critical speed prediction model to obtain the critical speed of the cold spraying particles; the critical speed prediction model is a neural network model optimized by adopting target features in cold spraying particle sample data, a genetic algorithm and a Levenberg-Marquardt back propagation algorithm. According to the method, the cold spraying ion critical speed prediction efficiency and reliability can be 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 hyperspectral technology-based inversion method for SPAD values and nitrogen concentrations of leaves in the whole growth period of rice. The hyperspectral data of the leaves of the rice are obtained by means of an ASD Field Spec4 portable ground object spectrometer in the initial differentiation period (PI), the heading period (HD), the middle filling period (MF) and the mature period (MA) of young ears; measuring the SPAD value of the rice as the chlorophyll content by using an SPAD-502 chlorophyll meter; determining the nitrogen concentration of the rice leaves by adopting a Kjeldahl method; performing correlation analysis on the hyperspectral data of the leaves, the nitrogen concentration and SPAD, and screening sensitive wavebands. According to the method, the hyperspectral characteristic wave band of the rice leaf is extracted through a correlation analysis method, so that the redundancy of full-spectrum data modeling is reduced, data are optimized, an inversion model of the nitrogen concentration and SPAD value of the rice leaf is constructed based on linear regression (LR), random forest (RF), support vector regression (SVR) and gradient boosting (GBRT), and the nitrogen concentration and SPAD value of the rice leaf are obtained. The optimal machine learning algorithm with high precision and good stability is obtained, and a scientific basis can be provided for non-destructive real-time leaf color monitoring and precise fertilization control of indica rice in the South China.

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 provides an adaptive causal discovery method and device based on a Monte Carlo tree, and belongs to the technical field of causal discovery data processing. Comprising the steps of obtaining an observation data set and a statistical information set, and dividing the observation data set into a training set and a verification set; constructing a causal discovery algorithm set A, and training the gradient boosting tree model by using the training set and the path combination to obtain an agent model Fb; establishing a Monte Carlo tree, selecting child nodes of root nodes according to performance expectation and exploration rewards, and expanding the child nodes to leaf nodes layer by layer; running the Monte Carlo tree, and obtaining a causal graph G * and a performance score by using a voting mode according to all causal relationships in the causal graph obtained by each node; calculating an exploration reward and a performance expectation of each node in a complete algorithm path, and returning the exploration reward and the performance expectation upwards to a root node from a leaf node along the path; updating Fb based on the searched path; and processing a causal discovery task by using a Monte Carlo tree and Fb, and finding a causal discovery algorithm path with the highest performance score.

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.