37 results about "Ensemble prediction" patented technology

The invention relates to the technical field of water conservancy projects and emergency management, and discloses a cascade reservoir group flood emergency scheduling method and system fused with dynamic risk assessment, and the method comprises the steps: a dynamic risk assessment stage, employing an ensemble forecast product of numerical weather forecast to drive a hydrological model to generate a flood set, calculating the instantaneous dam overtopping risk probability of each reservoir, and calculating the flood overtopping risk probability of each reservoir; a risk chain type propagation network model is constructed to evaluate the overall accident probability of the system; and the intelligent decision support stage is triggered when the overall failure probability of the system exceeds a risk threshold value, similar historical cases are retrieved by adopting a graph neural network based on the water conservancy field knowledge graph, and an optimal disposal scheme is generated and recommended through a multi-attribute utility evaluation model. According to the method, dynamic quantification of risks, accurate identification of systematic risks and intelligence of emergency decision making are achieved, the problems that traditional risk assessment is static and isolated, and emergency decision making depends on experience are solved, and the flood control emergency response capacity of the cascade reservoir group is improved.

A model weight mixing-based online knowledge distillation method and system, comprising: 1) the server collects labeled data, which is divided into a training set and a validation set; 2) select N student models with the same architecture, and construct a weight mixing model through linear weighting of student parameters; 3) select data augmentation to expand the training set into N+1 parts, which are respectively input into N student models and the weight mixing model to obtain N+1 predicted outputs and their average ensemble prediction; 4) calculate the classification loss of each student and the weight mixing model; 5) calculate the knowledge distillation loss between the output of each student and the ensemble prediction; 6) calculate the parameter optimization target of each student and update the parameters of the N students; 7) repeat steps 3-6, after each round, reconstruct the mixed weight model according to the updated student, and every fixed round Δ, fuse the parameters of the mixed weight model into the student; 8) select the model with the best performance on the validation set and deploy it to the terminal device.

The invention discloses a reservoir group risk adaptive scheduling method and system for coupling ensemble forecast uncertainty, and the method comprises the steps: fusing monitoring and meteorological ensemble forecast data in real time, and outputting an initialization field through assimilation processing; a complete scene set is generated through parallel hydrological-hydrodynamic simulation, and a dynamic risk probability field is calculated and clustered to obtain a weighted typical scene set; then, a rolling optimization model containing dynamic probability constraints is constructed, and an optimal instant scheduling instruction is solved and output; and updating data in a rolling manner according to a fixed period and repeating the process. The system adopts a modular design and comprises a data synchronization assimilation module, a parallel simulation and risk quantification module, a rolling optimization decision module, an instruction execution and feedback control module and a system support layer. According to the invention, the scheduling strategy adapts to the risk dynamic change in real time, the safety and benefit are balanced, the scheduling intelligence and standardization level are improved, and the engineering potential safety hazard is reduced.

The invention discloses a photovoltaic snow covering depth and power loss forecasting method and system based on multi-source data fusion, and belongs to the technical field of photovoltaic power forecasting. The problems of high-precision accumulated snow influence assessment, short-term risk early warning and power loss quantitative prediction of a photovoltaic system in cold and alpine regions are solved. The method comprises the following steps: selecting three numerical weather forecasting modes, namely a global forecasting system operated by the National Environmental Forecasting Center, a wind energy and solar energy forecasting system of the China Meteorological Administration and a unified mode weather forecast of the British Meteorological Administration, acquiring weather forecasting data, performing unified space-time interpolation, abnormal value correction and unit conversion, and then performing set averaging to obtain a set average value; ensemble forecast data is obtained; based on a temperature-irradiance condition and a friction force mechanism, an accumulated snow covering model is constructed, a photovoltaic panel snow covering range and photovoltaic power loss are obtained, early warning information of snow covering time, duration and power loss is generated through matching of station longitude and latitude and national grid forecasting, and photovoltaic snow covering depth and power loss forecasting based on multi-source data fusion is completed.

This invention discloses a multi-scale power prediction and uncertainty quantification method for photovoltaic-storage-charging microgrids, belonging to the field of power dispatching technology. The method includes: acquiring historical operational data and meteorological data of the photovoltaic-storage-charging microgrid; extracting different time-frequency features through a multi-scale decomposition algorithm; establishing a nonlinear mapping relationship through a deep neural network prediction model to determine the optimal network structure and hyperparameter combination; quantifying prediction uncertainty using a Bayesian deep learning framework to obtain prediction confidence intervals; training multiple base learners with different structures and obtaining ensemble prediction results through an ensemble learning algorithm; and obtaining a power prediction solution for the photovoltaic-storage-charging microgrid by constructing prediction intervals and combining them with point prediction accuracy indicators. This invention achieves high-precision power prediction and accurate uncertainty quantification for photovoltaic-storage-charging microgrids, providing reliable decision support for the safe and stable operation of distribution networks.

This invention discloses a classification method for ultra-high carbon steel microcrystals based on spatial attention and ensemble prediction, relating to the field of metallic materials technology. The method includes: acquiring K improved EfficientNet-B7 models; these K improved EfficientNet-B7 models are obtained by training with K-fold cross-validation based on the initial improved EfficientNet-B7 model structure; the initial improved EfficientNet-B7 model adds a spatial attention module between the output of the sixth feature extraction stage and the input of the seventh feature extraction stage; classifying the microcrystal image of ultra-high carbon steel to be classified using each improved EfficientNet-B7 model to obtain the probability distribution of each category; arithmetically averaging the multiple probability distributions predicted by the multiple improved EfficientNet-B7 models to obtain a fused probability vector; and selecting the category corresponding to the maximum probability in the fused probability vector as the category of the microcrystal image to be classified. This method improves the classification accuracy of ultra-high carbon steel microcrystal structures.

The application discloses a cold vortex path set prediction method and device, relates to the technical field of meteorological data processing, and comprises the following steps: determining the real-time position of a cold vortex based on the latest zero field data of a global model; selecting N member paths with the minimum path error in each member cold vortex path of set prediction to perform arithmetic averaging, so as to obtain a corrected path, wherein the path error is the distance between the current time cold vortex center position of set prediction and the real-time position; obtaining the prediction data of the center position and path of the cold vortex of all prediction members of set prediction, and performing path clustering through a DSBCAN algorithm. Cold vortex path prediction products are determined based on path clustering and the corrected path, and objective and accurate prediction results can be obtained based on the products combined with the results after the 500-hPa situation field of an initial date is clustered. Further, the defects in the prior art are solved.

The present application relates to an interpretable and machine learning-based climate model preferably combined with a collection estimation method, belonging to the technical field of climate models, the method comprising: obtaining area-weighted average ozone column total of reanalysis data and CMIP6 model data, and constructing a data set; based on the data set, respectively adopting a traditional method and a decision tree SHAP method to screen the CMIP6 model; based on the CMIP6 model data screened by the traditional method and the decision tree SHAP method, a machine learning model is constructed; based on the constructed machine learning model, the optimal screening method and the corresponding optimal machine learning model are determined; based on the optimal screening method and the corresponding optimal machine learning model, the Antarctic ozone under different greenhouse gas emission scenarios is estimated. Through the present application, more accurate Antarctic ozone recovery estimation results can be obtained.

The invention discloses a cascade hydropower station reservoir runoff prediction and optimal scheduling method and system based on land-gas coupling, and the method comprises the steps: fusing satellite remote sensing, glacier quality change, numerical weather forecast and other multi-source data through data assimilation, and generating a meteorological and hydrological ensemble forecast; inputting the ensemble forecast into the graph neural network model, and outputting short-term runoff probability distribution and a middle-term expectation sequence; a hierarchical optimization scheduling strategy is adopted; a two-stage stochastic programming model is established based on probability distribution to formulate a water level datum line; in actual operation, forecast distribution is updated based on latest observation data, and a distributed robust optimization model is adopted to generate a robust real-time scheduling instruction in a rolling manner; integrally calibrating parameters of the prediction model and the decision model on line through a variational inference method; according to the method, full-process closed loop from multi-source data fusion and probabilistic prediction to robust decision and adaptive learning is realized, and the precision and physical rationality of runoff forecasting and the comprehensive benefit and robustness of scheduling decision are improved.

The invention discloses a cloud analysis and random perturbation combined method for regional convective scale ensemble forecast in the technical field of meteorological data processing, and the method comprises the steps: obtaining cloud analysis data, such as blackbody brightness temperature, total cloud amount and radar three-dimensional networking reflectivity, of a monitoring region and other data needed by analysis assimilation based on a regional WRF mode; aiming at the uncertainty source of a cloud analysis system, the method comprises the following steps of: randomly disturbing multi-source observation data input in the process of forming an initial field of each ensemble forecasting member cloud in ensemble forecasting; aiming at the uncertainty of a micro-physical parameterization scheme of the cloud analysis system, three-dimensional disturbance is carried out on key dynamics and thermodynamics sensitive parameters of cloud analysis, combined random disturbance of two processes of an initial value and the parameterization scheme is formed, and the initial value and cloud micro-physical parameterization uncertainty of the cloud analysis system and the interaction of the initial value and the cloud micro-physical parameterization uncertainty are further reflected. Convection triggering and rainfall evolution processes can be changed, and the cloud and rainfall forecasting accuracy of a mesoscale service ensemble forecasting system is improved.

The invention provides an emergency communication spectrum prediction method based on a gating adaptive time sequence diagram integrated network, which relates to the technical field of communication, and comprises the following steps: acquiring multi-band historical spectrum data, and dividing a training sample and a prediction label according to a time step; constructing a self-adaptive dynamic graph structure fusing priori knowledge and a learnable matrix, and combining gating time sequence convolution, graph convolution, channel alignment convolution and residual connection to form a gating time sequence graph network; carrying out iterative training by utilizing the training sample and the dynamic graph structure, and obtaining an integrated prediction model through period storage and average weight snapshot; and inputting a to-be-tested sample into the model and outputting a frequency spectrum prediction value. The method has the advantages that spatio-temporal feature extraction is fused, and prediction precision and scene adaptability are improved; the generalization and robustness of the model in the environment change are enhanced by the self-adaptive dynamic graph structure; the parallel gating time sequence convolution is adopted to reduce the calculation complexity and the reasoning time delay, and the millisecond-level response requirement of emergency communication is met.

The invention relates to the technical field of equipment predictive maintenance, in particular to an industrial equipment residual life prediction method based on an SRenbPI algorithm, which comprises the following steps: preprocessing industrial equipment source domain data, including constructing a heteroscedasticity noise simulation data set and introducing real industrial sensor data, and setting standardized input through normalization, sliding window construction and label; in the training stage, a bootstrap sampling strategy is adopted to construct an integrated regression model, noise influence is quantized through a scaling residual formula, and a residual set is formed; in the prediction stage, noise scale estimation and residual quantile are combined to generate an unequal-width prediction interval, meanwhile, a dynamic updating mechanism is introduced, a residual set is updated in real time to adapt to data distribution changes after a plurality of test samples are processed every time, and the model does not need to be trained again. According to the method, the adaptive defect of a traditional static residual set in industrial equipment full-life-cycle monitoring is effectively overcome, and the prediction interval precision and real-time performance are remarkably improved.

The invention discloses a method for improving sand and dust numerical forecasting through assimilation-aerosol-cloud-radiation, and the method comprises the steps: building a sand and dust storm forecasting mode, calculating sand and dust optical parameters based on a spherical particle extinction theory, and achieving the bidirectional feedback of sand and dust to weather; a sand and dust aerosol-cloud interaction ice nucleus nucleation mechanism is given, a sand and dust CCN type aerosol homogeneous freezing process is realized, a radiation variable temperature rate is calculated and fed back to a weather mode power process, and bidirectional feedback of weather-driven sand and dust and influence of sand and dust on weather is formed; constructing an ensemble forecasting module, performing time and space related disturbance on a meteorological initial field, a boundary condition and an aerosol initial concentration, and performing ensemble forecasting after a mode is input; and establishing an ensemble assimilation analysis correction module, constructing a background error covariance matrix by utilizing mode ensemble forecasting, disturbing observation at an analysis moment, and assimilating and correcting the initial concentration of the mode aerosol based on a localized ensemble Kalman filtering method to obtain a forecasting result.

The present application belongs to the technical field of deep learning and meteorological prediction, and comprises the following specific steps: taking the reservoir basin in the central part of China as the research object, selecting the control prediction results of 5 prediction modes, dividing the prediction data of the central reservoir according to the latitude and longitude information of the ground observation station, taking the data information near the station as the influencing factor, and performing regularization processing on the data in the small space range; the regularized prediction data of each mode is respectively put into each grouping module to obtain the characteristics of each mode prediction, and finally the accurate precipitation prediction near each station is obtained through the output module after effectively fusing the features such as terrain and station latitude and longitude. The present application can use multiple sub-networks to extract different mode characteristics respectively, and jointly act, give different weights, and can effectively fuse the prediction data of each mode to generate more accurate precipitation value near the station, so as to achieve better correction effect.

A method and system for predicting product defect rate, the method comprising: providing a dataset, wherein each data point in the dataset corresponds to a design combination and includes at least one first type of data field and at least one second type of data field; training a first model using the first type of data field of a portion of the dataset and training a second model using the second type of data field; integrating another portion of the dataset into the trained first / second model to obtain multiple first prediction results and multiple second prediction results; training an ensemble prediction model using the multiple first prediction results and multiple second prediction results; and integrating new data into the first model and the second model to obtain new first prediction results and new second prediction results, and integrating the two prediction results into the ensemble prediction model to obtain a defect rate prediction result for the new data.

The application provides a multi-source field fusion method and system for meteorological gridded data, comprising: collecting gridded meteorological element data of multiple data sources in the same time and space range; performing two-dimensional Fourier transform on the gridded meteorological element data of each data source respectively to obtain complex plane wave data corresponding to each gridded meteorological element data; dividing the complex plane wave data corresponding to each gridded meteorological element data into low-wave-number large-scale data and high-wave-number small-scale data according to a preset scheme; averaging the low-wave-number large-scale data, superimposing any one of the high-wave-number small-scale data, and then performing two-dimensional inverse Fourier transform to obtain fused multi-source gridded new meteorological element data; the application improves the small-scale prediction effect of weather prediction by flattening high-frequency small-scale processes through averaging, simultaneously embodies the advantages of traditional ensemble prediction, reduces errors through the averaging of multiple prediction fields, and improves the prediction accuracy.

This invention relates to the interdisciplinary field of fatigue fracture mechanics and machine learning, and discloses a method and computer program product for predicting fatigue crack propagation rate. The invention first constructs a heterogeneous candidate model pool, and then selects three preferred models from the pool through Bayesian optimization and competitive screening strategies. These three preferred models are then adjusted to achieve unified scheduling, forming an HPD-Stacking ensemble prediction model as the first layer, which, together with the meta-learner in the second layer, performs ensemble learning. Finally, a trained overall model is obtained for prediction. This invention effectively integrates the physical consistency of fracture mechanics laws with the data mining capabilities of ensemble learning. Even when faced with data of varying quality, it can still achieve high-precision prediction of fatigue crack propagation behavior, avoiding overfitting and non-physical fluctuations inherent in traditional single models.

The invention provides a combined temperature forecast correction method and system, and relates to the technical field of weather forecast. According to the method, three kinds of errors are systematically corrected, and the climate mode temperature forecast deviation is corrected. The method comprises the following steps: firstly, considering an error source of temperature forecast, and respectively disassembling observation temperature data and original forecast temperature data in a training period into three independent components, namely a mean term, a trend term and a residual term; and using conditional Gaussian correction to optimize the residual term error, and keeping the rank correlation structure of the original ensemble forecast. And according to requirements, performing mean value correction, trend correction and residual error correction on the original forecast temperature data, combining a mean value correction result, a trend correction result and a residual error correction result of the original forecast temperature data, and outputting a combined temperature forecast correction result. The method is used for temperature forecast correction and has the advantages of being good in correction effect, flexible to use, efficient in calculation and the like.

The application provides a multi-model multi-boundary estuary saltwater intrusion ensemble probability prediction method and system, wherein the method is coupled based on a plurality of calibrated and verified numerical models, a plurality of runoff boundaries and a plurality of wind field boundary conditions, an ensemble prediction system of multiple source uncertainty disturbances is constructed, and saltwater intrusion evolution results under a plurality of prediction scenarios are generated. Further based on historical observation data and model performance evaluation indexes, a sub-item confidence probability weighting method of the numerical model, the runoff boundary and the wind field boundary is established, and probability distribution estimation of the ensemble prediction result is realized. The method can quantitatively represent the probability and uncertainty of the occurrence of a saltwater intrusion event in a water source, and improve the accuracy and reliability of the estuary saltwater intrusion prediction.

A product defect rate prediction method and system. The product defect rate prediction method comprises: providing a data set, wherein each piece of data in the data set separately corresponds to a design combination, and comprises at least one first-type data field and at least one second-type data field; using the first-type data field of a part of the data set to train a first model, and using the second-type data field to train a second model; inputting the remaining part of the data set into the trained first / second model to obtain a plurality of first prediction results and a plurality of second prediction results; using the plurality of first prediction results and the plurality of second prediction results to train an ensemble prediction model; and inputting new data into the first model and the second model to obtain a new first prediction result and a new second prediction result, and inputting the two prediction results into the ensemble prediction model to obtain a defect rate prediction result of the new data.

This invention relates to the field of intelligent monitoring technology for hydropower projects, and discloses a method, device, medium, and equipment for intelligent analysis and early warning of grouting in hydropower projects. The method includes acquiring real-time monitoring data during the grouting process and constructing an ensemble prediction model using random forest, support vector machine, and XGBoost as base learners (Stacking); iteratively optimizing the hyperparameters of the ensemble prediction model using a particle swarm optimization algorithm; predicting the unit grouting volume during the grouting process using the model; generating explanatory information using the SHAP algorithm for feature importance interpretation; generating a grouting quality early warning signal based on the prediction results and explanatory information; and outputting the grouting quality early warning signal to a monitoring terminal. This method effectively improves the prediction accuracy, dynamic adaptability, and decision interpretability of the grouting process in hydropower projects, thereby enhancing the intelligence, precision, and risk control level of grouting construction in hydropower projects.

The embodiment of the invention provides a regional wind power icing back-and-reserve capacity clustering set prediction method and device and a storage medium. The method comprises the following steps: dividing wind power plants in a region into a plurality of wind power plant clusters based on meteorological characteristic and topographic characteristic parameters; for each cluster, respectively establishing an individual icing reserve capacity prediction model of each wind power plant in the wind power plant cluster, and establishing an icing reserve capacity reference prediction model of the wind power plant cluster; obtaining an individual reserve capacity prediction value of each wind power plant in each wind power plant cluster through an individual icing reserve capacity prediction model; determining a set initial value of the wind power plant cluster according to the similarity between the meteorological characteristics and topographic characteristic parameters of each wind power plant in the wind power plant cluster; obtaining a reference prediction value of each wind power plant cluster and correcting the reference prediction value to obtain an icing reserve capacity prediction value of each wind power plant cluster; and summing the predicted values of the icing reserve capacity of all the wind power plant clusters to obtain a total predicted value of the regional wind power icing reserve capacity.

To provide a technique capable of performing ensemble prediction with high accuracy even when a distribution of information related to a prediction target locally changes.SOLUTION: The prediction device is configured to, based on an evaluation result obtained by evaluating performance of each of a plurality of models with reference to evaluation information including model input information for evaluation and a true value corresponding to the model input information, and the evaluation information, A weight update unit that updates some or all of the plurality of first weight vectors and some or all of the plurality of second weight vectors, and an integration unit that integrates a prediction result predicted by each model with reference to model input information included in prediction target information related to a prediction target, using a weight vector selected from the plurality of first weight vectors and the plurality of second weight vectors on the basis of the prediction target information, and SELECTED DRAWING: Figure 1

The invention relates to a climate mode optimization and set estimation method based on interpretability and machine learning, and belongs to the technical field of climate modes, and the method comprises the steps: obtaining the area weighted average ozone column total amount of reanalysis data and CMIP6 mode data, and constructing a data set; based on the data set, a traditional method and a decision tree SHAP method are adopted to screen a CMIP6 mode; constructing a machine learning model based on CMIP6 mode data screened by a traditional method and a decision tree SHAP method; based on the constructed machine learning model, determining an optimal screening method and a corresponding optimal machine learning model; and based on the optimal screening method and the corresponding optimal machine learning model, performing recovery estimation on the Antarctic ozone under different greenhouse gas emission scenes. According to the method, a more accurate ozone recovery estimation result of the Antarctic can be obtained.

The application discloses a kind of Enteromorpha prolifera green tide medium and long-term probabilistic ensemble prediction method, belong to Enteromorpha prolifera green tide prediction field.This method includes the following steps: a, based on historical monitoring data, the longitude, latitude and initial occurrence time of initial occurrence position of Enteromorpha prolifera green tide are obtained, potential throwing area and initial window period are set;Construct the time-space probability density function of throwing area, and generate the initial field set scheme of Enteromorpha prolifera green tide medium and long-term drift prediction;B, obtain a plurality of groups of medium and long-term marine environment prediction driving field data, and construct to form multiple driving field set scheme;C, the initial field set scheme and multiple driving field set scheme are substituted into Lagrange drift prediction model, and cycle simulation is carried out, the drift trajectory of each Enteromorpha prolifera green tide particle under all set schemes is obtained, and the medium and long-term ensemble prediction result of Enteromorpha prolifera green tide is generated.The application can carry out Enteromorpha prolifera green tide medium and long-term prediction, and significantly improve the accuracy and reliability of prediction, provide scientific basis for disaster prevention and mitigation decision-making.

The present application relates to the field of agricultural meteorology and precision irrigation technology, and specifically discloses a reference crop evapotranspiration probability prediction method and system based on bias correction. The method comprises: obtaining meteorological ensemble prediction data of a target site in a future prediction period, and preprocessing the meteorological ensemble prediction data to match the spatial and temporal scales of the target site; using at least one strategy in a preset bias correction strategy library to correct the system error of the prediction data of each ensemble member after preprocessing; inputting the corrected meteorological data of each ensemble member into at least one reference crop evapotranspiration calculation model respectively to calculate a set of reference crop evapotranspiration prediction values corresponding to the future prediction period; and statistically analyzing the set of reference crop evapotranspiration prediction values to generate and output a probability prediction product quantifying the prediction uncertainty. With this method, the accuracy and reliability of the final probability prediction can be improved, thereby improving the effect of precision irrigation.

The application discloses a tunnel flood early warning system based on fluid three-dimensional dynamic simulation and emulation, comprising: a data processing module for storing a water inflow rate model; a model module for storing a slice model set of tunnel water level rise simulation and emulation; a prediction module for acquiring future rain intensity, calculating water inflow rate, and calling the slice model of water level rise simulation and emulation; a display module for displaying a tunnel three-dimensional water level simulation video; and an emergency processing module for judging a risk level and outputting an emergency plan. The application has high early warning accuracy, long time efficiency and timeliness. The application discloses a tunnel flood early warning method, comprising: establishing and storing a water inflow rate model and a slice model set of tunnel water level rise simulation and emulation; acquiring future rain intensity, predicting V 进 , water inflow and water level depth, and calling the slice model of water level rise simulation and emulation; forming and outputting a tunnel three-dimensional water level simulation video; and judging a risk level and outputting an emergency plan.

The application provides a typhoon forecast-based target observation sensitive area identification method and system, and belongs to the technical field of numerical weather prediction and ensemble prediction, and comprises the following steps: a target area is delimited with a typhoon message center position; based on a business prediction model, Lanczos iteration algorithm and multi-scale singular vector algorithm are used to obtain tropical cyclone wet singular vectors and mesoscale and small-scale singular vectors by coupling large-scale condensation linear physical processes; humidity variables and the contribution of multi-scale singular vectors to the thermal structure of the typhoon are introduced to calculate the total energy of each grid point; the total energy of the grid points on different isobaric surfaces is accumulated along the vertical direction to obtain a multi-scale energy field; weighted summation normalization processing is performed according to singular value weight distribution; and the target observation sensitive area is obtained based on a set threshold value. The application solves the problems of incomplete physical process description, limited sensitive area identification precision and insufficient business applicability in the existing typhoon target observation technology.

This invention discloses an ensemble evaluation method for artificial cloud seeding effects based on intelligent extrapolation of radar echoes. The method includes acquiring sample radar data, selecting multiple intelligent extrapolation models, training them using preprocessed data, and constructing a model library; constructing an EES ensemble evaluation algorithm, calling the model library to infer the echo results at the time before the operation, and scoring the model performance; selecting high-scoring models to predict the echoes during the operation period, fusing and correcting them to obtain the ensemble prediction results; comparing the ensemble predictions with real-time monitoring data, extracting key physical parameter change characteristics, and quantitatively evaluating the operation effect. This invention solves the heterogeneous adaptation problem of different intelligent extrapolation models for cloud seeding effect evaluation, improves extrapolation accuracy by combining the advantages of multiple models, enhances the accuracy and reliability of the verification results, and provides technical support for the large-scale application of intelligent extrapolation technology in effect evaluation.

This invention discloses a hyperspectral inversion method for soil heavy metals based on a CNN-TabM model, belonging to the field of quantitative inversion of soil heavy metal content via hyperspectral remote sensing. The method includes: collecting soil samples and measuring hyperspectral data and heavy metal content; sequentially performing noise band removal, SG smoothing, and fractional derivative transform on the hyperspectral data, and using the CARS algorithm to select feature bands; inputting the selected bands into the CNN-TabM model, which uses a one-dimensional convolutional neural network to extract local spectral features, and after feature fusion, inputting the results into the TabM model for global feature interaction and ensemble prediction, outputting the heavy metal content inversion result. This invention achieves complementary advantages of local spectral detail perception and global feature modeling by coupling convolutional neural networks and tabular deep learning models, significantly improving the accuracy of soil heavy metal inversion and providing effective technical support for soil environmental monitoring.