439 results about "Independent predictor" patented technology

The invention relates to the technical field of photovoltaic power prediction, and discloses a photovoltaic power prediction method and system, and the method comprises the steps: obtaining the historical operation data of each photovoltaic station, carrying out the abnormal value elimination, expanding the sample data through a generative adversarial network, constructing a first training sample set, and carrying out the variable dimension reduction, screening principal component factors influencing the photovoltaic power to construct a second training sample set; calculating similar days by using the second training sample set, and screening and sorting; establishing a deep learning framework fusing the long short-term memory network, the maximum temperature prediction model and a parameter optimization algorithm, and based on a preset photovoltaic power prediction precision evaluation index, performing model training by taking the number of days of similar days and the weight as independent variables to establish a power prediction model; based on preset reanalysis data and weather forecast data, photovoltaic power prediction is carried out by using the trained power prediction model, precision evaluation and dynamic optimization of model parameters are carried out, and the photovoltaic power prediction precision and adaptability to different scenes are improved.

The invention provides a renewable energy equipment fault intelligent diagnosis method based on deep learning. The method comprises the following steps: S1, collecting data of a photovoltaic system and the like, carrying out data preprocessing, and constructing a training sample set with a fault label; s2, constructing a fractional order physical information neural network in a weighted shift Grunwald-Letnikov format, and outputting an equipment fault probability prediction value; s3, establishing a segmented Poisson regression model, taking a time variable and an environment virtual variable as independent variables, performing dynamic trend modeling on the equipment fault probability predicted value, and outputting the fault probability after statistical correction; s4, fusing the equipment fault probability prediction value and the corrected fault probability to generate a final fault probability; and S5, optimizing prediction strategy parameters based on a reinforcement learning method of a relative reward regression mechanism. According to the invention, the identification capability of implicit fault features is improved, and the fault diagnosis accuracy is significantly improved.

The invention provides a BIM-based bridge construction progress monitoring method and system, and the method comprises the steps: obtaining an observation matrix based on BIM model data and sensor monitoring data of a target bridge; inputting the observation matrix into a preset Kalman filter to obtain a corresponding fusion matrix; carrying out Granger causal test by taking the overall progress deviation as a dependent variable and taking the stress ratio, the environmental index, the resource delay rate and the process progress deviation as independent variables to obtain a Granger causal test result; and constructing a corresponding DAG, and obtaining the reason causing the overall progress deviation based on the DAG. According to the scheme, BIM model data and sensor monitoring data are accurately fused, the DAG between the overall progress deviation and each piece of quantitative information is constructed, the quantitative information causing the overall progress deviation can be accurately obtained, and an effective basis is provided for subsequent decision making.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to a construction method of a multi-class lipid retention time general prediction model and a prediction system. The method comprises the following steps: by taking experimental retention time tRE of a lipid compound in a training set as a dependent variable and characteristic structure parameters of the lipid compound as an independent variable, carrying out quantitative processing on the independent variable and then carrying out regression modeling analysis, the characteristic structure parameters comprise the total carbon number c and the total carbon-carbon double bond number d of a fatty acyl chain, an ether chain, an alkenyl ether chain or / and a sphingosine skeleton, the types of skeletons contained in the lipid compound and the number of corresponding skeletons, and the types of characteristic groups and residues in the lipid compound and the number of corresponding residues; carrying out numerical quantization on parameters according to the number of skeletons, characteristic groups or residues of the corresponding types; the QSRR general prediction model of the retention time of the multi-class lipid compounds is obtained by adopting regression modeling, a more accurate MRM data acquisition window can be set for the lipid compounds, and the sensitivity, stability and coverage can be improved.

The invention discloses a method for automatically solving the critical heat flux density of a reactor based on a statistical principle, and belongs to the field of safety analysis of nuclear reactors. The problems that an over-fitting problem exists, a data set cannot be verified clearly through a fitting data set, and the development process is complicated when a traditional method is used for predicting independent variable combination items of a relational expression artificially given by CHF relational expression development are solved. The method comprises the following steps: collecting reactor CHF experimental data points; eliminating possible repeated experimental points and cold rod critical points to form a development database; based on a layered random sampling method, extracting data from the development database, and determining a fitting data set and a verification data set; based on the fitting data set, selecting a curve form of a single independent variable or a combination item; the independent variables and the combination items with the collinearity problem are removed; importance ranking analysis of the variables and the combination items is obtained through regression coefficient standardization; forming a final CHF relational expression; and based on the verification data set, verifying the CHF relational expression.

The invention relates to the field of soft measurement, discloses a soft measurement method based on an optimization algorithm, and solves the industrial pain points of fast precision attenuation, poor mechanism adaptation and high maintenance cost of a traditional soft measurement model. Comprising the steps of obtaining historical production data and a configuration file, and processing and integrating the historical production data and the configuration file into a complemented data file; determining a candidate state variable set according to the complemented data file, and searching an optimal independent variable set through optimization algorithms such as a genetic algorithm; training a data driving model by using the optimal independent variable set to generate a soft measurement model; an unmeasurable input variable is used as a genetic algorithm optimization target, a fitness function is constructed through an average absolute error of a predicted value and an actual value, and an optimal solution is output through iterative evolution. By comprehensively applying various optimization algorithms, the precision and the reliability of the soft measurement model are effectively improved, powerful support is provided for real-time optimization of an industrial control system, and the production efficiency and the product quality can be improved.

The invention discloses a sewage energy-saving control method and system based on multi-dimensional variable data analysis, and relates to the technical field of data analysis, and the method comprises the steps: analyzing the incidence relation between variable data in different processing stages, the incidence relation of variables comprises the relation between independent variables and dependent variables and the interaction relation between the independent variables, and the relation between the independent variables and the dependent variables is the relation between the independent variables and the interaction relation between the independent variables; the complex coupling relation among multiple variables is fully and accurately described, and a reliable basis is provided for follow-up regulation and control. And determining the corresponding regulation time delay and variability on each regulation node, and considering the regulation hysteresis and the variable change condition on the sewage treatment flow, thereby setting a threshold value and generating an evaluation index for guiding the subsequent sewage regulation. The regulation and control strategy of each regulation and control node is controlled based on the incidence relation and the regulation and control time delay between the variable data, the adaptability and accuracy of sewage node analysis and control are improved, the real-time sewage treatment effect is ensured, and the sewage treatment efficiency is improved.

A facility for estimating a value relating to a occurrence is described. The facility receives a first occurrence that specifies a first value for each of a plurality of independent variables that include a distinguished independent variable designated to be monotonically linked to a dependent variable. The facility subjects the first independent variable values specified by the received occurrence to a statistical model to obtain a first value of the dependent variable. The facility receives a second occurrence that specifies a second value for each of the plurality of independent variables, the second value of the distinguished independent variable varying from the first value of the distinguished independent variable in a first direction. The facility subjects the second independent variable values specified by the received occurrence to the statistical model to obtain a second value of the dependent variable, the second value of the dependent variable being guaranteed not to vary from the first value of the dependent variable in a second direction that is opposite the first direction.

The embodiment of the invention provides a feature screening method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining an initial feature set and a target dependent variable; taking each abnormal value threshold in the plurality of abnormal value thresholds as a current abnormal value threshold, and executing the following feature screening operation to obtain a plurality of feature sets: according to a correlation coefficient between each initial independent variable and a target dependent variable and the current abnormal value threshold, performing linear regression processing on each initial independent variable in sequence to obtain a plurality of feature sets; and obtaining a saliency probability value of each initial independent variable under the current abnormal value threshold, so as to screen the initial independent variables from the initial feature set, obtain a feature set corresponding to the current abnormal value threshold, and select a target feature set. The problem that the reliability of a prediction model is low when data containing abnormal values are used for training is solved, and the effect of improving the prediction efficiency of the model is achieved.

The invention provides a disease burden prediction and prevention and control decision-making method and system based on machine learning, and relates to the technical field of disease prediction and public health decision-making, and the method comprises the steps: obtaining epidemiological data of tuberculosis and related diseases from an authoritative database, and carrying out the preprocessing of the epidemiological data; respectively training an XGBoost model, an RF (Radio Frequency) model and a Prophet model; training a random forest meta-model by adopting a Stacking fusion strategy, and constructing a hybrid prediction model; calculating RMSE, MAE, MAPE and Rindex evaluation model performance; based on the obtained hybrid prediction model; based on the variable importance analysis result and the prediction result, the influence of the key independent variable on the tuberculosis burden dependent variable is quantified, the effect of the independent variable change on the dependent variable is simulated, and a tuberculosis prevention and control intervention strategy suggestion is generated. By fusing multi-source data and a hybrid modeling technology, tuberculosis prediction precision is remarkably improved, confidence interval quantization and prevention and control strategies are linked for the first time, and data-driven decision support is provided for global tuberculosis prevention and control.

The invention belongs to the crossing field of urban design and multidisciplinary, and particularly relates to a mountain historical block ownership perception evaluation method based on multi-modal data, public spaces of mountain historical blocks are divided into multiple types, an experimental path is selected from each public space, and multiple experimental parking points are selected from each experimental path; the evaluation index system comprises physiological data, psychological data and spatial data, and the spatial data comprises feature data and acoustic environment data; for each experiment path, obtaining feature data, recruiting a plurality of experimenters, collecting acoustic environment data and physiological data of each experimenter in real time, and collecting psychological data from the experimenters in real time in a questionnaire mode at an experiment stop point; independent variables are defined as spatial data, dependent variables are defined as psychological data and physiological data, based on experimental data, the relation between the independent variables and the dependent variables is learned through a random forest model, and bar charts of the influence degrees of the independent variables and the dependent variables are output; and the evaluation result is more accurate and persuasive.

The invention discloses a sintering batching optimization scheme, and relates to the technical field of sintering ore batching control, and the technical key points are as follows: a random forest proxy model is used to predict the performance of finished ore, and a nonlinear mapping relationship between an independent variable and a dependent variable is used as a target function; carrying out Pareto optimal solution set search through non-dominated sorting and crowding distance calculation by adopting an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; calculating the weight of each target index by using an entropy weight method based on a Pareto solution set; weight is determined from a Pareto solution set generated by an NSGA-II algorithm, an optimal batching scheme closest to an ideal solution and far away from a negative ideal solution is screened out through a TOPSIS method, and multi-target comprehensive weighing is achieved; according to the method, sintering batching optimization based on finished ore performance feedback is realized, dynamic optimization of a batching plan can be effectively guided, the sinter quality control level is improved, the production cost is reduced, and technical support is provided for efficient and stable operation of a blast furnace ironmaking process.

The invention discloses a distribution transformer load loss prediction method and system based on pilot-scale test monitoring data. The method comprises the following steps: collecting pilot-scale test data of a distribution transformer load loss related variable; preprocessing the collected pilot plant test data, and performing correlation analysis on the preprocessed variable data to obtain an independent variable in a distribution transformer finished product load loss prediction function; performing multiple regression analysis on the selected independent variables to obtain a regression model, correcting the regression model to obtain a distribution transformer finished product load loss prediction function, and verifying the load loss prediction function; and performing load loss prediction on the distribution transformer finished product based on the verified load loss prediction function. According to the method, the predicted value of the load loss can be quickly obtained by analyzing pilot test data, the time and the cost of a traditional experiment are reduced, and the calculated load loss prediction precision can be effectively improved by analyzing variables and verifying functions.

The invention discloses a fracture pressure prediction and internal friction angle joint inversion method for a heterogeneous stratum. The invention belongs to the technical field of petroleum engineering and artificial intelligence cross application, well depth is used as an independent variable, the minimum / maximum horizontal stress, pore pressure and cohesion are combined to construct well depth mixed input, only multi-scale periodic basis function transformation is carried out on the well depth, and a linear well depth channel is reserved; establishing a rupture pressure depth mapping model, introducing an augmented Lagrange physical constraint containing dual variables and a dynamic threshold, adaptively adjusting a physical weight according to a physical residual error and a threshold difference value, realizing dynamic balance of a rupture criterion and data fitting, and searching and optimizing a model topology by using a multi-target network structure; under a limited sample, a fracture pressure profile and a physically reasonable internal friction angle can be obtained at the same time, and the prediction precision and the physical consistency are improved.

The invention provides a dynamic prediction algorithm for monitoring late-onset infection of a premature infant and an upgrading system. The dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system comprise the following steps: a, collecting clinical data of the premature infant, including birth weight, gestational age, 1-minute and 5-minute Apgar scores, right hand perfusion index, lower limb perfusion index and other related clinical information, and b, determining the early-onset infection of the premature infant through medical history collection and physical sign analysis. The method comprises the following steps: collecting data of 11 classification independent variables: prenatal antibiotic use conditions (existence and absence); according to the dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system, the infection risk index is effectively calculated through high-risk factors analyzed by the Lasso regression model in combination with clinical basic data of the premature infant, early warning of infection of the premature infant is provided for medical staff, and the accuracy and timeliness of infection prediction are remarkably improved. Besides, the system can automatically remind medical staff to intervene the high-risk child patient through red warning, so that the death rate caused by delayed discovery and delayed treatment is reduced, and the clinical intervention effect is improved.

The invention discloses a near infrared spectrum modeling method and system based on merge matrix PCA, and relates to the technical field of near infrared spectrum and the field of chemometrics. The method comprises the following steps: acquiring original near infrared spectrum data and reference data of a to-be-detected sample as independent variable data and corresponding dependent variable data respectively; respectively performing data set division on the independent variable data and the dependent variable data by adopting a sample set division method; and constructing a regression prediction model based on a least square regression method, training and verifying the regression prediction model by using the preprocessed data set, performing correlation analysis on the data set and a prediction target based on a principal component analysis method in the training process of the regression prediction model, and performing principal component extraction. According to the near infrared spectrum modeling method and system based on merging matrix PCA, dimensionality reduction is carried out on high-dimensional data possibly existing in a sample, then a robust prediction model is established, and rapid and accurate quantitative analysis is achieved.

The invention relates to the field of gates, and provides a gate vibration online analysis early warning method and system, and the method comprises the steps: training the historical monitoring data of a gate through a machine learning algorithm, and constructing a mapping relation between an independent variable and a dependent variable; upstream and downstream related independent variable information of the gate is obtained, and the gate vibration state is calculated online according to the mapping relation; analyzing and calculating the natural vibration frequency of the gate based on the modal analysis model of the gate; and evaluating the safety state of the gate according to the gate vibration state and the natural vibration frequency of the gate. When the system is used, the problems of data islanding, prediction lag, model distortion and low decision efficiency in the traditional technology are solved, high-precision and full-period intelligent guarantee is provided for hydraulic engineering safety, the resonance accident risk is remarkably reduced, and gate operation and maintenance management is promoted to be upgraded to the standard and unmanned direction.

The invention provides a full-dynamic non-invasive blood glucose detection method based on a full-spectrum peak segment optimization algorithm. The method comprises the following steps: step S1, performing blood glucose testing on a nail bed part of a human little finger by matching an infrared dual-wavelength semiconductor laser with a spectrograph, and recording blood glucose Raman spectrum data obtained in each blood glucose testing; step S2, carrying out pretreatment on the obtained blood glucose Raman spectrum data; s3, dividing the preprocessed blood glucose Raman spectrum data into a training set and a prediction set, analyzing through a principal component analysis method and a partial least square regression analysis method, and establishing a model; s4, inputting the preprocessed blood glucose Raman spectrum data as an independent variable into the model in the step S3, analyzing the blood glucose concentration of the human body, and giving a reference value; according to the full-dynamic non-invasive blood glucose detection method based on the full-spectrum peak segment optimization algorithm, the accuracy of quantitative analysis of a high-fluorescence background sample through a Raman spectrum is achieved, complex treatment on the sample is not needed, and the accuracy of human body non-invasive blood glucose detection is improved.

The invention discloses an auxiliary decision-making system of a transcranial magnetic stimulation mode, and the system comprises an obtaining module which is used for obtaining the personal feature information of a patient; the determination module is used for calling a preset evaluation model to calculate the brain function retention degree of the patient by adopting the personal characteristic information, and determining the treatment mode of the patient according to the brain function retention degree; wherein the preset evaluation model is a classification model constructed based on demographic characteristic parameters, behavioral evaluation characteristic parameters, neural electrophysiological characteristic parameters and brain image characteristic parameters as independent variables. The brain function retention degree is calculated by using the model, and the transcranial magnetic stimulation mode is determined according to the brain function retention degree without relying on subjective calculation of a doctor, so that the accuracy of determining the treatment mode can be improved; meanwhile, multiple evaluation, detection and calculation are not needed for processing, the processing time can be shortened, the processing efficiency and precision can be further improved, the diagnosis and subsequent treatment of the patient are prevented from being affected, and precise treatment is achieved.

The invention relates to the technical field of multi-node task data processing, in particular to a large model reasoning scheduling method and system in a multi-node heterogeneous environment. According to the method, stage vectors in two stages of an inferred task are extracted for each computing node, and the power consumption characteristic difference and the excess delay are further determined. Two independent predictors are trained based on the two features. A state conversion directed graph of each computing node is constructed, and setting of edge weights is determined by phase vector differences between state nodes and additional risks obtained by a predictor. And after determining the corresponding nodes of the to-be-reasoned task in the state transition directed graph, determining the optimal path of each computing node for the to-be-reasoned task, and further screening out the optimal execution computing node. According to the method, the optimal execution computing node is determined, so that the influence on task execution caused by unstable hardware performance due to blind selection of the execution computing node is avoided.

The method comprises receiving one or more bounds for each coefficient of independent variables. Further, a steepness value is selected for controlling the velocity of weight updates for each coefficient. Subsequently, an update vector with a length equal to the number of independent variables may be created. Further, the method may comprise iterating until convergence. Each iteration may include computing a gradient for each independent variable based on the gradient, updating values of each coefficient based on the computed gradient, the computed multiplier, a learning rate, and the update vector. Further, the method involves monitoring the CLR optimization process for convergence based on whether a change in the value of the cost function is below a predefined threshold or a maximum number of iterations is reached. Further, the optimized CLR model may be utilized for at least one application within Revenue Growth Management (RGM).

The present application discloses a method and a device for predicting the number of power outages of distribution network users under typhoon disasters, including: collecting environmental data of a target area; performing preprocessing and correlation analysis on the environmental data to obtain first data, where the preprocessing includes normalization processing, categorical variable processing, and dependent variable processing; based on the random forest algorithm, establishing a prediction model for the number of power outages of distribution network users under typhoon disasters based on global variables, and evaluating it to obtain the importance of each independent variable; selecting independent variables that meet the preset criteria as important variables from all independent variables according to the importance, and using the important variables as inputs to establish a prediction model for the number of power outages of distribution network users under typhoon disasters based on the important variables; training and testing the prediction model for the number of power outages of distribution network users under typhoon disasters based on the important variables, and outputting the number of power outages of distribution network users under typhoon disasters. The present application can accurately predict the number of power outage users of the distribution network under typhoon disasters through the model.

The present invention discloses a method for improving the prediction accuracy of soil iron oxide based on an amplified hyperspectral dataset. The method comprises the following steps: amplifying the hyperspectral dataset and performing multiple fractional-order differential transformations on the original hyperspectral spectrum using a fractional-order differential algorithm; then combining the fractional-order differential spectra with the original spectrum to form different amplified hyperspectral databases to amplify the amount of original spectral data; establishing a 1D-CNN neural network model for each of the different amplified hyperspectral databases, where the independent variable is the hyperspectral reflectance value and the dependent variable is the soil iron oxide content; and selecting the optimal 1D-CNN model based on the models established for the different amplified databases. The present invention effectively solves the problem of low accuracy of deep learning models under small sample conditions, improves the applicability and effectiveness of deep learning models to a certain extent, effectively improves the existing prediction accuracy of soil iron oxide based on hyperspectral data, and provides new methods and ideas for hyperspectral prediction research of other soil properties.

The invention discloses an open-web gravity dam deformation prediction and mechanism interpretation method, system, equipment and medium, and belongs to the technical field of dam and hydraulic structure health monitoring and state evaluation. Constructing the preprocessed monitoring data into corresponding feature vectors, dividing a time sequence data set, and performing XGBoost model training and hyper-parameter optimization to obtain a prediction model; calculating a corresponding SHAP value, and screening a key factor as an independent variable for model analysis; and selecting a control index to evaluate the performance of the model, and deploying the model to a dam safety monitoring system after verification so as to carry out real-time prediction and early warning. According to the method, while high-precision deformation prediction of the open-web gravity dam is realized, quantifiable mechanism explanation of the prediction result is provided, so that transparent and reliable decision support is provided for safety monitoring and early warning of the dam.

The invention discloses a tumor radiotherapy reaction prediction method, system and program product based on a three-dimensional image and a residual network. The prediction method comprises the steps of image acquisition and slicing, region-of-interest processing and image fusion, three-dimensional feature extraction, multi-gating hybrid expert model construction, model training, radiotherapy reaction regression value prediction, model performance evaluation and the like. Firstly, a three-dimensional PETpre image and a three-dimensional Dose image of a patient are preprocessed; secondly, inputting the preprocessed image into a hard parameter shared 3D CNN network, and extracting three-dimensional space features; and finally, taking the extracted three-dimensional features as independent variables X, respectively taking the average value of the SUV and the change rate of the SUV as dependent variables Y, and inputting the independent variables X and the dependent variables Y into the trained multi-gating hybrid expert model for prediction. According to the method, ResNet and 3D CNN technologies are combined, collaborative prediction of the SUV average value and the change rate of the SUV average value is realized by using a multi-task learning strategy, limited data can be utilized more effectively, and the prediction accuracy and the model generalization ability are improved.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.

The invention relates to the technical field of gastrointestinal tumor chemotherapy risk assessment, and particularly discloses a gastrointestinal tumor chemotherapy risk scoring model and a construction method thereof, and the method comprises the steps: obtaining the individualized feature data and basic physiological data of a historical patient, and constructing an individualized difference library with a unique ID, and a core influence library; the method comprises the following steps: standardizing and coding double-library data, synchronizing clinical data containing treatment effect codes, forming a data dictionary, acquiring and coding current gastrointestinal tumor patient data, and matching the data dictionary to judge individual difference abnormity; when the first data set is abnormal, constructing a first data set, screening core independent variables through Logistic regression, and constructing a scoring model by using an LSTM (Long Short Term Memory) model; when no abnormity exists, redundant codes are removed to obtain a second data set, and modeling is conducted through the same method. Through double-library linkage and scene-divided modeling, the three-level toxicity risk prediction precision is improved, data support is provided for clinical chemotherapy dose adjustment and toxicity prevention, and the risk of excessive treatment or insufficient treatment is reduced.

The invention discloses a phytoplankton space distribution prediction method based on environment DNA and machine learning, and belongs to the technical field of ecological environment evaluation and treatment. Comprising the following steps: determining a sampling site, and collecting a phytoplankton eDNA sample; carrying out DNA extraction and treatment on the collected eDNA sample, and calculating a species abundance matrix; screening a training data set for training a prediction model according to the sampling time and the sampling site of the eDNA sample; inputting the training data set as an independent variable of a training set, inputting the species abundance matrix as a dependent variable of the training set, and training a prediction model; and inputting remote sensing image data into the prediction model to obtain a species spatial distribution diagram. Compared with the prior art, the method has the advantages that efficient and high-species-resolution spatial distribution prediction of algae species such as blue-green algae can be achieved, the phytoplankton prediction model is trained through the machine learning model, automatic operation of spatial prediction of various phytoplanktons is achieved, the arrangement density of sampling points is reduced, and the monitoring efficiency is improved.

The invention discloses an ensemble learning reverse process parameter determination method based on Bayesian optimization, and belongs to the field of process parameter optimization. The method comprises the steps that historical process data including target pressure, butterfly valve input angles, circulation volume and circulation volume are collected and preprocessed; a target pressure threshold interval is set, target pressure is divided into two sections, and target models are established respectively; constructing a plurality of machine learning models by taking the butterfly valve input angle, the circulation volume and the circulation volume as independent variables and the target pressure as a dependent variable, and combining the machine learning models through an integrated learning method; an input feature vector is generated, and the minimum value and the maximum value of the input angle of the butterfly valve and the corresponding circulation amount and circulation volume are obtained; inputting the feature vector into an integrated learning model to predict target pressure; the optimal hyper-parameter is automatically searched through the Bayesian optimization algorithm, and the model prediction performance is improved. According to the method, the accuracy and the efficiency of determining the process parameters are effectively improved through a method of combining ensemble learning and Bayesian optimization.