335 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.

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.

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 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 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 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 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 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 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 relates to a light high-entropy alloy yield strength machine learning prediction method, which comprises the following steps: S1, obtaining an original data set of a light high-entropy alloy sample, the original data set comprising the yield strength of the light high-entropy alloy sample and corresponding experimental process conditions; s2, extracting feature parameters for describing samples based on the original data set; s3, dividing the original data set into a training set and a test set; s4, screening an optimal feature subset from the feature parameters, and calculating a secondary feature set based on the optimal feature subset; and S5, taking the yield strength of the light high-entropy alloy sample as a target variable, taking the secondary feature set as an independent variable, and adopting XGBoost trained by the original data set to construct a quantitative prediction model of the yield strength of the light high-entropy alloy. Compared with the prior art, the method has the advantages that the physical interpretability is enhanced, the generalization ability is improved, the dependence on the sample size is reduced, and the like.

The invention provides a method, a system and a kit for constructing a model for evaluating the possibility that a subject suffers from gastric cancer based on a Swis-prot database, electronic equipment applying the construction method and a computer readable medium storing computer program codes. The construction method comprises the steps of obtaining a plurality of independent variables, carrying out screening and data processing on the independent variables, further screening effective independent variables in a regression model training process, reducing the number of the independent variables, obtaining a relationship among the independent variables through correlation analysis, carrying out replacement and combination by adopting the independent variables with a replacement relationship, and obtaining a regression model. And obtaining a plurality of prediction models. The prediction models can obtain highly accurate prediction results only by using fewer effective independent variables, so that the prediction efficiency is high, the application range is wide, and the cost is low. The invention provides an efficient, reliable and economical solution for the field of gastric cancer risk assessment.

The invention relates to the technical field of petroleum and natural gas drilling engineering, and particularly discloses a multi-field coupled complex fractured formation fracture width distribution inversion method, which is characterized in that a drilling fluid leakage model considering a fluid-solid coupling effect is constructed based on a porous elastic mechanics theory, and a roughness correction factor is introduced to correct a traditional cubic law; the application of the Darcy law in the matrix is perfected, a matrix and crack coupled drilling fluid leakage mathematical model is established, the influence rule of key parameters such as fluid viscosity, pressure difference and crack width on the drilling fluid leakage amount is obtained through numerical simulation, and on the basis, the drilling fluid leakage amount is calculated. According to the method, the crack width multiple linear regression inversion equation with the fluid viscosity, the pressure difference and the accumulated leakage as independent variables is established, the inversion equation has high prediction precision and practicability and can be used for rapidly and accurately inversing the crack width on site, and theoretical and technical supports are provided for multi-scale fractured formation leakage mechanism understanding and leakage prevention and control optimization.

The embodiment of the invention discloses a key soil monitoring point screening method based on multiple machine learning methods, and the method comprises the steps: obtaining soil ecological risk values of a plurality of monitoring points in a to-be-monitored region, and values of multiple related factors; screening out a plurality of main driving factors of the soil ecological risk through a random forest method; fitting a prediction model which takes the soil ecological risk as a dependent variable and takes the multiple main driving factors as independent variables, and deleting redundant point locations in the prediction model; taking the point position deletion rate, the prediction precision of the prediction model after deletion and the space coverage uniformity of the remaining monitoring point positions as optimization objectives, and executing a Bayesian optimization algorithm to update hyper-parameters in the random forest method and prediction model fitting process; and returning to execute the random forest method according to the new hyper-parameter until the optimal target is realized. And executing the operation again according to the optimal hyper-parameter to obtain a prediction model with an optimal monitoring point position. According to the embodiment, the accuracy and representativeness of point location screening can be improved.

This invention relates to an optimized control method for the enrichment of anaerobic ammonia-oxidizing bacteria based on niche regulation, comprising: S1, selecting key niche indicators of constructed wetlands as investigation factors, differentially setting the investigation factors to form experimental systems with different niches, conducting constructed wetland control and monitoring, and collecting data; S2, using the relative abundance and / or absolute abundance of anaerobic ammonia-oxidizing bacteria collected in S1 as dependent variables and the key niche indicators as independent variables, fitting and then predicting a model; S3, making predictions using the prediction model, comparing the prediction results with the control target, and optimizing the control of anaerobic ammonia-oxidizing bacteria enrichment in constructed wetlands based on the comparison results. This method can obtain a prediction model with high prediction accuracy, and then, by adjusting the niche indicators in constructed wetlands, the enrichment of anaerobic ammonia-oxidizing bacteria can be ensured, wastewater treatment efficiency can be improved, and a better total nitrogen removal effect can be achieved.

The invention provides a titanium alloy performance multi-objective optimization method and system based on machine learning, and belongs to the field of computational material science. The method comprises the following steps: acquiring titanium alloy components, heat treatment process parameters, microstructure information, equivalent parameters of various elements and basic performance data as independent variables X; acquiring service performance data; performing theoretical supplementary calculation on the X; all the data are preprocessed; taking the independent variable parameters as features, screening the features to obtain a data set, and dividing the data set into a training set, a verification set and a test set; respectively inputting the training set and the verification set into a plurality of different machine learning algorithm models, selecting a model meeting index requirements, and performing hyper-parameter adjustment; constructing a mapping relation between X and service performance to obtain M service performance prediction models, and combining the M service performance prediction models to obtain a Z = F (Y) = G (X) prediction model; and defining boundary conditions of each service performance, searching in an output space of the prediction model to obtain an optimal solution set Y *, and reversely deducing an optimal X *.

The invention relates to the technical field of crustal stress field inversion, in particular to a crustal stress field intelligent inversion method based on a deep learning algorithm. Comprising the following steps: S1, establishing a three-dimensional geomechanical model; s2, setting a plurality of undetermined influence factors, and respectively acting on the three-dimensional geomechanical model; s3, setting a stress component for each undetermined influence factor, and obtaining a training sample and a verification sample by taking the undetermined influence factor as an independent variable and the stress component as a dependent variable; s4, a CNN-LSTM-Attention deep learning model is constructed; s5, training the deep learning model based on the training sample, taking the stress component as input, taking the combined working condition as output, and obtaining a crustal stress inversion model after training; s6, based on the verification sample, performing prediction precision verification on the crustal stress inversion model, and when the prediction precision verification is not qualified, iterating the steps S1 to S5; and reliable and large-range ground stress field distribution characteristics are obtained.

The invention discloses a PM2.5 high-precision prediction method capable of fusing multi-source data and improving spatial resolution. The PM2.5 high-precision prediction method comprises the following steps: constructing an independent variable data set and a dependent variable data set of a research area; a PS-MSViT model of the combined inversion model comprises a multi-scale fusion module and a Pixel Shuffle downscaling module, and weighted regression processing of dependent variable data is carried out in a geographic map according to a target spatial resolution by using a dependent variable data set to obtain the PM2.5 concentration under the target spatial resolution; the multi-scale fusion module carries out feature extraction and fusion processing on the independent variable data set to obtain a feature map F1, the feature map F1 is divided, flattened and coded, and the Pixel Shuffle downscaling module rearranges the space to obtain a feature map F3 under the target spatial resolution; and finally outputting the predicted PM2.5 concentrations of all the position points of the research area by combining the inversion model. According to the method, high-precision PM2.5 concentration data can be obtained, and the method is of great significance to precise monitoring of air quality, public health risk assessment and environmental protection.

The invention discloses a shared bicycle passenger flow prediction method based on a geographically weighted regression model and a related device. The method comprises the following steps: collecting spatial data and social economic data of a shared bicycle putting point of a target city; inputting the effective independent variable data set, the spatial autocorrelation of the effective independent variables, the multi-collinearity test result and the housing price factor data into a least square regression model and a geographical weighted regression model, and outputting a regression coefficient, a significance level and a goodness of fit; and according to the regression coefficient, the significance level, the goodness of fit, the delivery point category and the actual shared bicycle passenger flow data, calculating the prediction precision and the lifting amplitude at different delivery point categories. According to the method, the spatial heterogeneity relationship between the shared bicycle passenger flow volume and the influence factors is considered through the geographically weighted regression model, the regression coefficient is adjusted according to different geographic positions, the influence characteristics of a local area are reflected more accurately, and the prediction precision is remarkably improved.

The invention belongs to the technical field of life science, and particularly relates to a biological age construction method and system based on clinical physiological and biochemical indexes and an accelerated aging condition evaluation method. Initial clinical indexes and actual ages of the participants are collected, and outcome variables of the participants are recorded in a follow-up visit mode and comprise death or survival conditions, cardiovascular disease morbidity conditions and corresponding event occurrence time; screening key clinical indexes according to absolute Spearman correlation coefficients of the initial clinical indexes and actual ages; the key clinical indexes and the actual age are used as independent variables, death is used as a dependent variable, and a relational expression between the biological age and the key clinical indexes and the actual age is obtained through a Gompertz proportional risk model. According to the method, the biological age is calculated based on conventional clinical physiological and biochemical indexes, and compared with the biological age calculated based on omics and image data, the method can be more conveniently and efficiently applied to the majority of people, and aging evaluation is incorporated into conventional medical care items.

This invention discloses a water treatment dosing control method and system based on quadratic programming, belonging to the field of water treatment process control and optimization technology. It collects historical water treatment operation data and constructs a mechanistic feature set, using the mechanistic feature set as the independent variable and turbidity reduction as the target variable. The turbidity reduction is used to represent the change in flocculation or sedimentation of suspended impurities in the water, and a full-variable regression model is constructed. Variables in the mechanistic feature set are screened, and the full-variable regression model is optimized using a stepwise regression method to obtain a simplified prediction model. Real-time influent water quality parameters are acquired and input into the simplified prediction model. The process of maximizing turbidity reduction in the simplified prediction model is transformed into minimizing a convex loss function, and the convex loss function is iteratively optimized using a hierarchical constrained projection gradient descent method to obtain the optimal dosing scheme. Through mechanism-driven modeling, convex function optimization, and hierarchical constrained projection, intelligent, efficient, and reliable control of the dosing process is achieved.

This invention discloses a wastewater energy-saving control method and system based on multidimensional variable data analysis, belonging to the field of data analysis technology. It includes analyzing the correlations between variable data at different treatment stages. These correlations include the relationship between independent and dependent variables, as well as the interaction relationships between independent variables, accurately describing the complex coupling relationships between multiple variables and providing a reliable foundation for subsequent regulation. The method determines the regulation delay and variability at each regulation node, considering the regulation lag and variable changes in the wastewater treatment process, thereby setting thresholds and generating evaluation indicators to guide subsequent wastewater regulation. By controlling the regulation strategy at each node based on the correlations between variable data and the regulation delay, the adaptability and accuracy of wastewater node analysis and control are improved, ensuring real-time wastewater treatment effects and enhancing wastewater treatment efficiency.

Provided is a mathematical model that can predict, with high accuracy, the saturated liquid density of a pure compound at 298.15 K, said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic and having 25 or less atoms (excluding hydrogen). The model is a quantitative structure-property relation model and is obtained by solving an optimal model from a plurality of multiple linear regression models by a step-by-step selection method, and the model takes some molecule descriptors in various molecule descriptors as independent variables, takes saturated liquid density under 298.15 K as a dependent variable, and takes the saturated liquid density under 298.15 K as a dependent variable. The value of the molecule descriptor included in the input model can be received in a short time, and the saturated liquid density at 298.15 K can be output, so that the saturated liquid density at 298.15 K of a compound formed by the molecule singly can be predicted for any molecule meeting the requirements of the invention as long as the specific value of the molecule descriptor included in the model is known.