29 results about "Single factor analysis" patented technology

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a postoperative sub-delirium syndrome risk prediction system for a cardiac surgery patient, and relates to the technical field of medical treatment, and the system comprises a clinical data collection module which is used for collecting multi-dimensional clinical data of the cardiac surgery patient in a perioperative period under extracorporeal circulation, the data comprises patient baseline information, preoperative neurocognitive function evaluation indexes, intraoperative physiological parameters and postoperative early-stage vital signs; and the risk identification module is used for utilizing a single-factor statistical analysis mechanism. According to the postoperative sub-delirium syndrome risk prediction system for the cardiac surgery patient, accurate prediction of the sub-delirium syndrome risk is achieved by collecting multi-dimensional clinical data of the patient in the perioperative period, such as baseline information, preoperative neurocognitive function, intraoperative physiological parameters and postoperative early-stage vital signs; the independent risk factors are scientifically screened and quantified by using single factor analysis and a multivariate Logistic regression model, and the problem of incomplete data dimensions in traditional evaluation is effectively solved.

The invention belongs to the technical field of oil and gas field development engineering, and particularly relates to a construction method of an emulsion generation kinetic model. The method is based on a micro-fluidic experiment, comprehensively considers seven factors of pore throat radius, pore throat form, pore throat coordination number, displacement environment temperature, surfactant concentration, injection speed and crude oil viscosity, and determines an undetermined coefficient of a function by utilizing a least square method through single factor analysis and orthogonal experiment scheme design; therefore, the functional relationship among the pore throat radius, the pore throat form, the pore throat coordination number, the displacement environment temperature, the surfactant concentration, the injection speed, the crude oil viscosity and the emulsion generation rate and the generation radius is defined, and an emulsion generation kinetic model is constructed, so that an emulsion generation theoretical system under the multi-factor coupling effect is perfected; and reference is provided for other similar chemical flooding.

The invention discloses a lithofacies paleogeography single factor analysis method, medium, equipment and product, and relates to the technical field of lithofacies paleogeography analys.The method comprises the steps that lithofacies description, well position coordinates and stratigraphic stratification data in logging interpretation data are obtained; performing binary classification on lithofacies types according to whether the lithofacies description accords with the definition of the target lithofacies; based on the stratigraphic data and the binary lithofacies type, three single factor indexes of each well are calculated, and the three single factor indexes are the total stratum thickness, the target lithofacies cumulative thickness and the rock-to-ground ratio; according to the well position coordinate data and the three single-factor indexes, well points in the whole area are paired in pairs, experimental variation function values under different lag distances are calculated, and experimental variation scatter diagrams of the three single-factor indexes are drawn respectively; kriging gridding interpolation is carried out on the scatter points, contour maps of the three single-factor indexes are drawn, and lithofacies paleogeography analysis is carried out by using the three contour maps. The lithofacies paleogeography single factor analysis is realized based on the logging interpretation data.

The application discloses a kind of long approach in paste flow slope angle prediction method, belong to filling technical field, solve the prediction of filling slope angle in prior art is based on experience formula or static homogeneous model, unable to consider the influence of waste rock dynamic settlement on slurry flow resistance and slope form Problem, the method includes based on paste raw material basic parameter construction paste similarity model, based on range analysis combined with interaction analysis influencing factor on the influence degree of flow slope angle, convert flow slope angle calculation model into slope angle satisfaction function, obtain slope angle optimal filling parameter;The present application can comprehensively study the influence of multiple influencing factors and their interaction on paste flow slope angle through the pre-design and implementation of orthogonal experiment scheme, avoids the one-sidedness that single factor analysis may bring, makes the influence law obtained more comprehensive and in-depth, and is different from the homogeneous flow model or simplified discrete element model that the existing numerical simulation method is mostly used.

The invention is suitable for the technical field of safety protection equipment, and provides a trumpet-shaped bionic cobweb helmet filling structure design method, which comprises the following steps of: starting from the characteristics of a cobweb and a honeycomb structure, extracting cobweb radial lines, spiral lines and honeycomb hexagonal elements through similarity analysis, according to the bionics principle, deep analysis is carried out from structure, function and boundary similarity, cobweb features are subjected to three-dimensional processing, a winding mode is changed, the cobweb features are fused with honeycomb features to establish a three-dimensional preliminary trumpet-shaped bionic cobweb structure, element and size ranges are determined, structural parameters are optimized through a single-factor analysis method and a response curved surface method, and energy absorption performance is improved. According to the trumpet-shaped bionic cobweb helmet filling structure, the protective performance and the structural stability of the helmet can be remarkably improved.

The invention belongs to the technical field of road safety situation awareness, and provides a rural road network safety situation awareness method. The method comprises the steps of traffic state data collection, road condition data collection, meteorological data collection, original data base map construction, basic data base map construction, single factor analysis, analytic hierarchy process and fuzzy evaluation processing, overall road network safety situation index calculation, virtual optimization, key road section detection and fragile road section recognition. Through analytic hierarchy process and fuzzy evaluation processing, overall road network safety situation index calculation, virtual optimization, key road section detection and fragile road section identification, low-cost dynamic safety situation awareness and management and control of the rural road network are realized, and the road service level and the road network operation capability are improved.

The invention discloses a clinical prediction model construction method and system for treating oligometastatic non-small cell lung cancer through radioactive particle implantation, and relates to the technical field of medical model construction, and the method comprises the steps: obtaining patient data, taking survival time as a main endpoint event, employing a Cox regression model to carry out single-factor analysis on factors affecting patient prognosis, and obtaining a single-factor analysis result; determining the relationship between each clinical feature and survival time, and incorporating the important factors influencing the prognosis into multi-factor Cox regression analysis to determine independent risk factors influencing the survival prognosis of the patient; generating a first clinical prediction model according to each independent risk factor nano-construction; cD3 + AC, CD4 + AC, CD8 + AC, combined treatment, complete treatment and the number of metastatic lesions are taken as variables together to be included in construction of a clinical prediction model, and a second clinical prediction model is manufactured and generated. Independent risk factors influencing the prognosis of a radioactive particle implantation treatment few-metastatic NSCLC patient are confirmed, the clinical prediction model is established, the treatment prognosis can be accurately predicted, and the treatment cost is reduced. And screening of dominant benefit people is facilitated.

The invention discloses an amphibious vehicle engine compartment heat dissipation shutter fin multi-objective optimization method, which comprises the following steps: establishing a fin physical model, selecting three geometric parameters as influence factors, and determining temperature, pressure loss and comprehensive performance factors as optimization objectives; unreasonable intervals are screened out through CFD single-factor analysis in combination with different working conditions of the amphibious vehicle, and the effective value range of each factor is obtained; 17 groups of response surface test schemes are generated by adopting a three-factor three-level Box-Behnken design; constructing a response surface model based on the scheme, and obtaining an optimization target, a factor interaction effect and a second-order regression equation through CFD simulation; verifying a model prediction value and an actual value; and determining an optimal model and design parameters. According to the method, an efficient closed loop is constructed through RSM and CFD, multi-objective optimization is achieved under key working conditions, heat dissipation and aerodynamic loss are considered, a comprehensive performance index and constraint system is established, and design efficiency and engineering suitability are improved.

The application discloses a kind of risk factors prediction model of occurrence of initial treatment bacterium positive pulmonary tuberculosis lung cavity, belong to medical data processing technical field, the clinical data of initial treatment bacterium positive pulmonary tuberculosis patient is collected in the application, using single factor analysis, Lasso regression analysis and multivariate Logistic regression analysis, male, diabetes, smoking, white blood cell count and platelet / hemoglobin ratio are screened as the independent risk factors of initial treatment bacterium positive pulmonary tuberculosis causing lung cavity, and based on this, nomogram prediction model is constructed.H-L goodness-of-fit test, receiver operating characteristic curve and decision curve analysis are verified, the model has higher prediction efficiency and clinical practical value.The application can help clinical advance to predict and intervene the cavity caused by high-risk population of pulmonary tuberculosis, provide new ideas for the diagnosis and treatment of bacterium positive pulmonary tuberculosis causing cavity.

The application discloses a history landscape element analysis and protection evaluation system based on image recognition and relates to the technical field of landscape analysis, solves the technical problems that only single or limited dimension data is used to make protection decisions, targeted analysis on the characteristics of different types of landscape areas is lacked, and it is difficult to accurately warn the landscape areas needing protection, and through setting dynamic buffer areas around the river history landscape area and the city history landscape area, the data of a certain area around the history landscape area can be monitored and analyzed, and early warning can be facilitated before the influence reaches the landscape area. Through the combination of the hydrological risk of the dynamic buffer area and the landscape area, the urban heat island influence index evaluation, the climate influence intensity and the analysis result of the second level management node, the key information influencing the protection of the history landscape can be comprehensively integrated, the one-sidedness of single factor analysis is avoided, and the protection decision is more scientific and reasonable.

The invention discloses a method for improving the reliability of a wheel spoke and rim assembly, and relates to the technical field of wheels. The method comprises the steps that control factors and noise factors of the wheel spoke and rim assembly are collected; performing primary screening on the control factors and the noise factors by using single factor analysis to remove redundant factors; designing an orthogonal simulation scheme based on the control factor to identify a main effect and an interaction effect of the control factor; optimizing the simulation analysis process of the control factor and the noise factor by using a Taguchi method based on the main effect and the interaction effect of the control factor so as to reformulate an orthogonal simulation scheme; and constructing a target function and analyzing an optimal factor group in combination with an orthogonal simulation scheme. According to the method, the reliability, the cost and the light weight of the wheel assembly are comprehensively optimized, and the consumption of computing resources is reduced.

The invention discloses a radio frequency relay optimization design method fusing big language model priori knowledge and Kriging-IAGA. The method comprises the following steps: firstly, screening out key structure parameters and material parameters which influence insertion loss through HFSS simulation and single factor analysis; then constructing a sample library by using a method of combining single factor analysis with an orthogonal experiment, and establishing a high-precision Kriging agent model; further introducing an improved IAGA to carry out multi-parameter collaborative global optimization, and adopting an HFSS to regularly check and correct the proxy model; lLM is further introduced to serve as a process constraint device, an electromagnetic performance-process implementation dual evaluation system is constructed, semantic scoring is conducted on manufacturability of an optimal solution through the LLM, and a fitness function is corrected. According to the method, the problems of long simulation period, difficulty in processing nonlinear coupling in an orthogonal test and the like in the traditional relay optimization design are solved, the insertion loss of a high frequency band is effectively reduced, and the consistency and radio frequency performance of a product are improved.

The application discloses a prediction model for diagnosing infectious mononucleosis in children and a construction method thereof. The application adopts single factor analysis and logistic stepwise regression to screen independent influencing factors by using confirmed IM inpatient cases and suspected IM but EBV-DNA negative child cases, and adopts multiple factor regression to screen six independent influencing factors of age, ALB, GLB, PLT, LYMPH# and LYMPH% to construct a diagnosis model. Researches show that in the confirmed diagnosis scene, the multi-index combined model with blood routine and liver function has excellent diagnosis efficiency (AUC=0.986), realizes the ideal balance of high sensitivity and high specificity, provides a hierarchical tool for the precise diagnosis of IM in different clinical scenes, and is suitable for the precise diagnosis of inpatient children.

The application relates to a method for predicting the risk of postoperative complications of an acute abdomen patient, comprising the following steps: collecting clinical data of the acute abdomen patient after receiving surgical treatment, and preprocessing the clinical data; screening relevant risk factor variables through single factor analysis and multi-factor analysis, and constructing a characteristic variable set; constructing a postoperative complication risk prediction model based on the characteristic variables; the prediction model is based on an XGBoost base model, a moderate greedy algorithm is used for step-by-step parameter optimization, a plurality of optimal parameter subsets are selected to construct a plurality of XGBoost sub-models, and the MGA-XGBoost combined prediction model is combined through a weighted integration mode; the MGA-XGBoost combined prediction model is used for predicting the risk probability of postoperative complications of the patient, and important risk factors in the top list are prompted. The scheme provided by the application can accurately capture the nonlinear and high-dimensional risk correlation in the clinical data of the acute abdomen patient, improve the prediction accuracy of the risk of postoperative complications, and improve the clinical applicability.

The invention relates to a method and system for predicting the anxiety mood prevalence rate of a diabetic child patient, and belongs to the field of artificial intelligence prediction. According to the method, multi-dimensional clinical data such as demographic data, treatment modes, management behaviors and metabolic indexes of diabetic children are collected, evaluation results of a children anxiety mood disorder screening table (SCARED) are combined, single-factor analysis, correlation analysis and multi-factor Logistic regression analysis are carried out, and an anxiety mood prediction model is constructed. The model takes age, an insulin treatment mode and glycosylated hemoglobin as core variables, and can accurately predict the anxiety risk of a child patient. The system comprises a data acquisition module, a psychological assessment module, an analysis modeling module, a risk prediction module and a report generation module, and realizes automatic data integration, model construction and individual intervention suggestion output. Clinical verification shows that the model prediction efficiency is excellent, and the method has good clinical application value and is beneficial to early recognition and intervention of anxiety mood of diabetic children and improvement of physical and psychological health and disease management quality of the diabetic children.

The invention discloses a lung adenocarcinoma prognosis prediction method based on machine learning. RNA transcription data and clinical information of a lung adenocarcinoma patient are collected from a database, and a data set is constructed; performing gene expression data preprocessing and difference analysis to obtain differential expression genes; sequencing the risk coefficients of the differential genes by using single-factor COX analysis; carrying out survival related feature recognition on the screened genes by virtue of eight machine learning algorithms related to survival analysis to obtain related genes with prognosis values; a machine learning combinatorial algorithm is utilized to train multiple prognosis models, C-index average values of the prognosis models are calculated and compared, and an optimal StepCox-RSF algorithm is selected to carry out patient layering and prognosis prediction model construction according to feature genes; according to the risk score of the patient, predicting the total survival rate and the risk grouping condition of the patient; according to the method, the reflecting capacity of the model to the immune microenvironment is remarkably improved, the prediction precision is optimized, and a scientific basis is provided for personalized treatment of lung adenocarcinoma patients.

The invention discloses a current heat supply pipe network leakage point prediction method and system, and relates to the technical field of intelligent heat supply pipe network management. Systematic risk quantification is carried out by integrating multi-dimensional monitoring parameters such as soil temperature, humidity and pipe network node types, the limitation of single-factor analysis in the prior art is broken through, and the prediction accuracy is improved. According to the method, the leakage risk can be comprehensively evaluated from multiple dimensions, so that the accuracy of leakage point prediction is remarkably improved, and meanwhile, the risk priorities of different point positions are clearly defined for operation and maintenance personnel through clear risk grade division, so that operation and maintenance resources can be accurately projected to a high-risk area, and the risk risk is improved. The problem of insufficient prediction precision caused by lack of multi-dimensional integration and risk grade division in the prior art is solved, improvement of operation and maintenance efficiency and timely management and control of leakage hidden dangers are achieved, safe and stable operation of the heat supply pipe network is effectively guaranteed, and the method is used for solving the problem of lack of multi-dimensional parameter integration and risk grade division in the prior art. And the leakage point prediction precision is insufficient.

The present specification discloses a polyvinylidene fluoride pyrolysis hydrogen fluoride generation absorption simulation analysis system and method, the system includes simulation calculation module, experimental verification module and parameter optimization module. The simulation calculation module is based on the finite element platform to construct the multi-physical field coupling simulation model, the experimental verification module obtains the actual data to calibrate the model, and the parameter optimization module optimizes the key parameters by using the response surface method. The method realizes the accurate simulation of the whole process of polyvinylidene fluoride pyrolysis and hydrogen fluoride generation absorption by constructing the model, setting the parameters, single factor analysis, multi-parameter optimization and experimental verification. The scheme strengthens the microwave heat effect, improves the heating efficiency, reduces the power consumption, efficiently absorbs hydrogen fluoride, provides theoretical support for related process optimization, and has environmental and economic benefits.

The application belongs to the technical field of oil and gas field development engineering, and particularly relates to a method for constructing an emulsion generation kinetics model. The method is based on microfluidic experiments, comprehensively considers seven factors of pore throat radius, pore throat morphology, pore throat coordination number, displacement environment temperature, surfactant concentration, injection speed and crude oil viscosity, determines the function undetermined coefficient by single factor analysis and combined orthogonal experiment scheme design and the least square method, thereby clarifying the function relationship between the pore throat radius, the pore throat morphology, the pore throat coordination number, the displacement environment temperature, the surfactant concentration, the injection speed and the crude oil viscosity and the emulsion generation rate and the generation radius, constructing the emulsion generation kinetics model, which not only perfects the emulsion generation theoretical system under the coupling action of multiple factors, but also provides reference for other similar chemical flooding.

The invention discloses an establishment method and an evaluation method of a post-stroke inflammation injury prediction model, and belongs to the technical field of post-stroke inflammation injury prediction, and the establishment method of the post-stroke inflammation injury prediction model comprises the following steps: 1, collecting clinical index data of an AIS group and a health control group; 2, after the AIS group and the healthy control group are subjected to tendency score matching according to gender and age, a matched AIS group with the same number of people as that of the healthy control group is obtained, and matching features of the healthy control group and the matched AIS group are obtained; step 3, difference and correlation analysis of matching characteristics between the healthy control group and the matched AIS group; 4, carrying out single factor analysis on fibrinogen level influence indexes in the AIS group; and step 5, inputting the screened confounding factors into a full-automatic machine learning model for model training and verification to obtain a post-stroke inflammation injury prediction Logistic regression model. The regression model established through the steps is good in sensitivity and high in accuracy when being used for predicting the post-stroke inflammatory injury.

This invention discloses a method and apparatus for predicting the risk of urinary tract infection (UTI) in children with urinary tract stones based on nomograms. The method includes: acquiring and preprocessing clinical data of pediatric patients to obtain a raw dataset; performing univariate analysis on the training set based on clinical indicators to obtain candidate variables significantly associated with UTI; performing multivariate logistic regression analysis on the candidate variables to obtain independent predictors; constructing a nomogram model based on the independent predictors; calculating a corresponding score for each independent predictor in the nomogram model; calculating the total score of the independent predictors and obtaining the prediction probability; and performing quantitative evaluation and curve analysis on the prediction probabilities to obtain the UTI risk assessment results. This invention is the first to organically integrate four indicators: gender, mean CT value, U-LEU, and NIT, greatly improving the efficiency of clinical decision-making.

The invention discloses a neonatal asphyxia risk prediction model construction method and device, which are applied to the field of computer models for disease prediction, and are used for acquiring clinical feature data of a neonatal asphyxia group and a healthy neonatal group, including pregnant mother information, fetus information and other information; performing single-factor analysis on clinical risk factors between the two groups of data, and screening out single-factor predictive variables; and by taking the screened variables as independent variables and taking whether suffocation occurs as dependent variables, carrying out binary Logistic regression analysis by adopting a forward stepwise method, and establishing a risk prediction model. A visual column diagram is drawn according to the prediction model, and the prediction model has good distinction degree and calibration degree through evaluation. According to the method, key independent risk factors are screened from multiple factors through a scientific statistical analysis method, a prediction model beneficial to early clinical recognition of neonatal suffocation high-risk groups is constructed, a basis is provided for timely making individual intervention measures, and the method is of great significance in preventing neonatal suffocation and improving the survival rate of neonates.

The invention relates to the technical field of hydropower station maintenance, in particular to a deep hole radial gate fluid-structure interaction vibration control and improvement method. A monitoring point assembly is arranged on a deep-hole arc-shaped service gate and a dam, vibration, stress, water flow characteristics and operation state data under different working conditions are collected, transmitted in real time and displayed; evaluating the influence of vibration on the gate structure and dam connection safety; and according to vibration inducements obtained through analysis, a numerical simulation analysis scheme is adopted, and corresponding gate vibration reduction measures are adjusted. Monitoring points are arranged on a gate main beam, a supporting arm, a lifting lug and a dam, vibration, stress, hydraulic jump and jet data are comprehensively collected, and the authenticity and comprehensiveness of the data are ensured. A single factor analysis method and FSI modeling are adopted, vibration contributions of factors such as water seal water leakage and bottom edge hydraulic jump are quantified, finally, according to vibration inducements, the balance weight, the bottom edge type and the water seal structure are optimized, and the vibration reduction effect is verified through numerical simulation.

A method, model, and system for screening predictive factors of perioperative hypoglycemia during gastrointestinal endoscopy, relating to the field of medical technology, is disclosed. This method employs univariate analysis and multivariate logistic regression to screen seven statistically significant factors from various factors influencing perioperative hypoglycemia during gastrointestinal endoscopy, defining these as hypoglycemia predictive factors. These seven factors are: blood urea nitrogen, preoperative blood glucose, glycated hemoglobin, nutritional status, laxative use, diagnostic category, and diastolic blood pressure. The outcome event of the predictive model is set as whether the patient experiences perioperative hypoglycemia, and the input factors are the seven screened hypoglycemia predictive factors. The predictive system includes a data input module and a hypoglycemia prediction module, with the hypoglycemia prediction module containing a built-in hypoglycemia risk prediction model. The model and system provided by this invention can provide hypoglycemia risk assessment for perioperative patients, assisting clinicians in decision-making.

The invention discloses a method for demarcating a groundwater reserve area. The method comprises the steps of information atlas construction, overlay analysis and optimization adjustment. Key indexes are screened through single-factor analysis, a vector diagram is drawn, and the vector diagram is rasterized through a Delphi method; and determining a weight by using an analytic hierarchy process, overlapping and screening an optimal region, and optimizing a delimiting result in combination with a hydrogeological unit and an administrative region. According to the method, the problems of single evaluation dimension, high subjective dependence and incomplete technical path in the prior art are solved, scientific and systematic accurate demarcation of the underground water reserve area is realized, and the method has wide popularization and application values.

The invention provides a living body prediction model for identifying the brain injury degree of a neonatal hypoxic ischemic brain injury (nHIBD) model mouse and a construction method of the living body prediction model, and belongs to the field of brain injury. According to the method, related data such as weight values and multi-dimensional behavioristics of the model mouse before and after nHIBD are acquired as predictive factors, and according to the result of the actual brain injury degree of the nHIBD model mouse, the function relationship between the brain injury degree of the model mouse and the predictive factors is innovatively established; through a prediction system combining an ordered multi-classification Logistic regression model and a statistical test system, the problem that after an nHIBD model is constructed, living model animals with the unified brain injury degree cannot be obtained, and then basic research results such as mechanism analysis and drug screening are affected is solved. According to the method, the limitation of single-factor analysis is broken through, the prediction accuracy is improved, a quantification tool with biological logic and statistical support is provided for early-stage in-vivo identification of different degrees of nHIBD brain injuries in basic research, and then support is provided for subsequent research.

The invention discloses a reservoir control factor quantification method and a medium, and the method comprises the following steps: S1, obtaining the data of a target region, and analyzing the geological action of the target region according to the data of the target region to obtain different control factors; s2, calculating a correlation coefficient between each control factor and the reservoir permeability; and S3, normalizing and sorting the correlation coefficients of the control factors to obtain a quantized result of the reservoir control factors. According to the method, through quantitative analysis of different control factors of the reservoir, the one-sidedness of single-factor analysis in the reservoir evaluation process is effectively solved, the weights of influences of different factors on the reservoir are quantitatively displayed, and the accuracy of cause analysis and prediction of the reservoir is improved.

This invention discloses a method for controlling process parameters in composite material winding molding: Experiments are planned on a winding platform, defining the ranges for prepreg resin content, pressure roller pressure, and winding speed. Interlaminar shear strength and tensile strength tests are performed on the test products to investigate the influence of individual factors and their coupling effects on mechanical properties. A predictive model is then constructed, and the optimal parameter combination is obtained through rigorous verification. Orthogonal experimental design is used in the experimental planning to ensure scientific rigor. High-precision testing machines are used to ensure data reliability in mechanical property testing. Single-factor analysis limits the step size and number of test groups. Coupling effect analysis utilizes the response surface methodology. The predictive model employs a deep learning neural network and undergoes optimized training. Model verification uses k-fold cross-validation. An improved genetic algorithm is used for optimal parameter search. Real-time monitoring relies on high-precision sensors. Data is also recorded through a relational database for traceability, comprehensively improving the quality and performance of composite material winding molding.