112 results about "Weight analysis" patented technology

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention discloses a data real-time analysis and fault early warning method and system based on a digital electric port box transformer substation, and belongs to the technical field of power equipment monitoring and control systems. The method comprises the following steps: acquiring environmental parameters of each component of the box transformer substation in real time through a monitoring network, and performing data smooth denoising to obtain a parameter sequence; constructing a dynamic storage framework based on the sequence in combination with space-time attributes; extracting historical records of the target component to perform multi-dimensional sorting and change rate calculation, and identifying potential anomalies; when the change rate exceeds the limit and the distribution boundary breaks through, generating an alarm sequence and determining a preliminary abnormal mark; comparing the mark with the historical record, fusing the influence weight analysis verification accuracy, and obtaining a final early warning grading result; and parameter summary information is pushed to a control system according to a grading result, response feedback is obtained, and closed-loop control of the operation state is formed. According to the invention, the problem of control lag caused by data noise and delay is solved, and the timeliness, reliability and automation level of box transformer substation control are improved.

The invention belongs to the technical field of building design, and provides an optimization method and system based on the mix proportion of a heavy slag water permeable brick, and the method comprises the following steps: determining the mix proportion parameter of the heavy slag water permeable brick; establishing a performance data set of the heavy slag water permeable brick, and constructing a nonlinear mapping relation function between the mix proportion parameters of the heavy slag water permeable brick and the performance data set of the heavy slag water permeable brick; constructing a multi-objective function based on the nonlinear mapping relation function, establishing a multi-objective constraint condition, and constructing a multi-objective optimization data set according to the objective function and the multi-objective constraint condition; and performing weight analysis on each performance by adopting entropy weight TOPSIS, and substituting a weight value into the multi-objective optimization data set to determine an optimal mix proportion. According to the method, the RSM is combined with the NSGA-II and the entropy weight TOPSIS, so that the mix proportion is more accurate, and the adaptability is higher.

The invention relates to the technical field of data processing, and discloses a fault analysis method and system based on a label propagation algorithm. The method comprises the following steps: acquiring operation state data through a monitoring equipment network, extracting the data to obtain a system topology data set, performing label propagation fault analysis on the system topology data set to obtain node fault information, and performing fault influence weight analysis on each node based on the node fault information to obtain a maintenance priority, and inputting the influence weight data into the graph neural network for coping strategy analysis to obtain a target processing strategy. According to the invention, the efficiency of fault propagation analysis and the accuracy of decision making are improved.

The invention discloses a real-time virtual environment view layout algorithm based on spherical space guidance. The algorithm comprises the following steps that an original view is acquired and preprocessed; abstracting into a circular structure and completely mapping to a constructed spherical view projection structure; weight analysis is carried out, a preset threshold value is compared, a high-weight standard view set and a low-weight standard view set are divided, and dynamic batch layout optimization processing is carried out on the high-weight standard view set; constructing a space pressure function field for the low-weight standard view set, and determining a target position by using a space pressure field guiding method; smooth movement from the current position to the target position is achieved through a Bezier curve interpolation method; and when the viewpoint or view angle change exceeds the threshold value, re-calculating the layout until the change amplitude is smaller than the threshold value, and generating a final layout. According to the method, the spherical space projection and the adaptive mechanical model are combined, the large-scale view layout is optimized, the method has the advantages of being efficient, stable and real-time, and the data visualization effect in the virtual environment is improved.

The invention relates to the technical field of data verification processing, in particular to a consistency verification method and system applied to multi-source communication data. According to the technical scheme, historical data of various communication parameters of a plurality of data sources and target data obtained by performing consistency verification on target parameters through the historical data are obtained; on the basis of fluctuation correlation feature analysis of historical data and target data, obtaining an influence intensity index of each communication parameter on target parameter deviation; performing influence weight analysis according to the difference between the influence intensity indexes and the operation requirement value of each communication parameter to obtain a weighted difference score of inconsistent performance between the data sources; correcting partial judgment caused by historical stability difference in combination with the weighted difference score and the unreliability of each data source in the historical time period; and when the target parameter executes the verification task, marking the real-time data according to the inconsistency score corresponding to each data source, thereby realizing accurate identification of abnormal data.

The invention belongs to the field of clean coal separation, relates to a data analysis technology, and aims to solve the problem that raw coal in dense medium coal separation cannot be automatically matched with suspension density in the prior art, in particular to a suspension density intelligent matching and decision control system based on raw coal ash content. The decision control platform is in communication connection with a density test module, a density matching module, a weight analysis module and a storage module; before clean coal separation, the ash content of raw coal is obtained and marked as an original value, the initial density of suspension liquid of the raw coal subjected to dense medium coal separation is marked as a density value, the clean coal ash content and the coal separation duration in the dense medium coal separation process are marked as a fine separation value and a coal separation value correspondingly, and a test period is generated; and marking all dense medium coal preparation processes in the test period as test processes. According to the method, the suspension liquid density can be tested and analyzed in the clean coal separation process, and the corresponding initial suspension liquid density can be directly matched according to the category and ash content of raw coal.

The invention provides a flood inundation situation intelligent perception and lightweight analysis model construction method, and relates to the technical field of hydrological monitoring and flood control and disaster mitigation. Unmanned aerial vehicle LiDAR point cloud data and satellite multispectral image data are acquired, a differential manifold topographic representation model is constructed, the earth surface is regarded as a Riemannian manifold, and complex topographic features are accurately described; a lightweight submerging calculation model is constructed based on a manifold diffusion theory, and a Laplace-Beltrami operator and a multi-scale solving strategy are adopted to realize rapid and accurate prediction of a flood submerging range and water depth; through a multi-terminal early warning information pushing mechanism, full-process intelligent support is provided, the simulation time is shortened to be within 2 hours from traditional 24 hours, the submerging range prediction goodness of fit reaches 89.7%, the water depth prediction root-mean-square error is controlled to be 0.35 m, and the flow velocity prediction error in a gradient sudden change area is reduced by 12%.

The invention discloses a multi-source heterogeneous credit data dynamic cleaning and fusion analysis system, and belongs to the technical field of data cleaning and fusion, and the system comprises a multi-source data collection module which collects credit data and generates an original multi-source data flow; the data cleaning module is used for cleaning the original multi-source data stream to generate a clean data sequence; the entity analysis and sharing management module is used for extracting entity feature data in the clean data sequence and generating global entity state data and an incidence relation graph; the weight analysis engine is used for formulating a similarity calculation strategy, calculating entity similarity and generating weighted entity feature data based on an association path in the association relationship graph; and the incremental version management module is used for recording the state change history of the weighted entity feature data and generating an incremental data snapshot. According to the invention, a unified entity portrait library and a shared memory pool are constructed through the entity analysis and sharing management module, and dynamic credit data changes are effectively coped with.

The invention discloses a real-time optimization method for a reservoir water level prediction model, and particularly relates to the technical field of water level prediction. The method comprises the steps of processing a multi-source data sequence through a long-short-term memory network model to generate water level prediction intermediate data; performing attention mechanism analysis on the prediction intermediate data, identifying key factors influencing water level changes, and outputting feature weight analysis data; optimizing parameters of the long short-term memory network model to obtain optimized parameter data; integrating the feature weight analysis data and the optimization parameter data to obtain water level prediction fusion data; collecting and processing current water level data of the reservoir in real time to form a real-time reservoir water level data sequence; and inputting the water level prediction fusion data and the real-time reservoir water level data sequence into a long short-term memory network prediction model, judging whether the water level exceeds a preset warning value or not, generating real-time water level early warning information, and realizing accurate real-time monitoring and early warning of the reservoir water level.

The invention provides a personalized diet recommendation method for pregnancy diabetes management, and the method comprises the steps: processing diet option data inputted by a patient through an optimized blood glucose response model, and generating an alternative diet recommendation list if the carbohydrate content in the diet option data causes a predicted blood glucose fluctuation trend to exceed a safety range, determining a personalized diet scheme; analyzing the matching degree of the historical blood glucose fluctuation sequence and the personalized dietary scheme according to an iterative blood glucose reaction model, and if the matching degree is judged to be lower than a preset threshold value, re-classifying interaction modes among dynamic factors to obtain refined blood glucose influence weight analysis; and integrating multi-dimensional data from the obtained refined blood sugar influence weight analysis to generate final real-time personalized diet recommendation, and if it is judged that the final implementation of the personalized diet recommendation can stabilize the abnormal blood sugar fluctuation during pregnancy, outputting a complete diabetes management guidance sequence.

The invention discloses an electromagnetic environment-friendly radiation protection assessment method and system, and the method comprises the steps: obtaining electromagnetic field data containing power frequency and high-frequency components through a data collection device, generating a multi-dimensional index data set and a radiation characteristic distribution diagram through the combination of exposure time and a crowd sensitivity index through a computational simulation method, and accurate quantitative analysis of the complex electromagnetic environment is realized. On the basis, a weight analysis method is adopted to fuse effect parameters to calculate a risk quantitative score, when the score exceeds a safety threshold value, radiation limit value data is automatically extracted to generate a visual risk map, and a processing module is linked to output a grading protection instruction. By activating protection configuration in the sensor network, decision unit parameters are dynamically adjusted according to real-time electromagnetic field data, and an optimized protection scheme including a dynamic strategy is formed. According to the invention, the conversion from passive protection to active early warning is realized, and the intelligent level and protection effect of electromagnetic radiation protection are significantly improved.

The invention provides a traffic flow simulation scene extraction method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting static scene elements and dynamic scene elements from a data source after data preprocessing; performing data fusion on the static scene elements and the dynamic scene elements to generate a plurality of structured reference simulation scenes; performing weight analysis processing and state probability distribution processing on each scene element in each reference simulation scene by adopting an analytic hierarchy process and an information entropy theory, and determining scene complexity of the reference simulation scene; and screening out a high-complexity simulation scene based on the scene complexity, and outputting a target simulation test scene set for testing the automatic driving system. Information of various data sources can be fully utilized, and the authenticity and complexity of a simulation scene are improved. Meanwhile, through the analytic hierarchy process and the information entropy theory, the characteristics of the actual traffic environment can be reflected more accurately, and more effective support is provided for testing and verification of the automatic driving automobile.

The invention provides an unmanned distribution vehicle path planning method and system, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining distribution task point information and environment map data; by improving a sparrow search algorithm, joint optimization is carried out on a distribution task point access sequence and path nodes, and an initial global path is generated; segmenting the initial global path to obtain a plurality of path segments; local dynamic obstacle avoidance is carried out through a dynamic artificial potential field method of real-time fusion sensor data, and a local correction path is generated; carrying out weight analysis on the plurality of optimization targets through an analytic hierarchy process, and constructing a multi-target optimization model; embedding the multi-objective optimization model into a hybrid optimization framework formed by an improved sparrow search algorithm and a dynamic artificial potential field method, and performing global and local collaborative optimization on the local correction path to obtain a global optimization path; and in a simulation environment, verifying the global optimization path through a vehicle dynamics model, and outputting a final planning path.

The present application relates to the technical field of packaging box verification, and discloses a multi-source information verification system for product packaging boxes before storage. The system comprises a multi-source information acquisition module, which acquires packaging box stereo vision sequences, weight dynamic sampling and three-dimensional point cloud measurement data; a multi-modal feature learning module learns multi-scale space-time features from the vision data to generate vision space-time feature tensors; a dynamic weight analysis module decomposes the weight data in the frequency domain to generate weight frequency domain feature maps. A graph structure fusion module constructs a heterogeneous information graph from the two, generates a fusion verification graph feature through a graph neural network, an abnormal pattern recognition module generates an abnormal confidence distribution through a pre-trained variational autoencoder, an intelligent decision module outputs abnormal positioning and defect classification results accordingly, and an adaptive optimization module generates parameter adjustment strategies and transmits them to the storage system. The system realizes deep fusion of multi-source information, improves verification accuracy and intelligent level, and helps optimize the warehouse process.

The application belongs to the technical field of garden greening construction, in particular to a garden greening engineering construction and maintenance task collaborative control system, comprising: an AI multi-factor coupling prediction and early warning module, a multi-dimensional coupling prediction model is constructed, a deep learning algorithm control logic is carried, full-amount data of construction and maintenance is associated and called through double-track coding, multi-factor weight analysis and logical operation are carried out, early warning of seedling maintenance risk is realized, and the system simultaneously has a construction scheme reverse check control function, full-amount associated data is called through double-track coding, a risk cause and disposal scheme integrated prompt is generated, the module is mainly responsible for early warning of seedling maintenance risk, integrates multi-dimensional data, analyzes by using an artificial intelligence algorithm, identifies potential risks, and provides corresponding early warning information and disposal suggestions.

The present application relates to the technical field of risk assessment, in particular to a risk grade determination method for planning and construction period of a no-parking energy supplement zero-carbon transportation system, comprising the following steps: obtaining facility vibration frequency time series data, performing difference processing and identifying abnormal points by using isolated forest, combining corresponding stress peak value, fatigue grade and slope parameter weighted analysis to output risk correction value, normalizing and weighting to generate risk score and labeling, extracting path node stress and risk value, using K-means clustering analysis to output risk node, screening and spatial clustering the risk node elastic recovery ratio and stress peak value, and outputting the risk grade determination result. In the present application, the vibration frequency data is processed by subsection difference, the mutation nodes are accurately positioned by combining abnormal detection, the multi-dimensional stress, fatigue and terrain parameters are weighted and matched, the risk is normalized and graded and labeled, the structure and environmental characteristics are integrated by sliding window clustering analysis, the risk monitoring granularity and response speed are refined, and the dynamic monitoring precision and linkage ability are improved.

This invention relates to the field of intelligent breeding technology, specifically disclosing a high-throughput intelligent screening method for multi-trait ginseng breeding materials based on deep learning. The method includes breeding data acquisition, high-throughput acquisition of hyperspectral phenotypes, construction of a multi-trait prediction model, model interpretability analysis, comprehensive screening of multiple traits, and verification of screening results. This scheme utilizes UAV hyperspectral remote sensing technology to achieve rapid and non-destructive determination of saponin content, significantly improving the throughput of phenotypic data acquisition. A multi-task learning architecture and deep neural networks are used to construct a multi-trait prediction model. By sharing representation layers, genetic associations between traits are captured, while retaining the specific information of each trait, achieving collaborative and accurate prediction of multiple traits. Through integrated gradient and attention weight analysis, key marker sites are accurately located, and their biological significance is revealed by functional annotation, enhancing the model's transparency and credibility.

The application provides a high-steel-grade pipeline safety scoring method, device and system and a storage medium, and belongs to the pipeline evaluation field. The method comprises the following steps: scoring material data to obtain an initial material score set; scoring load data to obtain an initial load score set; scoring defect data to obtain an initial defect score set; performing weight analysis on the initial material score set, the initial load score set and the initial defect score set to obtain a target material weight set, a target load weight set and a target defect weight set; and performing scoring calculation on the initial material score set, the initial load score set, the initial defect score set, the target material weight set, the target load weight set and the target defect weight set to obtain a pipeline safety score. The application masters the safety state of the high-steel-grade pipeline, solves the problem that the commonly used safety evaluation method cannot be directly used for the high-steel-grade oil and gas pipeline, and provides a basis for pipeline safety management.

The invention relates to the field of data processing, in particular to an enterprise department and wound attribute scoring method and system based on big data analysis, and the method comprises the steps: firstly responding to the query of a user side for a target enterprise, obtaining multi-dimensional data and enterprise attributes of the target enterprise, then cleaning and complementing the multi-dimensional data to obtain to-be-processed data, and then carrying out data processing on the to-be-processed data; and according to the enterprise attributes, calling the corresponding weight analysis model, finally inputting the to-be-processed data into the weight analysis model, and calculating to obtain the medical and invasive attribute score of the target enterprise. Compared with the prior art, the method provided by the invention solves the technical problem of unreliable and inaccurate medical wound attribute scoring in the prior art.

The invention discloses a submarine cable risk area dynamic division method and system based on spatio-temporal clustering and dynamic weight analysis, and the method comprises the steps: multi-source data collection and preprocessing: collecting multi-source spatio-temporal data related to submarine cable risks, and carrying out the cleaning, normalization, abnormal value elimination and missing value filling of the multi-source spatio-temporal data, performing space-time alignment on the data by using Kalman filtering and smooth interpolation; spatio-temporal feature extraction and three-dimensional spatio-temporal cube model construction: extracting spatio-temporal features from the acquired multi-source spatio-temporal data, and dynamically constructing a three-dimensional spatio-temporal cube model of submarine cable risks based on the spatio-temporal features; streaming density clustering analysis: carrying out streaming clustering analysis on data in the three-dimensional space-time cube model by adopting a clustering algorithm, introducing a dynamic weight distribution mechanism, and adjusting the weight of a dynamic risk index; and risk assessment and region dynamic division: forming a risk index based on a clustering result to carry out submarine cable risk assessment so as to dynamically divide risk region grades.

The present application relates to the technical field of healthcare informatics, in particular to a smart ward patient nursing tracking and danger early warning system based on cloud computing, which comprises a data acquisition device, a segmentation module, a importance analysis module, an attention weight analysis module and a data uploading module, by acquiring different types of physiological data and motion state data of a plurality of patients with the same disease condition in their recovery period, and different types of environmental data of the ward where the plurality of patients are located, determining the comprehensive importance of the patients to different types of physiological data, and further determining the attention weight of any patient to different types of physiological data; based on the attention weight, determining the uploading frequency of any patient to different types of physiological data and uploading data. The present application adaptively determines the uploading frequency of the patient to different types of physiological data and uploads data, effectively avoiding the delay of patient tracking and nursing, and ensuring the reliability of nursing tracking.

The application discloses an industrial malodor online monitoring system based on big data, which comprises a data acquisition module, a pretreatment module, a feature fusion module, a multi-source perception weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module; data interaction between the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a time stamp is established in a distributed database; the application builds a new paradigm of industrial malodor monitoring with environmental intelligent adaptability through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and credible data governance architecture; four technical effects complementarily support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

A machine vision-based method for automatic measurement and deviation correction of workpiece dimensions, relating to the field of precision manufacturing technology, comprises the following steps: First, data from multi-process workpieces is collected and preprocessed to form a complete basic dataset; then, a dedicated feature anchor point benchmark library is constructed; next, self-calibration and coordinate unification are performed based on this library; subsequently, error decoupling and weight analysis are conducted to calculate the total deviation and the weights of various errors; finally, errors are corrected based on the relevant results, measurement results are output, a residual optimization model is fed back, the correction accuracy is verified, and the processing accuracy is improved. This invention constructs a dedicated feature anchor point benchmark library, achieving precise correlation and dynamic self-calibration between anchor points and errors, and unifying the coordinate system; through algorithms such as spatiotemporal decoupling of anchor point constraints, the total dimensional deviation across the entire process is accurately decomposed, error separation and correction are completed, and feedback correction is introduced to form a fully closed-loop system, improving the dimensional accuracy and overall stability of precision workpiece processing.

The invention discloses a geometric-process comprehensive parameter optimization method, system and equipment for BGA welding spot reflow soldering. Designing a geometric model of the BGA welding spot, and storing data as a form as a data set based on a preset geometric-process parameter level; performing finite element simulation of the reflow process by using an orthogonal test method to obtain a deformation value of the welding spot; the method comprises the following steps: carrying out range analysis to obtain a geometric-process parameter level corresponding to the minimum deformation of a welding spot after reflow soldering, carrying out finite element simulation to obtain the minimum deformation, carrying out weight analysis to obtain parameters with obvious influence, and sorting the parameters, so that the geometric structure and reflow process parameters of the welding spot are quickly optimized under the finite simulation results, and the optimization efficiency is improved. According to the method, the deformation value of a welding spot after reflow soldering is reduced, meanwhile, each geometric-process can be evaluated, the weight of each geometric-process parameter is obtained, the key optimized parameter is determined, and therefore a certain reference significance is provided for the actual reflow soldering process.

The invention discloses a cloud model-based tailing dam rainfall stability risk assessment method. The method comprises the following steps of S1, establishing a complete index system; s2, carrying out denoising preprocessing on the monitoring data by adopting a variational mode decomposition (VMD) algorithm; s3, carrying out weight analysis, and determining a combined weight; and S4, constructing a cloud model, determining a standard cloud of the cloud model, and judging a risk level. According to the method, the VMD algorithm is innovatively introduced to carry out denoising reconstruction on the monitoring data, original noisy data does not need to be stored, only effective signal components are reserved, risk level distribution characteristics can be described only through expected value-entropy-hyper-entropy parameterized representation of the cloud model and by using three groups of core parameters, dependence of a traditional method on full-amount historical data is replaced, and the method is high in reliability and high in reliability. The problem that the size of the buffer area is difficult to match the data size is thoroughly solved; under the mechanism, the risk evolution key information can be completely reserved with lower storage cost, and risk misjudgment caused by data loss can be avoided.

The invention provides a risk factor quantitative analysis method and system applied to a sewage treatment system and a medium. The method comprises the following steps: acquiring an adjacent matrix representing a direct influence relationship among a plurality of risk factors in the sewage treatment system; performing interpretation structure model analysis based on the adjacent matrix, and determining a hierarchical structure relationship of multiple levels of risk factors; based on the hierarchical structure relationship, automatically generating a judgment matrix for an analytic hierarchy process through a preset rule; and calculating to obtain respective risk weights of the plurality of risk factors based on the judgment matrix. According to the embodiment of the invention, an objective algorithm is adopted to replace subjective expert scoring, the objectivity, repeatability and logic consistency of the risk weight analysis result are remarkably improved, the automation of the analysis process is realized, and the evaluation efficiency is improved.

The invention relates to a method for evaluating the physical performance of a paper-making reconstituted tobacco sheet base, and belongs to the technical field of reconstituted tobacco performance evaluation. The method comprises the following steps: detecting physical performance indexes of paper-making process reconstituted tobacco base samples with different raw material ratios and different pulping processes; screening physical performance indexes of the reconstituted tobacco base sample by taking preset tensile strength, softness and absorbability as targets; carrying out normalization processing on physical performance indexes of the screened reconstituted tobacco base sample, and then carrying out Spearman correlation analysis; carrying out entropy method weight analysis on the physical performance indexes of the screened reconstituted tobacco sheet base samples, and establishing a paper-making process reconstituted tobacco sheet base physical performance evaluation model; and evaluating the physical properties of the to-be-evaluated paper-making process reconstituted tobacco sheet base by using the paper-making process reconstituted tobacco sheet base physical property evaluation model. The quantitative and effective evaluation method is established for the physical properties of the reconstituted tobacco base.