35 results about "Stepwise regression analysis" patented technology

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention provides a mechanical transplanting seedling quality analysis method and device, and the method comprises the steps: carrying out the dimensionless processing of a plurality of soil physical property parameters, obtaining a first dimensionless sequence, and carrying out the dimensionless processing of a plurality of transplanting quality evaluation indexes under the condition of completing the mechanical transplanting of a plurality of transplanting seedlings through target soil, so as to obtain a second dimensionless sequence; obtaining a second dimensionless sequence; analyzing the correlation between each element in the first dimensionless sequence and each element in the second dimensionless sequence to obtain the correlation degree between the multiple soil characteristics and the transplanting quality; and carrying out regression analysis on the multiple correlation degrees based on a stepwise regression analysis algorithm of statistical quantiles and a significance test algorithm, and predicting the transplanting quality of the transplanted seedlings by utilizing actual soil physical properties through an empirical formula obtained through analysis. By means of the method, the efficiency of mechanical seedling transplanting is improved, and the appropriate trends of duckbilled mechanical transplanting of plug seedlings in different soil environments are defined.

The invention discloses a phosphorus-efficient choerospondias axillaris family breeding method, and relates to the technical field of agricultural breeding, and the method comprises the following steps: step S4, index calculation, and according to the above data, selecting the choerospondias axillaris family; calculating the root-crown ratio, the total phosphorus utilization efficiency, the low-phosphorus stress resistance coefficient of each index, the membership function value of the comprehensive index, the weight of each comprehensive index, the comprehensive value of the low-phosphorus resistance efficiency, and the comprehensive value of the phosphorus efficiency under different phosphorus supply conditions; and S5, screening low-phosphorus-resistant identification key indexes, performing stepwise regression analysis by taking the low-phosphorus-resistant efficiency comprehensive evaluation value as a dependent variable and the low-phosphorus-resistant stress coefficient of the measured index as an independent variable to obtain a multiple regression equation, and screening out the low-phosphorus-resistant identification index of the choerospondias axillaris. The problems of long time consumption, large workload, no breeding system and the like of the traditional breeding method are solved. According to the method, the choerospondias axillaris families which are resistant to low phosphorus and efficient in phosphorus and aim at the problem that phosphorus in soil is generally lacked are screened out preferentially, existing germplasm resources are sequenced according to screening targets, optimal germplasm resources are screened out, and the target range is narrowed.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The invention discloses a method for forecasting anthracnose of wine grapes, and particularly belongs to the technical field of plant disease and insect pest forecasting. The method comprises the following steps: carrying out intra-group correlation analysis on climatic factors of threshold number of days before the wine grape anthracnose attack through SPSS to obtain meteorological factors significantly related to the wine grape anthracnose; through stepwise regression analysis, redundant climate factors in the meteorological factors significantly related to the wine grape anthracnose are removed, a multiple linear regression model is constructed, the meteorological factors significantly related to the wine grape anthracnose are screened out, and multicollinearity is avoided; secondly, inputting meteorological data to be measured into the multiple linear regression model, and outputting a prediction result of the disease index of anthracnose; and finally, forecasting the anthracnose of the wine grapes based on the prediction result of the disease index of the anthracnose. The method can prevent and control anthracnose, improve wine grape quality and reduce economic loss.

This application relates to the field of metal bending component manufacturing technology, and discloses a method for controlling the bending radius in progressive forming of tubular materials by spinning and bending. The method includes the following steps: S1, obtaining an optimal combination of loading parameters affecting the forming quality indicators of the tubular material through orthogonal experiments; S2, establishing a regression function between the bending radius and the spinning and bending deformation parameters based on the optimal combination and the Maximin Latin square test results through stepwise regression analysis; S3, testing the regression function; if the test passes, obtaining the regression model of the bending radius; if the test fails, returning to step S2; S4, controlling the bending radius under the optimal loading parameter conditions using the regression model. This application incorporates a diameter reduction parameter into the control method, significantly improving the accuracy of bending radius control compared to existing geometric control models. It reduces the average error between tubular bending radius prediction and control from 10.28% to 5.2%, offering advantages such as high forming accuracy, wide applicability, and higher reliability.

The invention discloses a lung cancer typing risk screening method based on joint risk assessment, which relates to the technical field of immunoassay and mainly comprises the following steps: screening model variable basic factors of each lung cancer typing by combining single-factor regression analysis with multi-factor stepwise regression analysis; carrying out normalized distribution processing on the normalized marker detection data, analyzing the distribution difference of each marker between healthy people and each lung cancer type group, and screening model variable marker factors of each lung cancer type; according to the screened basic factors and marker factors, constructing a risk assessment model in which a marker likelihood probability and a sample basic risk probability are combined based on a Bayesian formula; and performing risk grade assessment of each lung cancer type under Cuff value optimization on sample data through the constructed risk assessment model. Specific prior model variables are screened for different types of lung cancer, so that the model can realize screening and evaluation of different types of lung cancer.

The invention provides a method for selecting an index combination used for constructing an industrial park carbon emission evaluation model, and the method comprises the steps: according to a correlation analysis result, through a stepwise regression analysis method, calculating the index combination from a night light index, a park scale index and a social economic index, and selecting an index combination in which sub-indexes determined to have no correlation with the total amount of carbon emission based on the correlation analysis result are deleted as an industrial park carbon emission evaluation index combination. Therefore, the invention provides the method for selecting the index combination for constructing the industrial park carbon emission evaluation model, and personalized carbon emission evaluation model index selection can be carried out for a specific industrial park.

The present invention relates to the technical field of dynamic prediction models, and discloses a method for constructing a dynamic prediction model for tobacco diseases, which includes the following steps: S1: Dimensionless of meteorological factors. First, collect meteorological factor data and tobacco disease observation data in the pathological area, including survey records of brown spot and common mosaic virus disease, and meteorological observation station data including the average value and extreme value of meteorological data; S2: Calculation of disease factors; S3: Data processing. Analyze the calculated data through Python 2.0 programming for correlation analysis and stepwise regression analysis; S4: Establish a disease dynamic prediction model. The present invention uses a composite factor of meteorological factors and the previous disease condition, and at the same time considers the influence of wind, which is more targeted. In terms of the prediction period, taking 5 days as the prediction period, which is consistent with the step length of short-term weather forecasts, and the accuracy of short-term weather forecasts is relatively high. Therefore, the reliability of the disease prediction model proposed by the present invention is significantly improved and the practicability is stronger.

The invention relates to the technical field of steel smelting, in particular to a comprehensive metallurgical performance judgment method, system and equipment for steelmaking lime and a medium, and the method comprises the steps that furnace information of multiple historical furnaces is obtained, the furnace information comprises independent variable parameters and dependent variable parameters, and a historical database is established; dividing a secondary database according to a preset interval; stepwise regression analysis and symmetric neural network operation are performed on each secondary database, first and second optimal combination information is extracted, and third optimal combination information is generated through a recurrent neural network; constructing a metallurgical performance rating model based on all the third optimal combination information; and inputting the heat information of the to-be-evaluated heat into the lime metallurgical performance rating model to obtain the metallurgical performance grade of the lime used by the heat, supplementing the heat information into the historical database, and updating the lime metallurgical performance rating model. According to the method, the judgment comprehensiveness, precision, real-time performance and long-term adaptability can be improved.

The invention discloses a method for calculating a target heart rate of exercise, which comprises the following steps of: 1, obtaining the consensus of the relation among age, resting heart rate and target heart rate in the biomedical field and the sports science field at present by looking up academic literatures; 2, designing a research scheme, and determining a method and a tool for collecting data; 3, collecting data on site to obtain 324 samples; 4, through statistical analysis, carrying out multivariate stepwise regression analysis by taking the heart rate after exercise as a dependent variable and taking age and resting heart rate as independent variables, so as to obtain a double-drop target heart rate model; 5, establishing and calculating target heart rate matrix data, determining the age range to be 20-70 years old, determining the resting heart rate range to be 55-95 times per minute, and calculating the matrix data of the target heart rate lower limit value; the problem of correctly calculating the bull's-eye rate according with the medical physiology principle is solved.

The present invention provides a method for optimizing process parameters for selective laser melting (SLM) based on statistical analysis, which belongs to the field of additive manufacturing. More specifically, the method includes the following steps: preparing samples using a selective laser melting process and performing performance tests on them to obtain experimental data on the performance corresponding to different process parameters; establishing polynomial regression models for different performances based on the experimental data; optimizing the polynomial regression models for each performance using a stepwise regression analysis method, and adjusting the thresholds in the stepwise regression analysis method using a one-dimensional search method to obtain prediction models for different performances, which are then used to optimize process parameters. The present invention provides a fast and accurate method for predicting the performance of SLM products, which achieves rapid optimization of process parameters for target performance products manufactured by SLM. It can quickly, efficiently, and cost-effectively explore the optimal SLM process parameter range, effectively reducing scrap rates, improving the quality of formed parts, and increasing production efficiency.

The invention relates to the technical field of data models, in particular to a yellow rice wine quality prediction method, which comprises the following steps: after establishing a yellow rice wine sensory index evaluation table, respectively carrying out sensory analysis on Fujian yellow rice wine in different years to obtain a comprehensive taste actual score; carrying out correlation and significance analysis on physical and chemical components and sensory indexes of the Fujian yellow wine; performing correlation and significance analysis on the color and sensory indexes of the Fujian yellow wine; performing correlation and significance analysis on volatile matter indexes and sensory indexes of the Fujian yellow wine; selecting parameters with significant levels of less than 0.05 in physical and chemical components, color and volatile matters as independent variables, and establishing a sensory comprehensive evaluation and prediction model by taking the comprehensive taste actual score as a dependent variable and adopting stepwise regression analysis; according to the method, the Fujian yellow rice wine in different years is researched, the quality of the Fujian yellow rice wine is evaluated through sensory analysis, electronic nose analysis and physical and chemical analysis, sensory indexes are quantified, datamation indexes for directly evaluating the quality of the Fujian yellow rice wine are obtained, and management and grading are facilitated.

The invention discloses a method for evaluating the anti-brucellosis character of sheep, and the method specifically comprises the following steps: respectively infecting the sheep with an equal amount of pathogens according to the weight ratio of the sheep in an eyeshade manner; the method comprises the following steps: collecting serum 30 days after infection, measuring the serum Brucella antibody level and serum IL-12 and IL-17 cell factors, taking left parotid gland lymph nodes and right parotid gland lymph nodes of an infected object for general pathological semi-quantitative evaluation, and measuring the bacterium loading amount of the left parotid gland lymph nodes and left submandibular lymph nodes; and carrying out standardization processing on the measured data, and calculating by using a stepwise regression analysis method to obtain an anti-brucellosis index R of the sheep so as to evaluate the disease resistance of the sheep. The index for comprehensively evaluating the anti-brucellosis character of the sheep is constructed, so that the evaluation result is more accurate, and the limitation and uncertainty of judging the disease resistance character by a single immune index are overcome.

The application discloses a corn variety screening method suitable for soybean-corn 4:2 and 6:4 strip composite planting. Firstly, newly approved or large-area popularized corn varieties are selected, and different growth environment conditions of edge 1 row, edge 2 row and edge 3 row in each variety plot are created through high-density, non-protection row and relatively wide plot spacing. The agronomic characters, dry matter, yield and constituent factors of the edge 1 row, edge 2 row and edge 3 row are analyzed by using the principal component analysis, fuzzy membership function method, stepwise regression analysis and grey correlation degree analysis method, the key indicators reflecting the edge row advantage of corn are selected and identified, and the edge row advantage of the tested corn varieties is evaluated, so that the corn varieties with strong and weak edge row advantages are screened out, and the corn varieties are recommended as suitable for soybean-corn 4:2 and 6:4 strip composite planting. The screening method is suitable for providing reference for corn varieties of different strip type modes of soybean-corn composite planting and improving the strip composite planting benefit.

The invention discloses a natural gas monthly sales volume main influence factor screening method, system and device, and belongs to the technical field of natural gas data, and the main influence factor screening method comprises the steps: preprocessing natural gas monthly sales volume influence factors and natural gas monthly sales volume data; performing correlation analysis on the relationship between the influence factors and the monthly natural gas sales volume to obtain an influence factor set A; determining contribution values of the influence factors to the natural gas monthly sales prediction result to obtain an influence factor set B; determining importance indexes of the influence factors on the monthly natural gas sales volume to obtain an influence factor set C; determining a minimum public set of the influence factor set A, the influence factor set B and the influence factor set C; and through stepwise regression analysis, screening out main influence factors from the minimum public set. According to the method, the main influence factors of the monthly natural gas sales can be comprehensively and accurately identified and screened, and the precision and interpretability of the prediction model are improved.

The invention relates to a radio telescope pointing error correction method based on parameter optimization, and belongs to the technical field of radio telescope pointing correction. According to the method, an error source influencing the pointing precision of the radio telescope is analyzed, a 22-parameter model is adopted as an original regression model, a stepwise regression analysis method is applied, the original regression model is optimized, correction terms with redundant contributions are eliminated, after stepwise regression analysis is completed, corrected parameters are further verified through an MCMC method, and the accuracy of the pointing precision of the radio telescope is improved. And determining a final correction model according to parameter distribution obtained by an MCMC method, and completing error correction. According to the method, redundant items can be eliminated, the significance of each correction item can be checked one by one, the simplicity and the high efficiency of the model are ensured, and the stability and the prediction capability of the model are enhanced.

The invention provides a pathogen identification method and device for a greenhouse, and the method comprises the steps: obtaining meteorological factor data based on a preset meteorological sensor, and obtaining pathogen data based on a pre-constructed pathogen automatic monitoring device; inputting the pathogen data into a pre-trained pathogen recognition model to obtain the spore number of target pathogen spores; inputting the meteorological factor data and the spore number into a pre-constructed pathogen prediction model to obtain a predicted disease index; determining a pathogen quantity grade according to the predicted disease index; wherein the pathogen recognition model is obtained by performing fine adjustment on a preset YOLO model; the pathogen prediction model is constructed by utilizing a historical sample data set through multivariate stepwise regression analysis. The pathogen automatic monitoring equipment is adopted to dynamically monitor the target area, the pathogen identification model is used to identify the spore number of the target pathogen spores, the pathogen prediction model is used to predict and obtain the predicted disease index, the accuracy is high, and the speed is high.

The invention discloses a method for evaluating bean curd quality by using soybean protein structure indexes and application thereof, and belongs to the technical field of food evaluation. The invention provides a method for evaluating bean curd quality by using a soybean protein structure index in order to overcome the defects that in the prior art, resource waste and economic loss are caused due to the fact that soybean food is produced by adopting mixed varieties in the soybean processing industry, and a method capable of accurately evaluating bean curd applicable soybean varieties is lacked. Protein structure indexes of different varieties of soybeans and quality indexes of bean curds prepared from different varieties of soybeans are utilized to carry out principal component analysis, and stepwise regression analysis is adopted to establish an evaluation system of a linear regression equation. According to the method for evaluating the quality of the bean curd, objectivity and accuracy of bean curd processing suitability evaluation can be improved, and technical support is provided for rapid and accurate bean curd processing suitability evaluation in the soybean industry.

The invention discloses a stepwise regression analysis method-based college student physique test score prediction model, and belongs to the technical field of sports and metering economics. The method comprises the following steps: collecting and preprocessing college student physique test multi-dimensional index data; constructing a multiple linear regression model between the total physical test score and a plurality of explanatory variables; screening significant variables by adopting a stepwise regression analysis method, and optimizing a model structure; performing multi-collinearity, heteroscedasticity and sequence correlation test and correction on the model; and finally verifying the validity of the variable set by using a random forest model. According to the method, key influence factors can be automatically identified from numerous physical indexes, a prediction model with high goodness of fit and high interpretation is constructed, accurate prediction of student physical scores is realized, and a scientific basis is provided for physical health management of colleges and universities.

The present invention relates to the field of credit scoring modeling, and provides a method for high-dimensional variable screening of credit variable data. The purpose is to solve how to fully screen high-dimensional variables, and to try to construct a new variable screening method to assist credit scoring modeling, so as to ensure the optimal accuracy of the obtained modeling model. The main scheme includes obtaining application data, loan data, overdue data, and credit data; intercepting data for a period of time, and preliminarily screening the extracted data according to the cumauc induction method, and selecting data with large AUC values; performing chi-square binning on the preliminarily screened variables, and the chi-square binning makes each data have a separate weight, introduces nonlinearity to the subsequent scoring model, reduces the risk of model overfitting, and selects data with higher weights; performs stepwise regression analysis on the screened data, and finally obtains all data that meet the screening conditions.

The application provides a kind of mechanized transplanting seedling quality analysis method and device, the method includes: a plurality of soil physical property parameters are dimensionless, obtain the first dimensionless sequence, and in the case where a plurality of transplanting seedlings are mechanized transplanted by target soil, a plurality of transplanting quality evaluation indexes are dimensionless, obtain the second dimensionless sequence;The correlation between each element in the first dimensionless sequence and each element in the second dimensionless sequence is analyzed, and the correlation between the soil characteristics and the transplanting quality is obtained;Based on the stepwise regression analysis algorithm and significance test algorithm of statistical quantile, the correlation is regressed and analyzed, and the transplanting quality of the actual soil physical property is predicted by the empirical formula obtained by analysis. The method improves the efficiency of mechanized transplanting seedlings, and determines the suitable trend of plug seedlings in different soil environments.

The invention provides a Bayesian network state evaluation system and method based on data imbalance processing improvement for old travel building facilities, and a storage medium. The evaluation method comprises the following steps: extracting multi-dimensional influence factors and index data of to-be-detected old travel building facilities; carrying out data cleaning, coding and unbalanced data processing to obtain a trainable data set; key characteristic parameters are determined through correlation analysis and stepwise regression analysis; and constructing a Bayesian network model, and importing the key feature parameters into the trained model to obtain an old travel building facility state evaluation result. According to the method, key feature parameters are screened in advance, correlation and stepwise regression analysis is carried out, then an improved sample resampling technology is applied, the model can better reflect the real condition of the state of the old travel building facility, an improved data balance method is applied to weaken poor evaluation model performance caused by data imbalance, and the evaluation accuracy is improved. And comprehensive assessment of the state of the old travel building facility is realized.

The invention provides a basin rainfall-runoff response relation determination method based on multi-factor influence quantitative analysis, and belongs to the field of hydrological forecasting and water resource management. The method comprises the following steps: firstly, selecting representative factor indexes of three dimensions according to flood related characteristics of typical sessions of a watershed; then, in combination with a Spearman correlation coefficient analysis and stepwise regression analysis method, evaluating the statistical correlation strength of each factor and the runoff production coefficient, screening the factors with relatively strong correlation, constructing a multiple linear regression model, and determining the interaction among the factors; and finally, grading the significant indexes based on a decision tree binning algorithm, dividing flood event groups, and checking and evaluating a grouping effect through inter-group statistics. According to the invention, a multi-factor coupling action mechanism is disclosed, a runoff and convergence mechanism can be accurately reflected, the precision and reliability of a flood forecasting model are improved, and scientific grouping and differential forecasting of flood events are realized.

The embodiment of the invention discloses a landslide monitoring data feature level fusion method and device, electronic equipment and a storage medium. The method comprises the steps that landslide body deformation parameters are acquired; acquiring a plurality of influence factors related to the landslide; determining a key influence factor in the plurality of influence factors through clustering analysis; through stepwise regression analysis, taking the key influence factor as an independent variable and the landslide mass deformation parameter as a dependent variable, realizing feature level fusion, and obtaining a landslide mass surface displacement stepwise regression model; according to the method, the monitoring data can be effectively utilized, the monitored landslide deformation condition and the influence factors are comprehensively analyzed, the correlation of the influence factors is found out, an effective mathematical basis is provided for landslide deformation monitoring analysis, and the accuracy of landslide prediction and forecast is further improved.

The invention relates to a method for estimating starch content in a cigar tobacco leaf airing process. Comprising the following steps: detecting color parameters of the front side and the back side of the cigar tobacco in the airing process by using a portable colorimeter, determining the starch content of the cigar tobacco in different airing stages, performing correlation analysis on data obtained in the previous step, performing linear stepwise regression analysis on the color parameters and starch on the basis of the data obtained in the previous step, and determining the starch content of the cigar tobacco. Establishing a starch content estimation model based on color parameters, verifying the established prediction model, and determining the starch content of the cigar tobacco leaves by using the verified prediction model. According to the method disclosed by the invention, the correlation between the color change of the tobacco leaves and the starch in the cigar tobacco leaf airing process is determined, and the estimation model is established, so that the starch content of the tobacco leaves is quickly and nondestructively detected in real time, and reference can be provided for improving the cigar tobacco leaf airing quality and optimizing the tobacco leaf airing process research.

The invention relates to the technical field of oil field chemical sealing water / gas channeling, in particular to a channeling sealing gel main control factor evaluation and optimization method, which comprises the following steps: screening factors and levels influencing the stability of gel based on a stability test experiment; forming a multi-target evaluation system including jelly strength, bound water content, polymer molecular weight, jelly network density, dehydration rate and the like, and configuring different weight coefficients to determine target response; a space sample point database is formed, stepwise regression analysis is utilized to establish a response surface function of the gel stability performance adaptive to the oil reservoir environment, and main control factors of the gel stability performance and an action mechanism of the main control factors are determined; and determining stable formation conditions of the gel system in the fixed aging time and the gel stability under the optimal parameters by using a gradient rising method. According to the method, the main control factors influencing the stability of the gel can be determined, the optimal adaptability of the gel and oil reservoir conditions is determined, and water channeling and carbon dioxide gas channeling channels are permanently and effectively blocked.

The application discloses a buried hill geology pattern identification method while drilling based on multi-source information fusion and belongs to the technical field of oil exploration and development, and comprises the following steps: S1, data collection and processing; S2, stepwise regression analysis method is used to optimize logging element parameters and drilling engineering parameters; S3, a multi-parameter fusion technology based on a LightGBM algorithm is used to build a buried hill geology pattern identification model, namely a LightGBM algorithm model; in the manner, the method combines logging element data and drilling parameters, adopts the stepwise regression analysis method to optimize sensitive parameters, and establishes a geology model identification model through the LightGBM algorithm, so that the identification speed is faster and the accuracy is higher compared with a traditional method; the method can provide a strong basis for efficient on-site exploration and development decision and is favorable to promoting the oil and gas reservoir exploration and development process.

The invention relates to the technical field of alien species invasion risk assessment, in particular to an alien plant invasion risk assessment method based on pollination ecology characteristics, which comprises the following steps: acquiring pollination ecology characteristic data of alien invasive species; acquiring the distribution area of alien invasive species in the evaluation region; all parameters contained in the pollination ecology characteristic data of the intruding species are used as independent variables, the distribution area of the intruding species in the evaluation region is used as a dependent variable, and a stepwise regression analysis method is adopted to establish an alien plant intrusion risk index calculation model based on the pollination ecology characteristics; and determining the alien plant invasion risk level by using the alien plant invasion risk index calculation model based on the pollination ecology characteristics. The alien plants with the invasion risk can be rapidly and accurately distinguished, the method has the advantages of being low in cost, high in accuracy and comprehensive in factor consideration, and the accuracy of invasion risk early warning can be greatly improved.

The invention belongs to the technical field of exploration and development of oil and gas reservoirs, particularly relates to a method and a system for calculating the porosity of a reservoir based on an X-ray fluorescence logging technology, and aims to solve the problem of how to calculate the porosity of the reservoir. The method comprises the following steps: performing an XRF element experiment on a rock core sample according to well depth to obtain the content of elements; screening to obtain analysis elements; performing a conventional physical property experiment on the core sample according to the well depth to obtain the conventional physical property effective porosity of the core of the sample well; screening the analysis elements to obtain regional sensitive elements; stepwise regression analysis is carried out on the regional sensitive elements and the conventional physical property effective porosity of the sample well, and a reservoir effective porosity multiple linear regression equation is established; and substituting the content of the sensitive element of any well depth of the sample well into the reservoir effective porosity multiple linear regression equation to obtain the corresponding conventional physical property effective porosity. The method can be used for evaluating the physical property of the logging reservoir and calculating the porosity of the reservoir.