Grey Correlation Analysis Model Research Articles

Research on the Sales Distribution Law of Vegetable Products Based on Linear Fitting

Firstly, the hourly sales volume changes of vegetable categories and individual products are analyzed from the time dimension, and frequency distribution histograms are used to describe the distribution patterns of vegetable categories and individual product sales volume. Then, Spearman correlation analysis is used to obtain the distribution patterns of individual product sales volume, and linear fitting is used to compare and analyze the continuous sales volume of the six major categories for three years using a bar chart, From the demand side perspective, there is often a certain correlation between the sales volume of vegetable products and time. This article analyzes the relationship between vegetable categories and individual product sales volume from the time dimension. At the same time, based on the distribution patterns of the six major categories, Spearman correlation analysis was used to obtain the distribution patterns of individual product sales volume. Linear fitting was performed, and the continuous sales volume of the six categories for three years was compared and analyzed using a bar chart to obtain the general monthly and seasonal patterns of vegetable sales. Then, descriptive statistical analysis is used to analyze the distribution patterns of vegetable categories and single item sales volume, and the correlation relationships between different categories or single items of vegetable products are obtained. Finally, a grey correlation analysis model is established to predict the distribution patterns and interrelationships of vegetable categories and single item sales volume based on this model.

Research on Civic and Political Education and Economic Development in the Age of Digital Economy

Abstract This paper constructs the evaluation index system of Civic and Political Education and Digital Economy Development and uses the entropy value method to calculate the weight value of each index in the evaluation system. A gray correlation analysis model is established to explore the degree of correlation and relevance of indicators in the evaluation system of ideological and political education and economic development. Analyze the synergistic development between the two subsystems of ideological and political education and digital economic development by using the coupling degree and the coupling coordination degree analysis method, and explore the degree of coordination of the coupling development between the two. The analysis of the relationship between civic and political education and economic development in the sample sites found that the integrated gray correlation between the two is 0.610, which is a moderate level of correlation. The coupling degree of the two is increasing year by year, and it will reach 0.525 in 2023. On average, the coupling coordination degree reaches 0.610, which belongs to primary coordination. This paper provides data support for the improvement of the coupling coordination between ideological education and economic development in the era of the digital economy. It lays the foundation for the common development of ideological education and economy.

A Study on the Extension of Chinese Traditional Culture in College Art Education Combined with Gray Correlation Analysis Methods

Abstract This study incorporates gray correlation analysis from gray statistical theory to assess the quality of traditional culture and art education. It establishes an evaluation index system and a gray correlation analysis model, examining the relationship between contemporary art education and traditional culture. The model is applied to identify the correlation, key characteristics, and principal influencing factors of traditional Chinese culture and art education in higher education institutions. Additionally, it examines the advantages of random sequences of factors through specific data processing techniques. The structural model tests the influencing factors on the quality of traditional culture and art education, with an analysis of path coefficients. The results showed that 97.4% of teachers chose traditional culture and art teaching resources, mainly by referring to the network aspect. The results of the weight distribution indicate that the teaching process U3 (0.3613) is the most important in traditional culture and art teaching, and the innovative activities in teaching content (0.8113) hold the largest weight. The top three gray correlation values of the indicators of the second-level factor layer are U43>U22>U32. The direct influence effect of excellent traditional culture permeating the teaching process is 0.232, which has the greatest influence on the quality of teaching. The direct impact of excellent conventional culture permeating teaching content is 0.184, which has the second largest influence on teaching quality. This paper successfully constructs a gray correlation model to analyze the relationship between traditional culture and contemporary art, which has high scientific validity.

Study On the Composition of Glass Products Based on Soft Voting Model and Grey Correlation Analysis

The inspection and identification of ancient glass composition has always been an important research content, this paper on the ancient glass as a medium, for its chemical composition, weathering characteristics and classification rules and other aspects of the study, the establishment of a soft voting integrated learning model to random forest algorithm, SVM algorithms and GBDT algorithms as a base classifier, the use of soft voting on the base classifier for the classification of the results of the integration of the learning, and then to the The chemical composition of unknown artefacts is analysed and classified, and the classification accuracy of soft voting is as high as 96.6%, and the coverage rate of ROC curve reaches 99.1%, which indicates that it is reasonable and effective to solve the problem with the integrated learning model. Finally, by changing the number of decision trees, the classification accuracy rate is found to be unchanged, which verifies that the sensitivity of this model is good. In addition, this paper establishes a grey correlation analysis model to generate grey correlation coefficient matrix heat map for the chemical composition of different categories of glass artifacts, the horizontal coordinate of the heat map is the parent sequence, the vertical coordinate is the subsequence, according to the heat map of the correlation between the chemical composition of different types of glass artifacts to summarize and analyze, and in order to validate the reasonableness of the model, but also its Spearman correlation analysis, the results of the analysis of the two models are similar, so it can be judged that the application of the model is reasonable and effective. The results of the two models are similar, so it can be judged that the results of the grey correlation analysis model applied are reasonable. Finally, a detailed comparison of the chemical composition correlations of the major categories and subcategories is carried out, which is a differential analysis of the correlations of different categories of chemical compositions.

Analysis and Forecasting of Influencing Factors of Real Estate Price in Haikou City

With the start of construction of the Hainan Free Trade Port in 2018, Hainan has gradually become a new focus area. Haikou City, as the capital city of Hainan Province, has also begun to be paid more and more attention. Therefore, based on the relevant economic data of Haikou City from 2011 to 2021 and the grey correlation analysis and GM (1,1) prediction model, this paper investigates the variables that affect the cost of real estate in Haikou City and produces projections. According to the analysis, the real estate price in Haikou City is mainly affected by the number of deposits of financial institutions at the final year among the selected variables. Besides the real estate price in this area will have a relatively obvious growth in the next ten years. Considering the outcomes above, this study provides the government with reasonable control over the real estate industry by controlling the year-end deposit balance of financial institutions, an important indicator, and at the same time gives investors suggestions on long-term investment and holding of real estate-related products in Haikou City. These results can give relevant suggestions to Haikou City government and real estate investors, which also shed light on offering some suggestions for the study of new areas.

Unconventional Water Use Allocation in Harbin, China

To lessen the strain on Harbin’s water resources and enhance the environment, it is crucial to analyze the key determining factors for the use of unconventional water resources in the city and to allocate unconventional water reasonably among various industries in the planning year. In this study, a back-propagation neural network (BP neural network) model is used to predict the potential for unconventional water resource utilization in the planning year (2025), a gray correlation analysis model is used to evaluate water-using industries, and finally, an unconventional water resource allocation scheme for the study is used to determine the main influencing factors and determine the weights of key indicators. The findings demonstrate a strong correlation between Harbin’s level of investment and construction, economic efficiency, cost, level of water demand, and social factors, as well as a low level of utilization of unconventional water resources throughout the city.

Improved gray correlation analysis and combined prediction model for aviation accidents

PurposeThe purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.Design/methodology/approachThis paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.FindingsThe global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.Originality/valueUnlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Retracted: Study on the Coupling Relationship between Environmental Quality and Green Financial Economy Based on the Grey Correlation Analysis Model

Retracted: Study on the Coupling Relationship between Environmental Quality and Green Financial Economy Based on the Grey Correlation Analysis Model

Analysis of Carbon Emission Reduction at the Port of Integrated Logistics: The Port of Shanghai Case Study

With the goal of achieving carbon neutrality in the shipping industry, the issue of sustainable port development is becoming more and more valued by the port authorities. The shipping industry requires more effective carbon emission reduction analysis frameworks. This paper takes China’s Shanghai Port as the research object and analyzes it from the perspective of port-integrated logistics. Combined with the port data of Shanghai Port from 2008 to 2022, the principal component analysis gray correlation analysis model was used to screen the factors affecting the port’s carbon emissions, and three calculation models for Shanghai Port’s carbon emission sources were proposed. In addition, an expanded stochastic impact model based on the regression of population, affluence, and technology (STIRPAT) was constructed for the influencing factors of Shanghai Port’s carbon dioxide emissions and combined with the method of ridge regression to further identify important influencing factors. At the same time, a gray neural network model was established to predict the carbon emissions of Shanghai Port from 2021 to 2030 and compare them with their real value. The conclusion shows that there is a close relationship between Shanghai Port carbon emissions and container throughput, throughput energy consumption, number of berths, total foreign trade import and export, and net profit attributable to the parent company. Gray neural network model data calculations show that the growth rate of Shanghai Port’s carbon emissions will gradually slow down in the next ten years until the carbon peak is completed around 2033. The study can provide a reference for the sustainable development of other ports.

Integration degree of China’s the new development pattern of dual circulation and industrial green development

The long-standing development pattern dominated by international circulation makes China face the risk of "low-end lock-in" and "being decoupling". In addition, the current global climate change and environmental crisis are becoming increasingly severe, coupled with the COVID-19 impact. Chinese enterprises must actively build a green development system for domestic industries in a mutually reinforcing manner in the context of the domestic general circulation, in order to adapt to the new dual circulation environment as soon as possible. This paper analyzes the specific coupling and coordination relationship between the two systems based on the relevant data of China’s three major industries from 2008–2014 using Index DEA, entropy value method, gray correlation analysis and coupling coordination model. The results of the study show that: the two systems of dual circulation pattern and industrial green development have a strong correlation and basically present a coupling relationship, but within the industry, there is a problem of the collapse of the tertiary industry. In terms of the type of coupling, the domestic and international circulation in general gradually present the green development leading state, except for the primary industrial segment of the international circulation. On the whole, the coupling quality of the two systems needs to be further improved. Based on this, this paper puts forward the following suggestions: (1) coordinate the internal and external development of the industry; (2) take innovation as the driving force to promote the green transformation of industries; (3) take green sharing as the goal to strengthen the policy orientation of green development; (4) take the opportunity of mutual promotion of dual circulation to stabilize the steady state of green development coupling.

Analysis of Heavy Metal Pollution in Soil along the Shuimo River by the Grey Relational Method and Factor Analysis

Soil samples were collected from the upstream, midstream and downstream areas of the Shuimo River in three layers of 0–20, 20–40 and 40–60 cm, and each group of sample points was located 5 m, 1 km and 2 km away from the river bank, respectively. The analysis was carried out. Based on the investigation and research, six indicators, including As, Pb, Zn, Cu, Ni and Cr, were designated as evaluation factors in combination with the results of the sample collection with low or no detectable values of Cd and Hg. The mean values of the samples measured in the upper, middle and downstream layers were taken, and the degree and source of pollution were evaluated and jointly analyzed using the gray correlation analysis and factor analysis methods. By using the gray correlation analysis, it was found that the evaluation results of the upstream and middle reaches of the soil along the Shuimo River were both level 3, with slight pollution, and the evaluation results of the downstream areas were level 2, with good soil quality. There are two main sources of pollution obtained through the factor analysis; source 1 is mainly heavy metals such as Zn, Cu, Cr, Pb and Ni, while source 2 is mainly heavy metals such as As, Pb and Ni. The amount of pollution sources is inferred from the heavy metal types of each source and the soil environment along the Shuimo River as industrial and human sources of pollution. From the analysis results, the combination of the gray correlation analysis model and factor analysis model is convenient and fast and can accurately quantify the source contribution of various pollution sources. Not only can it reflect the actual situation more objectively and realistically in the evaluation of soil heavy metal pollution and pollution sources, but also the calculation is simple and easily applied with low data requirements.

Analysis of chemical composition of ancient glass products based on grey correlation method

The Silk Road was the channel of cultural exchange between China and the West in ancient times, and the glass is the precious material evidence of early trade. Early glass was often made into bead-shaped ornaments and introduced into our country in West Asia and Egypt. After absorbing the technology, the ancient glass in our country was made locally. Although the appearance of the glass products was similar to the foreign ones, the chemical composition was different. Therefore, the analysis and identification of chemical composition is an important aspect to identify and study ancient glass products. In view of the different contents of weathering or non-weathering chemical components of different types of glass relics, this paper considers the types, patterns, colors, chemical components and other factors, and ignores environment-related factors, such as atmospheric environment, pH value, burial time, depth and other factors. Based on the existing data and relevant research results of domestic and foreign scholars, the classification rules of chemical components of high-potassium glass and lead-barium glass were obtained by using grey correlation analysis model, Chi-square test and improved weighted average algorithm. It is pointed out that the contents of , , and should be observed in order to analyze and identify ancient glass products.

Identification of Ancient Glass Components Based on Grey Correlation Analysis

The article is based on mathematical modelling of the significant data of the two categorize glass specimen to examine the factors influencing the climate of the covering of the glass artefacts, to derive statistical patterns and correlations of the chemical composition content, on this basis, the subclasses are classified, and the accurate categorization manner applied to the analysis and discovery of glass composition is given. On the basis of data processing, the association between weathering of glass surface and glass kind, adornment and color is analyzed by using visual histogram. And it was found that the weathering of lead-barium glass was more serious than that of high-potassium glass, that B-grain decoration was easily weathered, and that black and light blue glass were the most severely weathered. The most significant effect on weathering is due to the content of silica in the lead-barium group, and the most significant effect on weathering is due to the content of sodium oxide in the high-potassium group. The correlations between the 14 chemical components in the two categories of high potassium glass and lead-barium glass were analysed by establishing a grey correlation analysis model, and the correlations between the different chemical components in the different categories were calculated and ranked. Similarly, the correlation between lead oxide and magnesium oxide was the highest in the high potassium group, while strontium oxide, tin oxide and sulphur dioxide were all less correlated, i.e. the correlation between this component and other chemical components was characterised by the ANOVA.

Ultimate pressure-bearing capacity of Type III onboard high-pressure hydrogen storage tanks under typical accident scenarios

Sufficient pressure-bearing performance was the basis for ensuring the safety of hydrogen storage tanks in service for the entire life cycle. The aim of this study was to analyze the ultimate pressure-bearing capacity of tanks under possible working conditions, such as room temperature, fire, and after flame exposure. The results show that the actual burst pressure of a Type III tank of 48 L and 70 MPa at room temperature was 209.80 MPa, which had sufficient explosion-proof behavior. Compared with the room temperature, the critical failure pressure of the tank under fire conditions dropped sharply by ca. 63.1 %, which readily induced pressure-bearing failure. However, after cooling to room temperature, the residual burst pressure of the tank thermally damaged by fire was only roughly 14.5 % lower than the ultimate pressure-bearing pressure at room temperature, and still maintained a certain strength. The grey correlation analysis model of influencing factors on the ultimate pressure-bearing performance of tanks in normal temperature and fire environments was established for the first time. This paper reports quantitatively that, in addition to the nominal working pressure, the design wall thickness of the tank at room temperature has the most prominent effect on the pressure-bearing performance. While the initial filling pressure and flame exposure time in fire environments were considered to be the key factors affecting the pressure bearing performance.

Study on the Optimal Reaction Parameters of C4 Olefin Prepared by Ethanol Catalyzed Reaction Based on XGBoost Regression Prediction Algorithm

The production of C4 olefin by ethanol coupling catalytic reaction has a broad development prospect in the field of renewable energy. In this paper, the influencing factors of C4 olefin preparation process were studied, the influence of temperature and catalyst type on the reaction was analyzed, and the multivariate nonlinear regression fitting model was established under the assumption of ignoring the small probability of experimental error. Then, based on the grey correlation analysis model, the correlation degree between each variable and ethanol conversion rate and C4 olefin selection rate was studied.Then, using the catalyst parameter variables considered above, the relationship equations between four catalyst parameter variables, temperature and C4 olefin yield were calculated, and the overall constructor model of C4 olefin yield under the joint action of five variables was established by using the constructor method. Finally, using MPai data science platform software based on XGBoost regression prediction algorithm, the established constructor was cross-validated by 50% fold, and the optimal solution of reaction parameters was finally obtained.Finally, five experiments were selected to improve the experimental control group data and verify the accuracy of the theoretical value of the optimal solution. By changing the catalyst parameters and temperature, a relative control group was added to the experimental group, and the conclusion of the theoretical parameter optimal solution under unrestricted and temperature restrictions was experimentally verified.

Study on Composition Analysis and Identification of Ancient Glass Products

In this paper, an entropy-TOPSIS model is established to analyze and identify the composition of ancient glass products, which solves the relationship between the type, decoration, colour of glass and the surface weathering of glass cultural relics; The multivariate linear regression model is established, which solves the problem of statistical law of weathering chemical composition content on the surface of cultural relics samples. A random forest classification model based on an ant colony algorithm is established to solve the problem of determining the importance of chemical composition, and a K-means clustering model is established to solve the problem of sub-class division within the group; A grey correlation analysis model was established to solve the problem of the correlation between the chemical composition of the same type of glass.

Portfolio investment decision model based on price forecasting model and risk assessment model

Investors often use gold and Bitcoin to reduce the risk of financial investments by combining them due to their low correlation and different dynamic correlation. Therefore, it is essential to study how a combination of the two can be invested to maximize returns. In this paper, we combine the BP neural network model and the gray correlation analysis model from the future unpredictability and risk quantification of portfolio investment. We want to use the model to construct a price prediction model and risk assessment model to build a model for portfolio investment decisions based on past-day price flow only. To be specific, this paper first builds a price prediction model based on a BP neural network prediction model. At the same time, the prediction needs to be performed in a cycle, and the training set of the neural training model is increased each time. Each prediction is recorded for the next day's data and aggregated to obtain the daily price flow prediction. Next, this paper builds a risk assessment model through gray correlation analysis. In order to measure the riskiness of the daily investment, this paper selects three indicators, namely, forecast price accuracy, recent price trend, and price volatility. It uses gray correlation analysis to score them quantitatively and then gives the daily investment risk assessment results. Finally, a sensitivity analysis was performed to determine the model's sensitivity to transaction costs by varying the transaction costs of gold and bitcoin. It was found that as the transaction costs become more extensive, the number of transactions becomes smaller and the total assets acquired become smaller.

Analytical identification model of ancient glass based on density clustering and PSO-SVM

In this paper, the correlation between the indicators of glass artifacts is explored in depth by establishing relevant mathematical models, giving the statistical laws of weathering for high potassium glass and lead-barium glass, and classifying a series of glass artifacts. In this paper, a support vector machine (PSO-SVM) model based on particle swarm optimization is developed. Firstly, the SVM model is proposed to achieve binary classification for the model, while the particle swarm model is established, and the hyperparameters c and g are optimized by adding adaptive particle variation operation and velocity contraction factor, and the optimal values of both are 0.248 and 2.244, respectively. and the training and validation sets are continuously changed by K-fold cross-validation, and the accuracy of the model classification is obtained as 100%. The model was then used to classify the Form 3 data, and the results were that the artifacts in groups 2, 3, 4, 5, and 8 were lead-barium glass; the artifacts in groups 1, 6, and 7 were high-potassium glass. Finally, the sensitivity analysis of the model was carried out to adjust the values of hyperparameters c and g. It was found that the model sensitivity was very low and the results were satisfactory. In addition, the spearman correlation coefficients between the individual chemical compositions of the two types of glasses were calculated, plotted thermodynamically and interpreted. Then, a gray correlation analysis model was developed to investigate the relationship between other chemical components and it by using silica as the parent sequence, and conclusions were drawn. And based on the results, the differences in the correlation between the chemical components of the two types of glasses were analyzed, and it was found that the correlation between some components in the two types of glasses and the silica content were significantly different.

Ecosystem services, landscape pattern, and landscape ecological risk zoning in China.

Ecosystem services represent a bridge between natural ecosystems and human well-being and are closely associated with landscape ecological risk (LER). Delimiting reasonable LER areas is important for ecosystem protection, and it is essential to link ecosystem services to LER zoning. However, only a few studies have achieved this, and the zoning accuracy of LER remains poorly understood. Therefore, in this study, we evaluated the ecosystem service value (ESV) and LER index using the equivalent value method and landscape pattern index via the remote sensing of land use data at the county level in China in 2000-2015. We applied bivariate spatial autocorrelation as well as the grey correlation analysis model to analyse the ESV-LER spatial relationships and developed a general framework to improve the accuracy of LER zoning. We found that the average ESV increased from $761.42 thousand/km2 in 2000 to $766.16 thousand/km2 in 2015, while the LER index continuously decreased in China. We also discerned a significant negative correlation between ESV and LER (p < 0.001), whereas approximately 750 counties in China exhibited high ESV and low LER indices during the study period. The final LER zoning revealed that the high-LER areas in China were mainly located in the plain areas, and the proportion of counties with high LER levels increased from 17.97% in 2000 to 26.56% in 2015. Overall, our results have important implications for ecological security, future landscape planning and design, and ecologically sustainable development in China.

A new method to evaluate the coordination of freight transport and economy for sustainable development

New-age business and information technology paradigms that maintain present and future goals to meet their fundamental requirements while protecting nature and the variety of natural processes of ecosystems lead to sustainable development. To understand sustainable development, one must first understand that there are limits to how far society can continue to develop in its current form. With the development of information technology, the informatization and digitization of freight transportation have been realized, and it has become possible to explore new paradigms of sustainable development. However, a major improvement in freight transportation technology is needed to link environmental development with sustainable business development. In this context, on the basis of the analysis of the freight coordination evaluation index system and the availability of information data, this paper built an improved gray correlation analysis model and a coordination evaluation model. It also discussed the content of the structural adjustment of freight transport based on an empirical study of the coordination between China's freight transport and economic development. The research shows that after the adjustment of the freight structure, the economic coordination of China's freight structure has realized a V-shaped process of first decreasing and then improving. The future structural adjustment of freight transport should be focused on the eastern and central regions of China, with special attention to improving the coordination of freight transport in the central region.