Grey Correlation Degree Research Articles

Sensitivity Analysis of Influencing Factors of Underground Civil Air Defense Construction Vibration under Vehicle Load

Grey correlation theory is an uncertainty analysis method, which can find the relevance of various factors studied through certain data processing in incomplete information and find the main influencing factors. In order to study the influence of vehicle load on the vibration of underground civil air defense construction, taking the civil air defense construction at the intersection of Zhongzhou Middle Road and Shachang South Road in Luoyang as the study subject, based on the field measured data, and using ANSYS to expand the working conditions, this paper studies the influence of such factors as elastic modulus of concrete, thickness of overburden layer, density of soil layer, elastic modulus of soil layer, vehicle speed, and vehicle mass on the vibration of the civil air defense construction through the grey correlation sensitivity analysis method. The field measurement and numerical simulation results show that the displacement, velocity, and acceleration values decrease gradually from the mid−span position of the roof to the wall. Displacement, velocity, and acceleration are important indexes for evaluating vibration. The grey correlation degree of the influencing factors of displacement and velocity from large to small is density of soil layer, vehicle mass, vehicle speed, elastic modulus of concrete, elastic modulus of soil layer, and thickness of overburden layer. The grey correlation degree of the influencing factors of acceleration from large to small is vehicle mass, layer of density of soil layer, vehicle speed, elastic modulus of concrete, elastic modulus of soil layer, and thickness of overburden layer. The grey correlation degree of soil layer density, vehicle mass and vehicle speed to displacement, velocity, and acceleration is 0.7951–0.9993, which indicates that their influence is significant. This study can provide reference for the design and construction of similar civil air defense constructions.

Optimal forecast combination based on PSO-CS approach for daily agricultural future prices forecasting

Forecasting agricultural commodity prices accurately is a challenging task due to the complexity of the trading market and the variability of influencing factors. Many studies have proven that forecast combination is an effective strategy for improving forecast performance relative to individual forecasting. In the field of forecast combination, how to determine the reasonable weights for combination is still an open question. This study proposed an optimal forecast combination framework for agricultural commodity prices forecasting, which integrates the decomposition–reconstruction–ensemble methodology with an improved nature-inspired global optimization algorithm. The update mechanism of particle swarm optimization (PSO) is introduced to improve cuckoo search (CS), in order to reduce the searching blindness in the huge exploration space. The framework consists of four steps. First, data decomposition using empirical wavelet transform (EWT), singular spectral analysis (SSA), and variational mode decomposition (VMD); Second, component reconstruction via a modified reconstruction approach based on the largest comprehensive grey correlation degree clustering (CGCD); Third, individual forecasting using autoregressive integrated moving average regression (ARIMA), exponential smoothing (ETS), back propagation neural network (BPNN) and extreme learning machine (ELM); Fourth, forecast combination via PSO-CS weight assignment method. Using corn and wheat future prices as research samples, the experimental results demonstrated that: (a) the PSO-CS weight assignment approach is superior to other combination approaches in most cases; (b) the CGCD approach can effectively reduce the computational cost of forecasting and improve the prediction performance; (c) the Full-PSO-CS model provides the most accurate forecast due to the diversity of individual forecasts, it reduces MAPE by 43.66% and improves directional accuracy by 30.80% on average compared with the best single model.

Integrated ecosystem management and regulation strategies in the South China Sea

In order to improve the coupling degree between ecosystem and environmental security in the South China Sea Gulf and enhance the ecological environment security, the integrated management and regulation strategies of the Gulf ecosystem were proposed. Firstly, the coupling relationship between the safety culture of the South China Sea Gulf and the ecological environment safety is determined. Secondly, the grey correlation analysis method is used to calculate the correlation coefficient between the safety culture of the Gulf in the South China Sea and the ecological environment safety, and the calculation formula of the arithmetic mean value is used to deal with the correlation coefficient, so as to complete the construction of the grey correlation degree model of the ecological environment safety. On the basis of value calculation and SLOPE trend change research method, the variation of rural ecosystem service value was obtained. Finally, through the analysis of the coupling time sequence law of ecological environment security, the regulation strategy of the South China Sea Gulf ecosystem was pointed out. The proposed method has the most obvious decreasing trend in the complexity of the South China Sea Gulf, and the proposed method has a better effect, and the coupling degree of ecological environment safety is closely related.

Fault Location Based on Comprehensive Grey Correlation Degree Analysis for Flexible DC Distribution Network

Flexible DC distribution networks have a strong capacity for new energy consumption and have received extensive attention from scholars in recent years, but fault location for DC distribution lines is a difficult task. To solve this problem, we propose using a comprehensive grey correlation degree analysis to analyze the similarity of aerial mode traveling differential current waveforms at the first and last ends of a DC line, thus achieving fault location by obtaining the optimal time shift. Additionally, we built a six-terminal flexible DC distribution network model in Matlab/Simulink for simulation and validation, showing that the method can complete the rapid and accurate location of all types of faults on a DC line, and that it possesses an anti-transition resistance capability, making it suitable and highly reliable for cases of low sampling frequency.

Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

With the aging trend in megacities, the travel behaviors of the elderly have attracted much attention. Accurate prediction of the travel behaviors of the elderly is a key link to meet the traffic demand and public facilities’ optimization. The aim of this paper was to explore the link between the travel characteristics and variables of the daily activities of the elderly. Based on a stratified sampling survey, the internal relationship between the characteristics of the elderly and their travel behavior was studied and discussed in this work. A novel grey correlation degree–genetic algorithm–back propagation (GR-GA-BP) hybrid model was proposed to predict the travel behavior of the elderly. Then, a grey correlation degree module was established and used to analyze the correlation between the individual elderly characteristics and their travel behavior. The results showed the following: (1) Both the times of weekly trips (y1) and average round-trip travel time (y2) were highly sensitive to the external environment, especially buses, subway stations, and recreational facilities. The size of the family was less sensitive to the travel behavior. (2) Referring to prediction of the times of weekly trips, the MRE of the proposed model was 23.12%, which was 15.22% less than the baseline models. (3) In terms of the prediction of round-trip travel time, the MRE of the proposed model was 7.13%, which was 14.00–69.41% lower than the baseline models. (4) The times of trips per week were 3.5. In summary, this paper provides technical support for formulating traffic demand policies and facilitates the configuration of cities for an aging society.

Application of Hyperspectral Technology Combined with Genetic Algorithm to Optimize Convolution Long- and Short-Memory Hybrid Neural Network Model in Soil Moisture and Organic Matter

A method of soil moisture and organic matter content detection based on hyperspectral technology is proposed. A total of 800 different soil samples and hyperspectral data were collected in the laboratory and from the field. A hyperspectral database was established. After wavelet denoising and principal component analysis (PCA) preprocessing, the convolutional neural network (CNN) module was first used to extract the wavelength features of the data. Then, the long- and short-memory neural network (LSTM) module was used to extract the feature bands and nearby hidden state vectors. At the same time, the genetic algorithm (GA) was used to optimize the hyperparametric weight and bias value of the LSTM training network. At the initial stage, the data were normalized, and all features were analyzed by grey correlation degree to extract important features and to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance was compared with CNN, support vector regression (SVR), and CNN-LSTM hybrid neural network model without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators. The highest accuracy rates of 94.5% and 92.9% were obtained for soil moisture and organic matter, respectively. This method can be applied to portable hyperspectrometers and unmanned aerial vehicles to realize large-scale monitoring of moisture and organic matter distribution and to provide a basis for rational irrigation and fertilization in the future.

Molecular Dynamics Study on the Compatibility of Asphalt and Rubber Powder with Different Component Contents.

In order to study the influence of asphalt from different origins on the compatibility of rubber powder and asphalt, the four most representative 90# asphalts from different origins were selected for the four-component test, namely, Karamay, Shell, Esso, and HuanxiLing, which have differences in their microstructure and molecular structure. First, Materials Studio software was used to build the asphalt molecular model based on the four-component test data, and styrene butadiene rubber and natural rubber were used as representative molecules of rubber powder to build the molecular model. The solubility parameters, molecular potential energy, and radial distribution function were obtained by molecular dynamics simulation of rubber powder and four asphalt molecules. The gray correlation degree analysis of the four components of asphalt is carried out with the gray correlation method. The results show that the solubility parameter difference between Esso asphalt and the rubber molecule is the smallest, and the potential energy between them is the largest; that is, the compatibility between Esso asphalt and rubber powder is the best, followed by Shell, Huanxiling, and Karamay. The analysis of the radial distribution function between two molecules also further confirmed the compatibility of the four modified asphalt. The gray correlation degree between the four components of asphalt and the solubility and molecular potential energy index is as follows: saturates > aromatics > resin > asphaltene. It is concluded that the influence of light components on the compatibility of asphalt is greater than that of heavy components. Therefore, in future industrial production, the rubber powder-modified asphalt made of more light components has better performance and storage stability.

An improved software defect prediction model based on grey incidence analysis and Naive Bayes algorithm

This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.

Linguistic q-rung orthopair fuzzy multiple-attribute group decision making based on the grey similarity degree and PROMETHEE II method

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q. In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed.

Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis.

The stability of the power grid and the operational security of the power system depend on the precise prediction of wind speed. In consideration of the nonlinear and non-stationary characteristics of wind speed in different seasons, this paper employs the weight of wind resource index calculated by triangular fuzzy analytic hierarchy process (TF-AHP), criteria importance through inter-criteria correlation (CRITIC), and entropy weight method (EWM) to improve gray correlation analysis (GRA) and obtain the gray correlation degree of each season. In addition, a wind speed prediction model is provided that includes single-layer and two-layer weighting and is based on both deep and shallow machine learning models. At first, we establish each quarter's wind resource characteristics at typical monthly intervals of 10min, 30min, 60min, and 120min. The GRA's TF-AHP-CRITIC-EWM, enhanced with subjective and objective weights, is used to assess the available wind resources in each season and to compute the forecasted combination of wind speed for each season. As the final prediction results, the prediction values of each layer model are evaluated independently. For the intervals with considerable errors, we apply wavelet denoising and replacement combination. The simulation findings show that the proposed combined model surpasses earlier benchmark models in terms of goodness of fit, prediction accuracy, and generalizability.

Correlation Analysis of the Change Law of Index Gas and Active Functional Groups in the Process of High-Temperature Spontaneous Combustion of Minerals in the Fushun West Mine.

The spontaneous combustion of underground minerals causes huge property losses and ecological damage. Coal and oil shale are co-associated minerals in the Fushun West Mine, and both have the ability to undergo oxidative spontaneous combustion. To study the effect of microstructure changes on the macroscopic gas product concentration during the mineral oxidation spontaneous combustion process in the Fushun West Mine, this study used a high-temperature temperature-programmed test to obtain the change trend of gas product concentration in different oxidation stages of minerals. Using Fourier transform infrared spectroscopy technology, the changes in active functional groups of surface molecules during the process of mineral oxidation and spontaneous combustion were identified. Finally, using the gray correlation degree, correlation analysis between the concentration of gas products and the concentration of active functional groups in different oxidation stages was carried out. The key reactive functional groups affecting mineral spontaneous combustion were identified. The essential reason for the change in the gas product was revealed.

Construction and Application of Physical Education Teacher Literacy Evaluation System Based on Grey Relational Model

With the development of our country, more and more attention is paid to the education of students. However, the issue of teachers’ literacy is the one that has the greatest impact on students in the campus. This paper makes an in-depth study and discussion on the quality evaluation system and application of physical education teachers. Better improving the literacy of physical education teachers also has a more positive impact on student education. The following conclusions are drawn from the research and analysis: (1) The basis of selecting evaluation indicators and the corresponding evaluation process for the evaluation of physical education teachers'literacy are multifaceted, and the evaluation criteria should be fully considered. (2) For the construction of the grey relational model, its algorithms are divided into many categories, and different algorithms are applied to different situations of relational degree. The grey relational model based on point relationship and generalized range is introduced, which provides convenience for subsequent computational research. (3) The difference between the basic information distribution and teaching quality of physical education teachers was obtained through the investigation experiment. The form of questionnaire survey determined the specific indicators of evaluation and the distinction between the first and second grades. Through multiple sets of experiments, the main evaluation indicators and the grey correlation degree of teacher literacy are tested, and the correlation degree between evaluation indicators and teacher literacy is obtained.

Gray correlation entropy analysis of zero shear viscosity and high-temperature rheological parameters of phosphogypsum-modified asphalt

The high-temperature rheological properties of solid waste-modified asphalt are useful indicators for ensuring the effective operation of asphalt pavements, and inhibiting their rutting. However, there are numerous high-temperature rheological indicators, and only a few key indicators of high-temperature properties of solid waste modified asphalt are reported. This leads to a lack of uniformity in the evaluation of high-temperature performance indicators. In this study, the variables that affect the high-temperature parameters of phosphogypsum-modified asphalt were analyzed. Various contents of phosphogypsum (0 %, 4 %, 8 %, 12 %, 16 %, and 20 %) were mixed with 70# asphalt binder to form phosphogypsum-modified asphalt. Penetration, ring and ball tests were applied to describe the conventional high-temperature properties of the phosphogypsum-modified asphalt. Moreover, the rheological properties were determined using temperature sweep, frequency sweep, and multiple stress creep and recovery experiments to determine the complex modulus (G*), phase angle (δ), SHRP rutting (G*/sinδ) and Shenoy rutting (G*/(1−(1/sinδtanδ))) parameters, zero shear viscosity (ZSV), the percent recovery (R), and the nonrecoverable creep compliance (Jnr) of the phosphogypsum-modified asphalt. Further, a gray correlation entropy analysis of ZSV and high-temperature rheological parameters was performed. The penetration of asphalt gradually decreased with adding phosphogypsum while softening point increased. At a constant temperature, the G* of asphalt binders increased with the increase in phosphogypsum content. Additionally, the δ decreased slightly, and the G*/sinδ and G*/(1−(1/sinδtanδ)) increased significantly. The G* increased with the increase in frequency from 0.1 to 100 rad/s. Phosphogypsum improved the R and reduced the Jnr of the asphalt binder. The ZSV fitted by the Carreau and Cross models exhibited a similar trend, indicating that phosphogypsum could improve the ZSV of the base binder. The gray entropy correlation degree between the ZSV and R values at 0.1 and 3.2 kPa, G*/(1−(1/sinδtanδ)), G*/sinδ, and G* were larger than 0.99. In contrast, Jnr exhibited a low correlation was about 0.96. Thus, R and ZSV can be adapted to assess the high-temperature properties of phosphogypsum-modified asphalt.

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research.

Multiple Fingerprints and Spectrum-Effect Relationship of Polysaccharides from Saposhnikoviae Radix.

Highlights The multiple fingerprints of 10 batches of Saposhnikoviae Radix polysaccharide(SP) were prepared and chemometrics analysis was carried out.The anti-allergic activity of the SPs was evaluated.The spectrum-effect relationship of SPs was establishedd. PMP-HPLC, FT-IR, and HPSEC fingerprints of 10 batches of polysaccharides from Saposhnikoviae Radix with different production areas and harvest times have been prepared, and the chemometrics analysis was performed. The anti-allergic activity of 10 batches of Saposhnikoviae Radix polysaccharide (SP) was evaluated, and the spectrum-effect relationship of the 10 batches of SP was analyzed by gray correlation degree with the chromatographic fingerprint as the independent variable. The results showed that the PMP-HPLC, HPSEC, and FT-IR fingerprints of 10 batches of SP had a high similarity. Two monosaccharides (rhamnose and galactose), the polysaccharide fragment Mn = 8.67 × 106~9.56 × 106 Da, and the FT-IR absorption peak of 892 cm−1 can be used as the quality control markers of SPs. All 10 batches of SP could significantly inhibit the release of β-HEX in RBL-231 cells, and the polysaccharides harvested from Inner Mongolia in the winter had the best anti-allergic activity. The spectrum-effect relationship model showed that the monosaccharide composition and molecular weight were related to the anti-allergic activity of the SPs. Multiple fingerprints combined with spectrum-effect relationship analysis can evaluate and control the quality of SPs from the aspects of overall quality and efficacy, which has more application value.

Research on anti leakage construction of building engineering exterior wall based on improved attribute recognition model

Due to the complexity of building shape, the diversification of building materials and the nonstandard construction technology, the seepage channel exists in the outer wall envelope of building engineering, which causes the leakage of the outer wall of the building project to become more and more serious. Therefore, the research on the construction of anti leakage of the exterior wall of the building project based on the improved attribute identification model is proposed. On the basis of the attribute recognition theory subsystem, this paper introduces the grey correlation degree method in the grey system to improve the attribute recognition theory subsystem, and uses the improved attribute recognition model to analyze the influencing factors of the leakage of the building envelope. Based on the porous medium model and numerical simulation method, this paper studies the internal flow of the medium in the external wall of construction engineering, and constructs the micro flow numerical model of fracture water. In this paper, the influence of wind pressure, height and local structure on crack flow is analyzed. The results show that the initial velocity of water is related to wind pressure, building height and the local structure of surrounding retaining structure. The water flowing into the fracture is deep at a small speed, and the velocity inside the fracture has nothing to do with the pressure distribution inside the fracture.

Construction and Analysis of Indicators of the Labor Education System Based on the Gray Correlation Degree Model.

Labor education is a complex concept whose value is not only the sum of labor + education. It plays an extremely important role in the growth education of students. Its fundamental purpose is to cultivate students' good technical literacy, improve their practical skills, and form innovative thinking. The monitoring data show that the path of labor education in schools is good, but there are also problems such as unbalanced development of labor practice, insufficient leading role of schools, insufficient basic role of families, and serious lack of social support. Responsibility index, learning motivation, motor health, and self-awareness are significantly and positively correlated with labor practice index. Based on the gray relationship theory, this paper selected relevant data of Chinese students, calculated the comprehensive gray relationship degree between each factor and students' labor reeducation level, and analyzed the variables; the new connotation of education, the construction of labor education evaluation index system, and the construction of labor education support system were studied.

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change.

In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

A Proposed Diagnostic Procedure for DC Electrical Tree Damage in XLPE

The growth and partial discharge (PD) characteristics of electrical tree in crosslinked polyethylene (XLPE) needle-plate electrode samples are investigated under constant dc voltage at different temperatures. The results reveal that under the experimental conditions, the electrical trees are all branch trees, and the discharge intensity fluctuates with time. The increase in temperature promotes the growth of electrical tree as well as the intensity of PD. Five texture features of PD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> histogram are extracted and used as the elements to evaluate the insulation status of XLPE needle-plate electrode under dc voltage based on the gray correlation degree. The area ratio and lateral tree ratio of electrical tree is extracted based on the gray histogram. The lateral tree ratio increases with the increase in temperature, which is closely related to the intensity of PD. Gray correlation theory is used to evaluate the status of XLPE insulation. The results reveal that the evaluated insulation statuses based on the gray correlation degree are the same as those based on experience, which indicates that the proposed diagnosis method of XLPE insulation status based on the gray correlation degree with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> histogram texture features as elements has a high accuracy.

Analysis of the following effect of the natural enemies with Frankliniella intonsa in tea garden

AbstractIn order to reasonably protect and utilize natural enemies for comprehensive control of Frankliniella intonsa, a systematic investigation was conducted on F. intonsa and natural enemies in tea gardens in Hefei, Anhui Province, and the spatial following effect of natural enemies on F. intonsa was studied. The semi‐variogram of natural enemies and thrips was obtained by the geostatistics method, and the correlation degree between them was analyzed by the grey correlation degree analysis method. The higher the correlation degree between natural enemies and F. intonsa, the closer the relationship between natural enemies and F. intonsa was. In this study, we analyzed the relationship between 11 natural enemies of F. intonsa in tea gardens from October 28, 2020 to November 20, 2020 and from August 22, 2021 to November 19, 2021. The top five natural enemies with the largest closeness index to F. intonsa in Huangshan Dayezhong tea garden are Oxyopes sertatus, Theridion octomaculatum, Plexippus setipes, Xysticus ephippiafus and Erigonidium graminicolum. The top five natural enemies with the largest closeness index to F. intonsa in Pingyang Tezao tea garden are Erigonidium graminicolum, Plexippus setipes, Xysticus ephippiafus, Oxyopes sertatus and Clubiona japonicola. Among the top five natural enemies in the two tea gardens, those in common are Oxyopes sertatus, Erigonidium graminicolum, Xysticus ephippiafus and Plexippus setipes. According to the summation of the closeness index and the sum of the serial numbers, the top five natural enemies closely related to the spatial following of F. intonsa in the tea gardens were Oxyopes sertatus, Plexippus setipes, Erigonidium graminicolum, Xysticus ephippiafus and Theridion octomaculatum. One of the factors closely related to the space of F. intonsa in the same tea garden was the ratio of F. intonsa to a certain natural enemy. The smaller the ratio was, the closer the following relationship was. The results of this study provide scientific basis for biological control and natural enemy protection of F. intonsa.