Data Fusion Technology Research Articles

Modeling of Satellite Constellation in Modelica and a PHM System Framework Driven by Model Data Hybrid

The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, often composed of hundreds or thousands of satellites, which puts forward high requirements for the design and health management of the constellation system, and the existing telemetry data monitoring system cannot meet the actual needs. CPS is a multidimensional complex system that integrates computation, communication and control (3C). Through the deep integration and cooperation of 3C, the real-time monitoring and dynamic control of large-scale engineering systems are realized, which is completely suitable for the operation and maintenance requirements of the satellite constellation system. This paper firstly establishes the entire satellite constellation system model, which is integrated from the satellite multidomain system, the constellation orbit environment system and the communication link system. Then, according to the technical concept of cyber-physical systems (CPS), an implementation framework of a prognostics and health (PHM) system driven by a model–data hybrid for satellite constellation systems is proposed. The framework is based on model simulation data and telemetry data and combines virtual and real data fusion, fault diagnosis, simulation prediction and other technologies to generate enhanced data to drive the effective operation of the PHM system. Finally, a verification case is designed to prove that the satellite constellation health management system implemented under this framework has a positive effect on the reliable operation and maintenance of the satellite constellation system.

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

Fuzzy Dynamic Obstacle Avoidance Algorithm for Basketball Robot Based on Multi-Sensor Data Fusion Technology

This paper uses multi-sensor data fusion technology to design and develop a multi-sensor based basketball robot’s target recognition and positioning system in an unknown environment. We establish the movement model of the basketball robot, and combine the hardware configuration of the basketball robot, analyse the methods of speed control, position control, and direction control, propose a fuzzy obstacle avoidance strategy in the basketball robot movement process, and simulate the obstacle avoidance strategy. The simulation results show that the proposed fuzzy obstacle avoidance strategy is effective. Carry out the experiment of basketball robot target calibration, build the basketball robot monitoring system, and realize data transmission and real-time image processing. Experiments show that the basketball robot can achieve target recognition and positioning tasks in an unknown environment (obstacle comparison rules). It proves the effectiveness and robustness of the proposed following algorithm. It illustrates the effectiveness of the system designed in this paper.

A Generic Indirect Deep Learning Approach for Multisensor Degradation Modeling

To monitor the degradation status of units and prevent unexpected failures in engineering systems, health index (HI)-based data fusion technologies have been rapidly developed by combining multiple sensor signals, which are helpful to understand the degradation processes of units and predict their remaining useful lifetime (RUL). Although promising, existing HI-based data fusion models for degradation modeling are still limited due to the restrictive assumptions made during the fusion or the degradation modeling processes, e.g., assuming the fusion model as a linear or kernel-based function from multiple sensor signals, or modeling the degradation process by a preselected basis function. Such assumptions are often invalid in industrial practice and may fail to accurately characterize the complicated relationships between multiple sensor signals and the underlying degradation process. To address the issue, this article proposes a generic indirect deep learning method that constructs an HI by combining multiple sensor signals to better characterize the degradation process. In particular, our innovative idea is to seamlessly integrate a deep neural network (DNN) and a long short term memory (LSTM) model to construct the HI by fusing multiple sensor signals and characterize the degradation process, which can be applied to the degradation modeling of various engineering systems. Domain knowledge including the concept of failure threshold and monotonicity of the degradation process is also considered to enhance the interpretability of the proposed method. For parameter estimation, we develop an indirect gradient descent (IGD) algorithm to train the proposed method. Simulation studies and a case study on the degradation of aircraft gas turbine engines are presented to validate the performance of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The article aims to develop a generic health index (HI)-based data fusion method for degradation modeling when multiple sensors are available to monitor the degradation status of a unit. Specifically, the developed method addresses two challenging questions in practice: 1) how to effectively combine multiple sensor signals to construct an HI that accurately characterizes the underlying degradation status and 2) how to flexibly model the degradation evolution based on the constructed HI. To implement this method in practice, four steps are included as follows: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , collecting multiple sensor signals and failure time of historical units. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , constructing the HI and describing the underlying degradation process by training a deep neural network (DNN) model and a long short term memory (LSTM) model, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , estimating model parameters using the proposed IGD algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fourth</i> , constructing the HI of in-service units and predicting their remaining useful lifetime (RUL) using the constructed HI. The proposed method is expected to be able to characterize various degradation processes and be applied to the degradation modeling of different engineering systems.

English Teaching Ability Evaluation Algorithm based on management, leadership and entrepreneurship including education, Data Fusion and Notch Filtering

All universities and colleges has been offering English as one of the important courses. The effectiveness of English teaching is inextricably related to the quality of talent acquisition and student growth. The term evaluation of teaching quality describes the type of content knowledge in which the focus is to analyze quality teaching. One of the significant aims of every school and university is to improve the English teaching quality and promote the development of English teaching and entrepreneurship. As the existing evaluation algorithms have been facing various problems of fuzzy core dimension characteristics, which result in poor data integrity. Therefore, we proposed an English teaching ability evaluation algorithm based on data fusion, entrepreneurship, and notch filtering. Moreover, for the purpose, to achieve the goal of education entrepreneurship and quality teaching, strengthen of the English teaching quality algorithm is necessary, as well as the leadership and management qualities of the teachers also have a lot of impact on the performance of teaching quality and students working performance and it is a topic worthy of in-depth and significant investigation. We select the explicit indicators that are easy to measure, establish the hierarchical structure system of evaluation indicators, construct the evaluation model by data fusion and notch filtering, obtain multi-source heterogeneous data, extract the core dimension characteristics of English teaching ability, make rational use of teaching resources, convert variables into standard units, and set up the evaluation mechanism by using random walk algorithm. Experimental results: the average data integrity of the designed English teaching ability evaluation algorithm is 4.33, and the average data integrity of the other two evaluation algorithms is 1.921 and 1.885, which proves that the English teaching ability evaluation algorithm integrating data fusion and notch filtering technology has a broader application prospect.

The Construction and Exploration of University English Translation Teaching Mode Based on the Integration of Multimedia Network Technology

The teaching mode of English translation in universities has been changed greatly, so the design of teaching mode is needed to be optimized. Besides, the network teaching mode should be studied. Specifically, in this article, the advantages of English translation teaching in network environment are analyzed. Finally, we focus on the effective countermeasures to solve the problems of translation teaching mode and provide the reference values for teaching staff. This paper summarizes the integration of information technology and interpretation teaching according to the evolutionary route of technology application from three aspects: (1) general field, (2) interpretation professional field, and (3) education field. Furthermore, we propose a correlation analysis method of English translation teaching based on data fusion technology. Considering the time-related characteristics of English translation teaching, a perturbation time period division method based on moving time window is proposed based on the actual data and the development results of cutting-edge technologies. Finally, interpretation teaching is foreseen in four aspects: intelligence, simulation, precision, and collaboration.

Spatial Characteristics of Sports Tourism Destination System Based on Data Fusion and Data Mining

With the development of information technology around the world, the research on the integration of data mining and data fusion is becoming more and more in-depth. In recent years, sports tourism has developed better and better, with the growing demand for tourism, and the tourism industry has developed by leaps and bounds. Tourism is a new economic growth point in many regions. But it also faces many challenges from both internal and external sources. The tourism destination system is an extremely important subsystem in the tourism system. The quality of its spatial structure not only affects the quality of tourism activities, but also affects the effective operation of the tourism function of the destination system. This paper studies the spatial characteristics of sports tourism destination system based on data fusion and data mining technology. The external environment of sports tourism development, the distribution of sports tourism resources, and the spatial structure of sports tourism are deeply studied by using Gini coefficient, nearest neighbor index, and other technical methods. A questionnaire was also designed to study the physical activity preferences of different age groups. The results show that the nearest neighbor index R of fitness sports tourism destinations is less than 1, which is in a clustered distribution state. The main group of sports tourism activities is mainly concentrated in the age group of 16–45.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Structural damage detection based on data fusion method

In order to improve the accuracy of structural damage detection, data fusion technology is applied to structural damage detection. Three damage identification algorithms based on vibration are used to analyze the damage state. Then, the improved D-S combination method was used to combine the obtained diagnosis results to obtain the final diagnosis results. The results show that this method can effectively improve the accuracy of structural damage detection and has a good application prospect in engineering.

Multi-sensor time synchronization algorithm based on Gaussian process regression

In order to address the problem of inconsistent sampling frequency and accuracy of each sensor in multi-sensor data fusion technology, the algorithm of Gaussian Process Regression (GPR) is proposed for time alignment, and in the scenario where the target motion model is uniform linear motion and uniform turning motion, the simulation is compared and analyzed respectively. The simulation results show that the proposed algorithm can effectively improve the accuracy of time synchronization.

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.

Fusion of THz-TDS and NIRS Based Detection of Moisture Content for Cattle Feed

As an essential index to evaluate feed quality, feed moisture content which is too high or too low will impose an adverse impact on feed nutritional value. Therefore, the quantitative analysis of feed moisture content is significant. In this paper, the detection of feed moisture content based on terahertz (THz) and near-infrared (NIR) spectroscopy and data fusion technology of THz and NIR (THz-NIR) was investigated. First, feed samples with different water content (29.46%–49.46%) were prepared, and THz (50–3000 μm) and NIR (900–1700 nm) spectral data of samples was collected and preprocessed, and the feed samples were divided into correction set and verification set by 2:1. Second, the spectral data was fused through the head-to-tail splicing, and the feed moisture content prediction model was established combined with partial least squares regression (PLSR). Third, competitive adaptive reweighting sampling (CARS) was applied to extract spectral characteristic variables for feature layer fusion, and the feed moisture content prediction model in feature level was constructed combined with PLSR. Finally, the evaluation parameters validation set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were employed to evaluate the prediction effect of the model. The results indicated that THz, NIR spectra, and data fusion technology could quickly and effectively predict feed moisture content. Among them, the characteristic layer spectral data fusion model achieved the optimal prediction effect while Rp, RMSEP, and RPD reached 0.9933, 0.0069, and 8.7386 respectively. In conclusion, compared with the prediction model established by single THz and NIR spectrum, THz-NIR spectrum data fusion could more accurately predict feed moisture content and provide certain theoretical and technical support for inspirations and methods for quantitative analysis of feed moisture content of livestock and poultry.

Anonymization of Quasi-Sensitive Attribute Sets in Aggregated Dataset

The widespread use of Internet of Things (IoT) and Data Fusion technologies make privacy protection an urgent problem to be solved. The aggregated datasets generated in these two scenarios face extra privacy disclosure. We define attribute sets with different sources in an aggregated dataset as quasi-sensitive attribute sets (QS sets). The QS set itself is not sensitive, but internal linking attacks may occur when two QS sets in an aggregated dataset are linked. In this paper, we propose a new privacy model, namely, the QS k-anonymity model. The QS k-anonymity model is effective in preventing internal linking attacks. We provide two algorithms for the QS k-anonymity model, the Greedy QS k-anonymity algorithm and the Efficient QS k-anonymity algorithm. We evaluate our algorithms on real datasets. The experimental results show that the Greedy QS k-anonymity algorithm has good data utility, the Efficient QS k-anonymity algorithm shows better efficiency, and both algorithms are well scalable.

Analysis of the Effect of Branched Chain Amino Acids on Muscle Health Information of Swimmers Based on Multisensor Fusion and Deep Learning.

Swimmers must fully mobilize the muscles of the whole body during exercise, and it is necessary to study the protection of swimmers from muscle damage. Now, muscle damage is increasing year by year, and more athletes are affected. Therefore, studying the causes of muscle injuries and exploring more effective treatments have become important research topics in the field of sports medicine. This study is mainly based on deep learning to analyze the protective effect of branched-chain amino acids on swimming athletes' muscle injury. Due to the complex and changeable environment and the interference of unknown factors, a single sensor cannot meet the needs of obtaining information. Therefore, people have developed the technology of multisensor information fusion to obtain enough information. Multisensor data fusion technology can synthesize the information of each sensor and then obtain more comprehensive and accurate decision-making information. This study is mainly based on multisensor fusion and deep learning to analyze the impact of branched chain amino acids on Swimmers' muscle health information. Finally, two experiments were designed in this article. The first experimental result showed that the pain level of the experimental group who took BCAA supplements was 19% lower than that of the control group that did not take the BCAA supplement within three days after exercise. The results of the second experiment show the following: after exercise, the creatine kinase activity value of the experimental group taking BCAA supplement was 4.38 ± 1.45, and the creatine kinase activity value of the control group taking placebo was 5.42 ± 2.12. It proves that BBCA can protect muscle damage by reducing the activity of creatine kinase.

Big Data Fusion Method Based on Internet of Things Collection.

With the development of information collection technology, the data that people need to deal with are also increasing, which brings problems such as isomerization of data types, poor data quality, and fast data generation speed. At present, as an important method of data fusion technology, data fusion method based on deep learning has become an effective way of data fusion under the background of big data, which has important research significance. There is a problem with heterogeneous data types between time series data and text data, and it is difficult to fuse them effectively by traditional data fusion methods. In order to make full use of the information contained in text data and improve the accuracy of time series prediction, this paper proposes a data fusion model based on FC-SAE. In this model, GloVe and CNN are used to extract the features of text data, FC neural network is used to extract the potential features of time series data, and then, the SEA model is used to fuse the data, which fully discovers the relationship between data and greatly improves the prediction accuracy.

3D Stratum Interpolation Algorithm of Metro Tunnel Based on BIM and Data Fusion

The three-dimensional stratum space interpolation algorithm plays a vital role in promoting the safety, management, and decision-making of subway tunnels and is a current research hotspot. The purpose of full text is to study the 3D stratum space interpolation algorithm of subway tunnels and to study the 3D stratum space interpolation algorithm based on building information modeling (BIM) and data fusion technology. This article first uses computer three-dimensional visualization technology to simulate the surface of the ground-fall tunnel based on known data. It is to do the interpolation processing on the two-dimensional space of the subway tunnel to create an accurate surface model of the subway tunnel. It then uses the block model method to divide the internal data of the tunnel according to actual needs. In order to create a model of the internal data of the tunnel, three-dimensional spatial interpolation is performed. After the three-dimensional interpolation is completed, it is convenient to quickly estimate the taste and reserves according to the interpolation results. The experiments in the text show that the average error value of the algorithm in this paper is 38.98 and the performance is improved by 23.4%. The algorithm in the text has obvious advantages in interpolation effect, and the interpolation efficiency is faster. A bird’s eye view of the current study is shown in Figure 1.

Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion

In recent years, under the background of stable economic operation, people's research on economic systems has become increasingly popular. The dynamic input-output model reflects the change and development process of the input-output relationship of the economic system over a period of time. The main purpose of tracking control is to design a suitable controller so that the output of the control system can track the output of the reference system as much as possible. In the economic system, data is an important factor. Based on this, this paper mainly studies the tracking control of dynamic input-output economic system based on data fusion. This research takes the data fusion of the dynamic input-output economic system as the starting point and takes the optimal control and tracking of the economic system as the research object of this research. Based on data fusion technology, a new dynamic input-output economic system tracking and control is proposed. This paper studies the finite-time optimal tracking control of linear systems. Through numerical examples, comparing the finite-time and infinite-time optimal control simulation results, it is proved that the algorithm can achieve good tracking control. Experimental data shows that the optimal and suboptimal performance indicators for a limited time are 0.7729412 and 1.5687310, respectively. Therefore, compared with the infinite-time optimal control, the performance loss and the final tracking error of the suboptimal control proposed in this study are reduced.

Application of Data Fusion and Image Video Teaching Mode in Physical Education Course Teaching and Evaluation of Teaching Effect

The technology and functions of data fusion and image teaching are constantly improving with the development of society and are gradually produced in life in order to adapt to the progress of science and technology. Data fusion technology refers to the information processing technology that uses computer to automatically analyze and synthesize several observation information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. In this paper, the traditional teaching mode on students' learning and other aspects of help is not very obvious, and video teaching method can not only increase students' interest in learning, but also help their learning is very huge. Teaching video is to make the knowledge, skills, and other content that teachers want to impart to students into video form to assist modern multimedia teaching. In this paper, two classes with the same number and similar average test scores were selected as the experimental objects. The test results are evaluated by f-s-o algorithm, f-s-o matrix, variance proportion method, and teaching evaluation, so as to get more accurate results. In the experiment, two classes with 30 students were selected, and different teaching methods were adopted for the two classes. One class adopted the traditional teaching mode, and the other class used the video teaching method. During the 4-month experiment, there was no significant difference in test scores and comprehension between the two classes in the first month. The ratio of the two classes was 5 : 3 in the second month, 10 : 5 in the third month, and 20 : 7 in the fourth month. Through the comparative evaluation of the test results of four months, the experimental results show that the video teaching method can not only mobilize the enthusiasm of students but also plays a very important role in promoting students' performance.

Hybrid IoT and Data Fusion Model for e-Commerce Big Data Analysis

This article is aimed at studying the e-commerce big data recommendation model based on data fusion and the Internet of Things. This article chooses an embedded system for the construction of an e-commerce platform and uses data fusion technology to collect, transmit, and filter useful information from various information sources. Then, the collected information and data are analyzed and integrated, and visualization algorithms are used to better present data analysis, and association rules and structural similarity methods for electronic comparison are uses. This article uses the B/S architecture to design the overall framework of the data access layer, business logic layer, and user presentation layer; collects, organizes, stores, and presents the acquired consumer information; and finally analyzes the e-commerce background, platform performance, and supply and demand analysis. The experimental results show that the average clustering coefficient of the platform (0.7559) is smaller than the average clustering coefficient value of 8 items in the store (0.811) and smaller than the average network diameter and average path length of the online store (3.86, 7.7). Store products are better than store products, and the diameter and length of the product should be larger (2.71, 5.75). The recall rate of the e-commerce big data platform and model matching review method designed in this paper is 5% higher than that of the word matching model method and has a better expected effect in terms of user supply and demand.

Multisensor Data Fusion System for Wushu Sanda Teaching in Higher Education Institutions

Classical data fusion methods can be roughly divided into two categories: one is based on random methods, such as weighted average, least squares, Kalman filtering, Bayesian estimation, DS evidence inference, and production rules; the other is based on artificial intelligence. Intelligent methods include fuzzy logic reasoning, artificial neural networks, and support vector machine methods. This paper introduces, analyzes, and summarizes the advantages and disadvantages of the above methods, as well as the level of use of these methods, and then introduces the current trend of data fusion technology toward the mixed development of multiple methods. This article is aimed at carrying out a research on the innovation of Wushu Sanda teaching mode in colleges and universities based on multisensor data fusion technology. This article puts forward the innovative theory of youth sports training in Sanda in the new era, combined with the current status of physical education in Chinese colleges and universities, and analyzes the practicality of the theory and the effect of system integration. This article accelerates reforms and collects data from four aspects: the policy system at the macro level, the integrated governance system at the meso level, the teacher service system at the micro level, and the digital industry system at the functional innovation level, and proposes innovative strategies for youth sports training and teaching in the new era. The results of the study show that people with 5.99 are more interested in the World Cup, accounting for the largest proportion, while the number of people who are interested in martial arts is the least, only 1.63. The innovative theory of youth sports training in Sanda in the new era improves the training effect of students by 60% compared with ordinary college Sanda training. It can to a large extent protect students from reluctant exercises that do not receive physical education in traditional teaching. Physiologically, it has unique advantages for the development of students. The teaching innovation model of this article can effectively solve the problems of physical education organization difficulty.