Big Data Statistical Analysis Research Articles

Application of Big Data Statistical Analysis Methods in the Field of Economic Management

Application of Big Data Statistical Analysis Methods in the Field of Economic Management

The Relationship Between Emotional Intelligence, Mental Health, and the English Achievement of College Students Based on Big Data Statistical Analysis

This article explores the association between emotional intelligence, mental health, and English proficiency among college students. Survey results reveal that the gender ratio of college students is roughly equal, and the number of participants is stable each year. Humanities majors comprise 31% of the total. The authors collected data from nearly 1000 college students, which was analyzed using a matrix model. Results suggest that while there is a weak correlation between emotional intelligence scores and English grades, there is a strong correlation between mental health scores and English grades. The researchers conducted a 3-month psychological intervention experiment on 60 sophomore students. These students showed improvement in both mental health scores and English scores. Additionally, survey results indicate student satisfaction with the testing and psychological intervention. Overall, this study demonstrates that mental health and English proficiency are closely related, and psychological interventions can positively affect language skills.

Practical Exploration of Smart Classroom in Piano Teaching

Abstract In this paper, texture mapping technology is utilized to enhance the virtual reality piano teaching situation, and the piano classroom teaching data is integrated through big data statistical analysis methods. A deep neural network based on BERT+BiLSTM+CRF is used to train a named entity recognition model and complete the construction of a knowledge graph for piano subject data. Then, deep learning methods are used to extract the piano subject data and construct a visualized knowledge graph for the piano subject. Analyze the use of the Piano Smart Classroom teaching platform and the development of a blended teaching mode. The design requirements of the piano intelligent teaching platform are planned from two aspects: platform user roles and platform use cases, and the overall design of the piano intelligent teaching platform is carried out. The use of piano teaching video samples is a tool for calculating the teaching language ratio, analyzing the teaching interactions between teachers and students, and comparing the number of interactions between traditional teaching and smart classroom teaching. Analyze the effectiveness of piano-smart classroom teaching in three dimensions: cognition, emotion, and skill. The mean value of the specific content score of piano intelligent classroom teaching is 3.82, which is the single item with the highest mean value, and the piano teaching classroom supported by smart technology can show the piano teaching content more comprehensively.

Research on the cultivation mode of Russian language talents in the context of Hainan Free Trade Port based on big data statistical analysis

Abstract In this paper, a talent training model based on big data analysis is designed for the background of the construction of the Hainan Free Trade Port. A learning behavior analysis method is designed using the K-Means clustering algorithm and particle swarm optimization algorithm, which can accurately mine valuable information from a large amount of user learning behavior data and provide a reference for the exploration of the Russian talent training model. The accuracy rate in the experimental validation can reach 91.99%, which is outstanding and can provide important support for establishing a systematic talent training model in the context of the Hainan Free Trade Port.

A multi-scenario urban resilience simulation study based on the system dynamics model

Scientific quantification of urban resilience can provide a basis for decision-making on the construction of resilient cities to ensure safe and high-quality urban development. This study used text mining and big data statistical analysis to construct an urban resilience evaluation index system from four dimensions: social, economic, infrastructure, and ecological, and constructed an urban resilience evaluation model based on the system dynamics approach to elucidate the interactions among the indicators. Taking 11 cities in the Yangtze River Economic Belt as study cases, four future development scenarios were set under Shared Socio-economic Pathways (SSPs), and the variations in the urban resilience index of each city to 2030 were simulated. The research results show that: (1) The resilience of the 11 cities currently shows an upward trend, but the stratification phenomenon is obvious, and the overall situation is high in the east and low in the west, high in the north and low in the south; (2) The variations of resilience index vary among cities under different scenarios, and in a comprehensive comparison, SSP1 is the optimal development path; (3) The resilience gap among the 11 cities will remain in the future, but the gap in the east-west direction may gradually decrease, while the gap in the north-south direction remains significant.

A comprehensive literature mining and analysis of nitrous oxide emissions from different innovative mainstream anammox-based biological nitrogen removal processes

The biological nitrogen removal (BNR) process in wastewater treatment plants generates a substantial volume of nitrous oxide (N2O), which possesses a potent greenhouse gas effect. A limited number of studies have systematically investigated the N2O emissions of anammox-based systems with different BNR processes under mainstream conditions. Based on extensive big data statistical analysis, it had been revealed that simultaneous nitritation, anammox and denitrification (SNAD), partial nitritation anammox (PNA) and partial denitrification anammox (PDA), exhibit significantly lower N2O emission factors when compared to traditional BNR processes. The median values for N2O emission factors were determined to be 1.01 %, 1.15 % and 1.43 % for SNAD, PNA and PDA, respectively. Based on nitrogen removal data and N2O emission factors, the N2O emissions from PNA, SNAD and PDA processes were calculated to be 0.016 g·d−1, 0.037 g·d−1 and 0.008 g·d−1, respectively. Furthermore, the machine learning models (SVM and ANN) exhibited excellent predictive performance for N2O emissions in the BNR processes. However, after removing environmental factors, the R2 value of the SVM model sharply decreased. The SHAP feature analysis demonstrated the significant impact of environmental factors on the accuracy of predictive performance in machine learning models. Spearman correlation analysis was employed to investigate the relationship between N2O emissions and operational factors as well as microbial communities. The results demonstrated a negative correlation between HRT, temperature and C/N with N2O emissions. Moreover, strong associations were observed between Nitrosomonas, Nitrospira, Denitratisoma, Thauera species and N2O emissions. The contribution of N2O production via AOB pathways played a key role that was quantitatively calculated to be 93 %, 80 % and 48 % in the PNA, SNAD and PDA processes, respectively. These findings highlight the potential of these innovative BNR processes in mitigating N2O emissions.

Contribution of the 5G Smart First-Aid Care Platform to Achieving High-Quality Prehospital Care.

China is gradually becoming an aging society, and the necessity for prehospital first-aid care is increasing. However, there is a long-term information blind spot in traditional prehospital first-aid care. Fifth-generation (5G) network has the advantages of enhanced broadband, multiple connections, and low latency. Combined with the current prehospital first-aid system, the 5G smart medical prehospital first-aid care model creates a new opportunity for the development of prehospital first-aid care. This paper aimed to describe the 5G smart first-aid care platform and offers practical insights into the construction and application of the 5G smart first-aid care platform in small- and medium-sized cities. We first introduced the working principle of the 5G smart first-aid care platform and then chose patients with prehospital chest pain as an example to describe the whole workflow in detail. The application of the 5G smart emergency-care platform is at the stage of pilot exploration in large- and medium-sized cities. Big data statistical analysis of the completed first-aid care tasks has not been performed yet. The 5G smart first-aid care platform realizes real-time interconnection of information between the ambulance and the hospital, performs remote consultation, shortens the treatment time, and enhances treatment efficiency. Future research should focus on quality control analysis of the 5G smart first-aid care platform.

Data Driven Analysis of Insurance Claims Using Machine Learning Algorithm

The insurance industry is undergoing major changes with the integration of big data and artificial intelligence technologies. The design and research of an insurance survey claims system based on big data analysis aims to improve the efficiency and accuracy of insurance claims processing. The system uses image recognition, computer vision systems, language recognition, and other AI technologies to analyze case information and accelerate the speed of insurance claims settlement. The system also includes an intelligent customer service feature that uses AI algorithms such as language processing and big data statistical analysis to provide processing suggestions to policyholders. The system implements an individualized insurance service that collects, stores, and analyzes data on policyholders to create personalized insurance products and perform precise marketing. Big data analysis in the insurance personalized service primarily uses association rule analysis, classification and clustering analysis, and change and deviation analysis to improve the service. The one-click reporting function simplifies the reporting process for policyholders, allowing them to report a case from anywhere at any time. The intelligent claims processing feature separates liability in claims cases and deals with non-controversial cases through the use of AI, shortening the processing time and reducing manpower costs. The insurance survey and claim system has undergone five iterations under an agile development model and has achieved the goals of personalized insurance services, one-click reporting, intelligent claims processing, and intelligent customer service. The practical application results demonstrate that the system can improve the efficiency and accuracy of insurance claims processing while also providing policyholders with a more convenient and personalized experience. In conclusion, the design and research of an insurance survey claims system based on big data analysis has the potential to revolutionize the insurance industry and greatly benefit both insurance companies and policyholders

Application of Remote Sensing Methodology for Vehicle Emission Inspection

Remote sensing detection of vehicle emissions is an effective supplement to the statutory periodic inspection of in-use vehicle emissions and it is a convenient technical method for real-time screening of high-emission vehicles. The principle of remote sensing detection is to inversely calculate the absolute concentrations of gaseous pollutants in vehicle exhaust according to the relative volume concentration ratio of each exhaust component to carbon dioxide (CO2) in the vehicle exhaust plume. Because the combustion mechanisms of gasoline engines and diesel engines are different, different inversion calculation methods of remote sensing data must be applied. The absolute concentrations of gasoline vehicle gaseous emissions measured by remote sensing can be calculated by the inversion calculation method based on the theoretical air–fuel ratio combustion mechanism. However, the absolute concentrations of diesel vehicle nitrogen oxide (NOx) measured by remote sensing must be calculated by the inversion calculation method based on the correction of the excess air coefficient. For the integrated remote sensing test system of gasoline and diesel vehicles, it is necessary to determine the vehicle category according to the vehicle type and license plate and adopt different inversion calculation methods to obtain the correct remote sensing results of vehicle emissions. The big data statistical analysis method for vehicle emission remote sensing results can quickly screen high-emission vehicles and dynamically determine the remote sensing emission screening threshold of high-emission vehicles as the composition of in-use vehicles changes and the overall emission of vehicles declines, so as to achieve dynamic and accurate screening of high-emission vehicles.

Characteristics of Chinese Translation of Emily Dickinson's Poetry: A Big-Data Analysis Based on Nine Translations

Using a program written in python language to conduct a big-data statistical analysis of poetic form and vocabulary use of nine Chinese translations of Emily Dickinson's poetry, it is found that the current Chinese translations deviate significantly from the original in terms of poetic form in which deviation of punctuation is greater than that of stanzas and lines, and no translation completely retains formal characteristics of the original. Different characteristics in the use of vocabulary can be found in different translations. Some translations have too many additions and deletions, which makes language content deviate from the original too much. In translating Dickinson's poetry, the form and content of the original text should be faithfully translated. In particular, dashes of the original text should be kept completely, reduce the manifestation of the translator in the translation and avoid addition and deletion in translating.

Applied Logic in the Age of Big Data: The Evolution of Linguistic Programming and Decoding in Psychological Trend Analysis

Mental health quality is closely related to individual mental health level. High mental health quality can help high school students deal with mental illness in time, actively seek help from others, and improve their stigmatized abnormal beliefs, so as to improve their overall mental health level. The party and the government pay more and more attention to the shaping of everyone’s proactive social psychology, which has been widely concerned in modern society. Therefore, this article scientifically studies the applied logic of the information age: the evolution of language programming and decryption in the trend of psychological change. SQL Server data management system, Oracle big data statistical analysis and research ideas, ACSI mental health evaluation index system, and language expression encoding and decoding method were used to measure the mental health of Chinese teenagers and children, and the relevant measurement conclusions were compared. The results show that the accuracy of language programming is higher than that of traditional mental health measurement. Finally, the paper discusses the scientific research of mental health, puts forward corresponding proposals, and finally prospects the future of the exploration of the ways of mental health cultural education.

Construction and Promotion of Reading Service Platform of University Library Based on Computer Network Cloud Platform

This paper analyzes the basic framework, cloud service mode, and cloud service approach of digital library cloud service platform under cloud computing environment and studies the future development trend of cloud service platform and cloud service mode of digital library under cloud computing environment. This paper introduces the advantages of cloud service platform construction in digital reading promotion of university library in detail, expounds the construction and application of cloud service platform for digital reading promotion of university library, and puts forward the construction and promotion strategy of reading service platform of university library and the framework model of personalized information service of digital library. By describing the technical characteristics of cloud platform, the advantages of digital reading based on cloud platform are revealed, such as massive data storage, personalized service virtualization, and big data statistical analysis. On this basis, the innovation of digital reading promotion mode in colleges and universities is discussed. This study is conducive to promoting the standardized management of reading and information services and the rational allocation of resources in university libraries in order to provide some reference for the practice of building and popularizing the reading service platform of university library.

The application of an integrated monitoring system for power distribution and consumption

In view of the fact that the traditional integrated power distribution and consumption monitoring system and power information acquisition system are independent of each other, and the data between the two systems are not shared and reused, which brings great obstacles to the construction of new power systems, this paper proposes an integrated power distribution and consumption monitoring system. The system can simultaneously collect distribution monitoring data and power consumption information data, and accurately perceive the panoramic data of power systems such as high voltage power line, distribution transformer area, low voltage power line, metering tank and so on. Through big data statistical analysis and accurate prediction of fault events, realize the two-way interaction between distribution side and power consumption side, reduce power maintenance costs, improve power supply quality, it provides a strong guarantee for the safe operation of power distribution and electrical equipment.

A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion

This paper adopts the method of multisource big data fusion to conduct an in-depth study and analysis of precision poverty alleviation and uses big data statistical analysis model to explore and analyze it. Combining the characteristics of big data itself and the development of precision poverty alleviation, it focuses on the exploration of big data and introduces the background, development status, and achieved results of poverty alleviation with typical cases, followed by the analysis of the problems in the process of big data precision poverty alleviation and the study of the improvement path of big data technology precision poverty alleviation. Through the comparative analysis of the simulation accuracy of three models, the results show that the random forest model has the lowest error rate, after which the importance degree of indicators is derived using the model. In addition, the empirical analysis of the preprocessed sample data for multidimensional identification of poor households yields the contribution rate of each dimensional indicator that leads to multidimensional poverty of farm households, establishing scientific judging criteria to accurately judge whether farm households are poor on the one hand and selecting accurate identification methods to achieve accurate identification of poor households on the other hand. The tenfold crossover method is used to verify the errors in the test sample set. When the number of classification trees is greater than 100, it will gradually increase. Therefore, it is most appropriate to select the number of trees as 100. The multidimensional accurate identification model of farm household poverty constructed in this paper has an accuracy rate of 90.26% for the identification of poor households. By analyzing the accuracy rate of model identification and the contribution rate of multidimensional indicators leading to the poverty of farm households at the same time, the poverty degree of farm households under each dimensional indicator is derived, to accurately identify the poor households and their poverty status. The results show that the multidimensional accurate identification model of farm household poverty has the accurate identification ability and application value in the identification problem of poor households, and through the implementation of the model algorithm, a good application environment of accurate identification of poverty is created, which provides technical support to help poverty alleviation work and improve the accuracy of identification of poor households.

Exploring the Common Mechanism of Fungal sRNA Transboundary Regulation of Plants Based on Ensemble Learning Methods.

Studies have found that pathogenic fungi and plants have sRNA transboundary regulation mechanisms. However, no researchers have used computer methods to carry out comprehensive studies on whether there is a more remarkable similarity in the transboundary regulation of plants by pathogenic fungi. In this direction, high-throughput non-coding sRNA data of three types of fungi and fungi-infected plants for 72 h were obtained. These include the Magnaporthe, Magnaporthe oryzae infecting Oryza sativa, Botrytis cinerea, Botrytis cinerea infecting Solanum lycopersicum, Phytophthora infestans and Phytophthora infestans infecting Solanum tuberosum. Research on these data to explore the commonness of fungal sRNA transboundary regulation of plants. First, using the big data statistical analysis method, the sRNA whose expression level increased significantly after infection was found as the key sRNA for pathogenicity, including 355 species of Magnaporthe oryzae, 399 species of Botrytis cinerea, and 426 species of Phytophthora infestans. Secondly, the target prediction was performed on the key sRNAs of the above three fungi, and 96, 197, and 112 core nodes were screened out, respectively. After functional enrichment analysis, multiple GO and KEGG_Pathway were obtained. It is found that there are multiple identical GO and KEGG_Pathway that can participate in plant gene expression regulation, metabolism, and other life processes, thereby affecting plant growth, development, reproduction, and response to the external environment. Finally, the characteristics of key pathogenic sRNAs and some non-pathogenic sRNAs are mined and extracted. Five Ensemble learning algorithms of Gradient Boosting Decision Tree, Random Forest, Adaboost, XGBoost, and Light Gradient Boosting Machine are used to construct a binary classification prediction model on the data set. The five indicators of accuracy, recall, precision, F1 score, and AUC were used to compare and analyze the models with the best parameters obtained by training, and it was found that each model performed well. Among them, XGBoost performed very well in the five models, and the AUC of the validation set was 0.86, 0.93, and 0.90. Therefore, this model has a reference value for predicting other fungi’s key sRNAs that transboundary regulation of plants.

Optimization Model of Shortest Distribution Path Selection in Intelligent Logistics

In order to quantitatively analyze the relationship between intelligent distribution path selection and logistics economic development and promote the development of logistics economy, an optimization model of intelligent logistics shortest distribution path selection is proposed. This paper analyzes the statistical characteristics of intelligent distribution path selection to the development of logistics economy, adopts the shortest path optimization method to select the intelligent distribution path, constructs the big data statistical analysis model of intelligent distribution path selection and logistics economic development by using quantitative regression analysis method, analyzes the development factors of logistics economy by intelligent distribution path selection combined with multiple linear fusion method, and combines the fuzzy constraint control method. Realize the optimization of logistics intelligent distribution path. According to optimization The distribution effect is modeled by statistical analysis of logistics economic development, and the quantitative relationship between intelligent distribution path selection technology and logistics economic development is analyzed. The results of empirical analysis show that the intelligent distribution path selection has a significant level to the development of logistics economy. This method has good optimization for logistics distribution path selection, improves logistics throughput and promotes the development of logistics economy.

Study on Real-Time Monitoring Method of Marine Ecosystem Micro-Plastic Pollution

Chen, S., 2020. Study on real-time monitoring method of marine ecosystem micro-plastic pollution. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1032-1036. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the real-time monitoring ability of marine ecosystem micro-plastic pollution, a real-time monitoring method of marine ecosystem micro-plastic pollution is proposed, which is based on the fusion of marine ecosystem micro-plastic pollution data and fuzzy correlation detection of pollutants. Firstly, a data acquisition system for real-time monitoring of micro-plastic pollution in marine ecosystem is designed. On the basis of data collection, big data's statistical analysis model of micro-plastic pollution in marine ecosystem is constructed. The data fusion of marine ecosystem micro-plastic pollution is carried out by the method of block detection and gray-scale quantitative statistical analysis, and the cluster analysis and detection of marine ecosystem micro-plastic pollution monitoring data are carried out with the method of correlation feature extraction. Achieve real-time monitoring of micro-plastic pollution in marine ecosystem. The simulation results show that the time-effectiveness and accuracy of real-time monitoring of micro-plastic pollution in marine ecosystem by this method are good.

Research on the Application of Big Data Statistical Analysis in the Field of Economic Management

Nowadays, the Internet and computer are becoming more and more popular in the society. People become the transmitter and receiver of information through the Internet. This way has changed the traditional life, work and study mode of human beings. With the continuous increase of data and information, its application value gradually increases. Researchers use big data statistics to help people manage their personal data. This paper analyzes the application of big data analysis in the field of economic management and makes the corresponding summary.

Numerical Analysis of Long Period Wave Characteristics in Sea Area Based on Mathematical Probability and Statistics

Yu, M. and Du, H., 2019. Numerical analysis of long period wave characteristics in sea area based on mathematical probability and statistics. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 119–124. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the numerical analysis ability of long period wave characteristics in sea area, a numerical analysis method of long period wave feature in sea area based on mathematical probability statistics is proposed. The big data statistical analysis model of long period wave feature analysis in sea area is constructed, and the feature modeling and probability density feature analysis of long period wave data in sea area are carried out by using fuzzy association rule feature mining method. Combined with the mathematical probability statistical method, the fuzzy regional clustering processing of the long period wave data in the sea area is realized, and the feature quantity of the association rules of the long period wave in the sea area is extracted. The fuzzy clustering of the long period wave in the sea area is carried out by using the fuzzy C means clustering fusion method, and the random probability density distribution characteristic quantity of the long period wave in the sea area is excavated in the data clustering center. Combined with the feature space distributed structure reorganization method, the long period wave data is reorganized in the sea area, and the transmission control and feature numerical analysis optimization of the long period wave data in the sea area are realized. The simulation results show that the numerical analysis of long period wave characteristics in sea area is accurate and the data clustering performance is better, which improves the evaluation ability of long period wave dynamic evolution in sea area.

Promoting Effect of Logistics Development on Economy in Coastal Area

Guo, X., 2019. Promoting effect of logistics development on economy in coastal area. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1099–1104. Coconut Creek (Florida), ISSN 0749-0208.In order to quantitatively analyze the relationship between the spatial planning of logistics distribution in the coastal area and the development of logistics in the coastal area, analyze the promoting effect of the development of logistics in the coastal area on the economy, and promote the development of logistics in the coastal area. Based on the optimal logistics path planning and statistical data regression analysis, a modeling method for the influence of spatial planning of logistics distribution in coastal areas on the development of coastal logistics is proposed. This paper constructs an object model to analyze the statistical characteristics of logistics development in coastal areas in the spatial planning of logistics distribution in coastal areas, and chooses the mode of logistics development in coastal areas by using the shortest path optimization method. An adaptive regression analysis method is used to construct the big data statistical analysis model for spatial planning of logistics distribution in coastal areas and logistics development in coastal areas. Based on the multi-linear fusion method, the spatial planning of logistics distribution in coastal areas is analyzed to analyze the factors of logistics development in coastal areas, and the fuzzy correlation test method is used to realize the model planning of logistics development in coastal areas. The statistical analysis and modeling of logistics development in coastal area are carried out according to the optimized distribution effect, and the quantitative relationship between the spatial planning technology of logistics distribution in coastal area and the logistics development in coastal area is analyzed. The results of empirical analysis show that the spatial planning of logistics distribution in coastal areas has a significant level for the development of logistics in coastal areas, and this method has a good optimality in selecting the mode of logistics development in coastal areas, thus improving the logistics throughput. It has promoted the economic development of coastal areas.