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Abstract The planning and development department of the China Yangtze river three gorges engineering development corporation aims to improve its intelligent performance management system in accordance with the company’s vision. This paper presents a study on enhancing intelligent performance management in the Planning and Development Department of the China Yangtze River Three Gorges Engineering Development Corporation. By combining data mining techniques with association rule algorithms, a performance prediction model was established. The goal is to explore a performance management system aligned with the company’s vision and the specific characteristics of construction enterprises. Experimental results show the effectiveness of the GABPNN model, achieving a prediction error of 5% or less in seven out of seventeen sample points, with a maximum relative error of 20.86%. The suggested performance management system presents a useful strategy for construction companies to improve their performance evaluation procedures, boosting overall management efficiency and aligning with their distinctive traits.

The planning and development department of the China Yangtze river three gorges engineering development corporation aims to improve its intelligent performance management system in accordance with the company’s vision. This paper presents a study on enhancing intelligent performance management in the Planning and Development Department of the China Yangtze River Three Gorges Engineering Development Corporation. By combining data mining techniques with association rule algorithms, a performance prediction model was established. The goal is to explore a performance management system aligned with the company’s vision and the specific characteristics of construction enterprises. Experimental results show the effectiveness of the GABPNN model, achieving a prediction error of 5% or less in seven out of seventeen sample points, with a maximum relative error of 20.86%. The suggested performance management system presents a useful strategy for construction companies to improve their performance evaluation procedures, boosting overall management efficiency and aligning with their distinctive traits. [ABSTRACT FROM AUTHOR]

The Youjiang Basin in China is the world's second-largest concentrated area of Carlin-type Au deposits after Nevada, USA, boasting cumulative Au reserves nearing 1000 t. This study examined the recently unearthed Lintan Carlin-type Au deposit within the Youjiang Basin. Factor analysis and association rule algorithms were used to identify exploration vectors and indicators essential for navigating this promising geological territory. In the Lintan mining area, the geological strata encompass the Triassic Bianyang, Niluo, and Xuman formations comprised clastic rocks, followed by the deeper Permian Wujiaping Formation with massive carbonate rocks. The orebodies are restricted to the F14 inverse fault, cutting through the Xuman Formation, with an additional F7 fault between the Wujiaping and Xuman formations. A total of 125 rock samples from the F14 fault and a representative cross-section were analyzed for 15 elements (Au, Ag, As, Bi, Cd, Co, Cu, Hg, Mo, Ni, Pb, Sb, Tl, W, and Zn). The elements were divided into four groups based on cluster and factor analysis. Group 1 (Co, Cu, Zn, Ni, Tl, W, and Bi) was mainly enriched in the Xuman, Niluo, and Bianyang formations controlled by sedimentary diagenesis. Group 2 (Au, As, Hg, and Sb) was concentrated in the F14 and F7 faults, representing Au mineralization. Group 3 (Pb, Ag, and Mo) was mostly enriched near the F14 and F7 faults, displaying a peripheral halo of Au mineralization, and was probability controlled by ore-forming hydrothermal activities. Group 4 (Cd and Mo) exhibited extreme enrichment along the periphery of the F7 fault. This pattern indicates the presence of a substantial hydrothermal alteration zone surrounding the fault, likely influenced by ore-forming hydrothermal processes. Additionally, Pb, Ag, Cd, Mo, and W are considered essential indicators for ore formation besides Au, As, Sb, Hg, and Tl. Twelve effective association rules were derived using the association rule algorithm, which can aid in discriminating Au mineralization. The spatial distributions of the 15 elements indicated that the F14 fault is the main ore-bearing fracture zone, while the F7 fault serves as the ore-conducting structure, channeling ore-forming fluids into the F14 fault. Faults between the Wujiaping and Xuman formations, along with their associated reverse faults, present potential prospecting targets both within and outside the Lintan Au deposit in the Youjiang Basin. Exploration geochemical data can be fully utilized by combining factor analysis and association rule algorithms, offering key guidance for prospecting Carlin-type gold and similar deposits. [ABSTRACT FROM AUTHOR]

The first part of this paper examines the classification of traditional music culture and the method of integrating it with modern vocal teaching. Then, the association rule algorithm was used to establish the calculation model of integrating college vocal teaching and traditional music culture, and the weighted MINWAL algorithm improved the model. Finally, the degree of integration and the effect of integration between college vocal music teaching and traditional music culture were analyzed using indicators of integration degree, value, and acceptance. The fusion levels of singing style, voice, tune melody, lyrics, performance technique and dynamics were 4, 3.5, 4.3, 4.4, 4.1 and 3.9, respectively. The fusion level regarding teaching content was 4.1, and the fusion level in terms of practical activities was 4.0. A teaching strategy that is feasible and effective and has promotion value is the combination of college vocal music teaching and traditional music culture.

This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.

In this paper, in order to solve the teaching problems of college Civics courses under the perspective of the big Civics course, data mining technology is used to obtain school history resources and then construct research on the teaching of college Civics courses empowered by school history resources based on data mining. The initial data of school history resources obtained by data mining, regression analysis and decision tree analysis of the initial data are used to offset the influence of the attribute values of school history resources on the research results, and association rule analysis is used to help students understand the characteristics, connotation and connection between school history resources and the teaching of ideological and political education. Based on the relationship between school history resources and ideological and political education, a data mining-based research program on school history resources empowering ideological and political teaching was designed, confirmed and analyzed. The results show that the average scores of the four items of management attitude, management ability, management method and management effect in the school history resources empowering the teaching of ideology and politics are 0.6001, 0.5761, 0.5481, 0.5751, which indicates that the development of the school history resources based on mining association rule algorithms of data mining technology can better realize the function of teaching in the teaching of ideology and politics courses. This study enhances the use of previous school history resources in ideological and political education, which is conducive to the development of high-quality socialist successors and reliable builders.

The most important thing in sports training is the development of ability, and the emergence of outdoor sports has made people realize that sports can better improve the efficiency of sports when they are integrated into the outdoors. The association between outdoor sports and physical training is analyzed in this paper using the association rule algorithm and chi-square test. Based on the number of correlation items in the dataset of outdoor sports and physical training, it can be determined whether there is a correlation or not. Combining the chi-square values and the degrees of freedom calculated from the column table, the significance levels of outdoor sports and physical training were obtained under the condition that the null hypothesis was valid. To determine the association value of outdoor sports and physical training, the distance of the serial curves between them was calculated. The focus was on exploring the common motivations of outdoor sports and physical training, as well as the relationship between outdoor spatial elements and exercise time and frequency. The results showed that Pearson’s chi-square value of outdoor exercise and physical training was 77.367, p=0.000, and the p-value was less than 0.05. Accessibility had a direct effect on fitness frequency, with the role effect values of 0.237* and 0.213*, respectively. The role effect value of physical environment on fitness time was 0.192*. It shows that combining outdoor space with physical training can increase the fitness frequency of exercisers.

In this paper, the association rule algorithm is used to mine all the strong association rules in the public art education music appreciation course, and the course Civics, and the Inter-transaction algorithm is utilized to decompose the mining task into two parts of long and short itemsets according to the time-consuming time, so as to reduce the number of transactional intersection operations. A new pruning strategy has been adopted to effectively limit the number of candidate sets and improve the performance of the algorithm. Finally, based on the model of this paper, we analyzed the relevance of the music appreciation courses in public art education in colleges and universities to Civics and the change in students’ Civics literacy after integrated teaching. The results show that the mean value of students’ moral quality after teaching increased by 3.24 from 18.07±2.08, and the p-value of each index except music literacy is 0.000, which is significantly different. And sup=0.05, conf=0.622, lift=4.499 for Music Art A and Introduction to Basic Marxist Principles A. There is a strong correlation between teaching music and Civic and Political Science courses.

Big data analysis of education management in colleges and universities plays an important role in the improvement of education levels. In this paper, a triangular fuzzy algorithm based on association rule mining is proposed to realize a better data mining effect. Firstly, the relative similarity in WBARM algorithm is used to find out the similarity difference index of attributes from decision-making data, and then the association rule mining algorithm is used to find out the influence factor of attributes, and finally, the weight of attributes is obtained by combining the above results, and the generated association factor is evaluated, and the research is carried out in terms of enrollment and admission, learning behavior and teaching evaluation. The results show that: in terms of college enrollment and admission, the three colleges and universities in M can increase the promotion of candidates with scores between 451-580 on the college entrance examination, and expand the number of enrollment in the majors of visual communication design, software engineering, and mechanics. In terms of teachers’ teaching quality, the overall teaching level of teachers in the 3 colleges and universities is at a medium level (3.06), among which G1 has the best teaching level, with an average score of 4.13. By communicating more with teachers from G1, teachers from the other two schools can enhance their teaching level. Developing the educational management of colleges and universities towards precision can be accomplished through the results of big data analysis.

In order to promote the model innovation of higher education management and student training mechanisms, this paper proposes data mining technology to assist the research of education management and training mechanisms. Using web crawler technology to obtain the required initial data for the study, data cleaning and conversion are carried out to ensure the accuracy of the results of the research and analysis in response to the problems of missing data, intermingled noise, and attribute redundancy in the initial data. Aiming at the slow efficiency and collapse of the traditional association rule algorithm in the face of massive data, the whale optimization algorithm is used to optimize the association rule algorithm for multi-objective optimization, and the optimized association rule algorithm is used to analyze the data of higher education management. The data showed that the Confidence=77.82% for the association rule poor grades in introductory courses=>poor grades in major courses, which indicates that the probability of students whose average grades in introductory courses are outside the 75th percentile and those whose average grades in significant classes are outside the 60.00th percentile is 77.82%. This study aids teachers and administrators in making scientific decisions to enhance teaching management, enhance teaching quality, and reduce costs associated with school operations.

Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

With the continuous development of society, the requirements of enterprises and the public for the reliability of the power system are also improving, and the fault identification and protection action of the distribution network is conducive to the comprehensive analysis and overhaul of the line, which is of great significance. In this paper, a short-circuit fault identification method for distribution networks based on artificial intelligence technology is proposed to categorize and predict power system faults using an association rule algorithm. Then, a distribution network adaptive protection strategy based on the equivalent impedance of the distributed energy system is proposed, which real-time adjusts the setting value and criterion of the protection through the distribution network containing distributed energy devices connected to the grid and adjusts the working status in normal operation, realizing that the current protection of the distribution network adapts itself to the change of the working conditions of the distribution network system of IIDG. Simulation results show that when two-phase grounding and three-phase short-circuit faults occur in the distribution network under the protection of the adaptive system. The protection circuits start instantaneously in 0.005s, which realizes the functions of the fast startup, self-sustained operation, and controllable shutdown when faults occur in the distribution network. Meanwhile, the feasibility of the scheme is verified by utilizing the system to output short-circuit faults in the case of IIDG grid-connected or off-grid when the respective integrating values are greater than the positive sequence short-circuit current values.

The rapid development of the tourism industry has led to a continuous change in the way of tourism consumption. This paper takes the information of tourists’ consumption behavior as the research object and investigates their consumption habits. This paper adopts the fuzzy clustering (FCM) algorithm to analyze tourists’ consumption habits and clustering validity indexes and then uses the association rule algorithm on the basis of the FCM algorithm to mine the factors affecting tourists’ consumption habits in tourism management. In this paper, tourists are divided into five categories: free youth, couples, parents and children, families, and explorers. In tourism consumption, the top three primary concerns of tourists are “attraction characteristics, safety and consumption, which account for 30.34%, 18.04% and 12.07%, respectively. In the process of tourists’ tourism consumption, 93.47% of the concern factors are attraction features, with a confidence level of 98.76% and rule enhancement of 1.245, respectively. In addition, the probability of security and consumption appearing in the text at the same time is high, with rule support, confidence level, and enhancement of 89.52%, 75.59%, and 1.045, respectively. Attention should be given to the characteristics of attraction, safety, consumption, and service simultaneously. The results of this paper help to identify tourists’ consumption preferences and provide suggestions for tourism management centers to accurately understand tourists’ consumption habits.

In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users. [ABSTRACT FROM AUTHOR]

With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.

In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users.

With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being. Association rule mining is an important part of data mining technology. Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them. Traditional association rule algorithms need to set minimum support and minimum confidence in advance. However, these two values are largely influenced by human subjectivity. Many scholars use average and weight to set these two values, but the effect is still not very good. In order to solve this problem, this paper proposed an improved algorithm of association rules - PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP - growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.

Customers benefit from the ability to select their desired options to configure final products. Manufacturing companies, however, struggle with the dilemma of product diversity and manufacturing complexity. It is important, therefore, for them to capture correlations among the options provided to the customers. In this paper, a data mining approach is applied to manage product diversity and complexity. Rules are extracted from historical sales data and used to form sub-assemblies as well as product configurations. Methods for discovering frequently ordered product sub-assemblies and product configurations from 'if-then' rules are discussed separately. The development of the sub-assemblies and configurations allows for effective management of enterprise resources, contributes to the innovative design of new products, and streamlines manufacturing and supply chain processes. The ideas introduced in this paper are illustrated with examples and an industrial case study. [ABSTRACT FROM AUTHOR]

To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning. [ABSTRACT FROM AUTHOR]

With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.

• The proposed multi-factor-based forecasting model outperforms ARIMA, ARMAX and SVM. • Energy, economy and industry are main influencing factors on carbon emissions. • With no constraint, carbon emissions of China in 2020 will be 6078.21 million tonnes. • Carbon intensity in the middle region will be the most serious in 2023. Accurate carbon emissions forecasting plays a pivotal role in reducing global warming by providing references to formulate emission reduction policies. Although numerous studies have focused on forecasting China's carbon emissions, the results of different methods are contradicting, because they are based on different data and use different parameters. This paper aims to propose a hybrid carbon emissions forecasting model based on multi-factor identification to offer reliable forecasting results. First, association rule algorithm was applied to find influencing factors and analyse their joint effects on carbon emissions from the perspective of time and space. Energy consumption, economic growth, industrial structure, foreign direct investment, and urbanization are proven to be the five major factors that can cause an increase in carbon emissions. Second, a multivariate grey model optimized by firefly algorithm was utilized to conduct carbon emissions forecasting under different scenarios. Empirical results indicated that the proposed hybrid model had the best performance compared to other methods. If no effective measures are taken, it is difficult for China to realize its goal for carbon emissions reduction in 2020. [ABSTRACT FROM AUTHOR]