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With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, the development speed of all walks of life has entered a new level, and the power industry has also developed rapidly. Driven by market demand, China's power transmission range and power transmission capacity will enter a new level. At the same time, the problems brought about by the development of the power system are equally severe. Due to the large load density in individual areas, the detection of short-circuit current must be improved as an important issue. The purpose of this paper is to study how to improve the practical model of short-circuit current calculation of photovoltaic power plants, so that it can be well applied to the current high-density current detection in China. Therefore, this paper improves the recursive least squares (RLS) algorithm and applies it to the practical model of short-circuit current calculation of photovoltaic power plants and describes the improvement process of the algorithm in detail. At the same time, this paper designs relevant experiments and analysis to count the data of the improved RLS algorithm in the short-circuit current calculation of the actual photovoltaic power station and combines the data of this part to test and analyze the ability of the algorithm. The experimental results in this paper show that the improved RLS algorithm has a very good improvement in the calculation accuracy of the short-circuit current calculation of photovoltaic power plants in the actual model calculation. At the same time, the calculation efficiency is also improved, and the current tracking effect is also improved by 7%. [ABSTRACT FROM AUTHOR]

The current social construction process is gradually accelerating, and people's growing spiritual and cultural needs are eager to be satisfied. Spiritual culture is conceived from the material production process of human beings. It is a unique ideology of human beings that is different from other animals. National music culture is a branch of spiritual culture. It occupies an indispensable position in China, and its development is closely related to the inheritance of national culture. At present, the cultural value of ethnic music has not been fully excavated. The reason is that the research methods used are relatively backward, resulting in insufficient optimization of the analysis method. Therefore, this paper applied the distribution estimation algorithm and the information index method to the analysis of the cultural value and strategy of ethnic music. After simulating 120 samples, this paper obtained four results: convergence rate results, average optimal value results, sorting effect results, and final optimization degree results. The experimental results showed that the research method after applying the distribution estimation algorithm can improve the optimization degree of the value and strategy analysis of ethnic music culture by 5.81%, which is conducive to the better value of ethnic music culture in social spiritual and cultural construction. [ABSTRACT FROM AUTHOR]

The successful holding of the Olympic Games has made the attention to sports continue to rise. China's badminton has always been at the forefront of the world, and since badminton became an Olympic sport, it has been tasked with winning gold medals. Because the entry of badminton is simple, badminton has a solid mass foundation in China. Badminton has a very high fitness, confrontation, and fun, with speed as the core and stability, accuracy, ruthlessness, and liveliness as the main tactical style. In professional competitions, athlete's prediction of badminton's trajectory is of great significance to the performance of the game, so the prediction of badminton's trajectory is very important. This paper aims to study the performance evaluation of badminton trajectory error analysis based on wireless sensor network. It is expected to predict the trajectory of badminton with the help of wireless sensor network technology, reduce the prediction error, and improve the game performance. Sensors in WSN communicate wirelessly, so network settings are flexible, device locations can be changed at any time, and wired or wireless connections to the Internet are possible. This paper analyzes and compares the commonly used dynamic target capture algorithms, compares the advantages and disadvantages of this wireless sensor network, combines the flight characteristics of badminton, and analyzes the error of its trajectory. Aiming at the vision system in badminton monitoring, the application research on the side detection, tracking, and trajectory prediction algorithm of flying badminton in the two-dimensional image plane video stream is completed. The experimental results of this paper show that the error of using the wireless sensor network is 2 cm and the trajectory error of not using the wireless sensor network is 8 cm. It can be seen that the trajectory extraction error using the wireless sensor network strategy is small. [ABSTRACT FROM AUTHOR]

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule. [ABSTRACT FROM AUTHOR]

Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one's language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. "Besieged City" is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain's thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel "Besieged City" is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains. [ABSTRACT FROM AUTHOR]

Competence of Teaching or Scientific Projects, which include teacher competencies, knowledge, self-image, motivation, and personality traits, distinguishes high-performing teachers from average teachers in school education. The Competence of Teaching or Scientific Projects model is the sum of the competency characteristics required for the specific role of a teacher, and it is a set of characteristics developed in response to the professional performance and task requirements of the teacher. This paper adopts a systematic literature review method to analyze the research results on teachers' research competency in China since the 21st century. Horizontally, the number of literature types first increases and then stabilizes, the publishing journals are concentrated, and the research topics are diverse; vertically, the definition of teacher research competency includes hybrid, behavioral, and trait theory; the cultivation goals of teacher research competency range from breakpoint separation to articulation and integration, the cultivation process ranges from theory acquisition to participation in creation, and the cultivation methods range from primary and secondary schools based to demand-based. The construction of a teacher research competency model helps in teacher recruitment, research competency development, and assessment; the construction methods include qualitative, quantitative, and mixed research; specific strategies involve sample selection, competency indicator determination, and subjective influence control. This study selected high-performing primary and secondary school outstanding teachers as a valid sample, used the event interview method, adopted the classical BEI competency modeling method, analyzed key behavioral events that occurred in teachers' past teaching work, examined performance behaviors, explored identifying and conceptualizing Competence of Teaching or Scientific Projects characteristics, determined outstanding Competence of Teaching or Scientific Projects criteria through retrospective interview evaluations of graduates, explored key elements of teachers' long-term impact on students, compared outstanding teachers with ordinary teachers, and constructed a model of primary and secondary school Competence of Teaching or Scientific Projects to provide an effective basis for teacher selection, training, and assessment. Future research needs to increase research perspectives, broaden research themes, and innovate research methods. [ABSTRACT FROM AUTHOR]

With the progress of society and the development of science, the competition of competitive sports has become extremely fierce, and the talents of competitive sports have gradually shown their strong vitality. Elite athletes are an important group for the development of China's competitive sports industry. To continue to progress and develop, it is inseparable from the careful training of elite athletes. In order to closely integrate the needs of China's socialist modernization and the training of elite athletes, sports schools that focus on cultivating elite athletes need to strengthen their sense of urgency and mission. It is necessary to meet new challenges at the height of social development and constantly optimize the construction of the curriculum system and the reform of the teaching operation mechanism. The discussion and research of the current elite athlete's talent training model has become a real problem faced by sports colleges, education departments, and sports departments. This paper conducts an experiment on the modeling of the sports athlete's social network based on machine learning, and the experimental data are shown as follows: among the athlete social network pictures optimized by machine learning, there are 1,200 male athletes, accounting for 48%, and 1,000 female athletes, accounting for 40%. Before the optimization, male athletes accounted for 70.58% and female athletes accounted for 16.3%, which was a decrease of 22.58% for male athletes and an increase of 23.7% for female athletes. From the above data, it can be seen that after the research on the social network of sports athletes through machine learning, it has a positive effect on the development of the social network of athletes. [ABSTRACT FROM AUTHOR]

In order to fully implement the Party's tough fight against poverty, conscientiously implement the policy of targeted poverty alleviation, and accelerate the development of the socialist market economy and the national economy, it is particularly important to strengthen digital information management in the audit of special fiscal poverty alleviation funds. With the continuous advancement of science and technology, the informatization of audit work has become an inevitable trend. However, in the past audit work of special financial funds, it was found that there were a series of problems such as imperfect audit system, weakened audit work, poor audit projects, imperfect supporting facilities, inadequate poverty alleviation funds, slow project progress, and failure to timely check the completion of projects. In order to better realize the timeliness and practicability of audit information of special fiscal poverty alleviation funds and solve the loopholes and deficiencies found in the past, based on the original digital information submanagement system, this paper adopts the J2EE system framework, MVC mode, and B/S security detection system to build a more stable, efficient, and secure financial special poverty alleviation fund management system. After a series of tests, under the premise of effective use of poverty alleviation funds, the system data shows that, from 2012 to 2018, the per capita disposable income of residents in poverty-stricken areas in China increased by 12.1% in nominal terms, an increase of 8.9% compared with 2012, and the gap between the national rural averages has narrowed again. [ABSTRACT FROM AUTHOR]

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy. [ABSTRACT FROM AUTHOR]