Smart contract technology based on artificial intelligence background is gradually becoming a brand-new path to improve the efficiency of economic transactions due to its unique advantages. This paper initially explores the impact of smart contract technology on economic transaction efficiency through empirical analysis of models and data. The credit mechanism is introduced as an intermediate variable to analyze its mediating effect in the process of improving economic transaction efficiency by smart contract technology. The optimization of Fabric transaction mechanism is realized by using the improved credit model, which further exerts the role of smart contract technology in enhancing economic transaction efficiency. The principal component analysis is used to calculate the comprehensive score of economic transaction efficiency before and after the optimization of smart contract trading mechanism to show the effect of the development of smart contract technology on the improvement of economic transaction efficiency. This paper concludes that the development of smart contract technology will significantly and positively promote the improvement of economic transaction efficiency through benchmark regression analysis, mediation effect test and other methods. After the optimization of smart contract transaction mechanism, the comprehensive score of economic transaction efficiency produces significant improvement compared with the pre-optimization period, in which the average value of the comprehensive score of transaction efficiency in Guangdong, Jiangsu, Shanghai, and Beijing is improved by 20.18%, 24.52%, 33.77%, and 35.54%, respectively. It further indicates that smart contract technology is an effective path to improve economic transaction efficiency.

In the context of globalization where market competition is becoming more and more intense, strategic human resource management (SHRM) plays an important role in enhancing corporate competitiveness. Through structural equation modeling and multiple linear regression methods, this paper reveals the complex paths of SHRM perceptions on employee proactive behaviors, and uses convolutional neural network pairs to explore the nonlinear relationships in the model and validate the results of SEM analysis. Overall, SHRM perception has a significant positive effect on employee proactive behavior (β=0.254, p<0.001). The indirect effect values of job self-efficacy and conceptual psychological contract as mediators were 0.1043 and 0.1726, respectively, which were both positive, i.e., they played a mediating role in the mechanistic effect of SHRM perceptions on employees' proactive behaviors. Insider identity perception, on the other hand, has a significant positive moderating effect between SHRM perception and employee-initiated behavior (β=0.09, p<0.01). The importance of independent variables using CNN model was ranked in descending order as: conceptual psychological contract, job self-efficacy, SHRM perception, job category, and insider identity perception, which was consistent with the results of SEM analysis, revealing the significance of convolutional neural network in optimizing human resource management strategies and enhancing employee motivation.

The rapid development of information technology makes intelligent decision support system play an increasingly important role in economic standardized management. The Intelligent Decision Support System (IDSS) constructed in this paper includes interaction layer, analysis layer and data layer. The system standardizes the management of enterprise economy through strategic forecasting and decision analysis, economic planning and control, and economic analysis. The study combines the fuzzy hierarchical analysis method (FAHP) and the fuzzy comprehensive evaluation method (FCE) to evaluate the standardized level of economic management of enterprise A. The evaluation score of the standardized level of enterprise A’s economic management is , which is greater than 80, and it belongs to the grade of “good”. It shows that the intelligent decision support system constructed based on this paper can effectively help standardize the management of enterprise economy.

With the development of renewable energy technology and the pursuit of sustainable development in the construction industry, the design of direct-soft photovoltaic systems integrated with buildings has become an important research direction. In this paper, a variety of photovoltaic power generation modules are selected and combined with building roof functions to design a solar photovoltaic building integration system. In addition, this paper constructs a multi-objective optimization configuration model, improves the multi-objective particle swarm algorithm, and analyzes the optimization effect of the improved particle swarm algorithm on the photovoltaic building integration system by using multiple sets of test functions and evaluation indexes, combined with a number of experiments. The improved particle swarm algorithm in this paper converges to the optimal value of 0.21 when iterating to 25 rounds. And with the increase of the number of nodes, the optimized particle swarm algorithm, the distribution of node voltages in the vicinity of the standard voltage. The PV building integrated system designed in this paper still has a generation output efficiency higher than 85% after 20 years, which shows good stability of power generation. And the power generation in its whole life cycle is about 1645710kwh, which greatly reduces the consumption of conventional energy. In conclusion, the PV building integrated system in this paper not only has significant advantages in terms of capacity efficiency, but also shows strong potential for environmental protection.

As the birthplace of national culture, traditional villages can convey cultural and social natures through spatial configuration. Based on the theory of spatial syntax, this paper combines the genetic algorithm to design the fitness function for optimization, and selects the streets and lanes of Wengji Village as the research sample, focusing on the analysis of its morphological evolution mechanism from 1975 to 2020. Through quantitative analysis, it is found that although the streets and alleys of Wengji Village show spatial scale expansion due to social and economic development, the village streets and alleys can still maintain the original spatial texture and style. The integration degree, selectability, synergy (0.4273~0.6395) and comprehensibility (0.3744~0.5761) of the streets and alleys in Wengji Village are all characterized by increasing, indicating that the spatial accessibility, spatial openness and spatial wholeness of the streets and alleys in Wengji Village have been improved. However, the degree of synergy and comprehensibility are still lower than 0.7, and there is some room for optimization of the wholeness and cognizability of the streets and lanes of Wengji Village. It is necessary to protect and continue the overall structure of the village, optimize and integrate the key spaces of the village, and rationally control the development process of the village, so as to promote the protection of the spatial form of the streets and lanes of Wengji Village and the continuation of the cultural lineage.

In the context of big data, many problems connected with usage of classical techniques in computing large and complicated data have emerged. For a true disruption of silicon and computational power, there is quantum computing which is poised to redefine itself in the future. Quantum computing is the area of study for this research; real-time big data processing with a specific emphasis on speed. In a sequence of experiments, the performance of quantum algorithms was compared to the performance of classical algorithms regarding large-scale data processes. These findings imply that the compared quantum algorithm has enhanced the processing time while providing on average 70%-time reduction as well as 30 percent improvement of the computational efficiency. Besides, the scalability of quantum computing is also better; it remains effective when used on large datasets, and it has an accuracy of 95% that is higher than classical one. This outcome depiction the power of quantum computing in the alteration of data processing tactics. However, there are some issues like quantum decoherence and error rates, which are guaranteeing the non-stability of quantum computing, but they are expecting more improvements in the field of quantum hardware and error correction. From this study, it consequently becomes clear why the advancement of quantum technology should continue and for what; to meet the challenges of big data.

With the direct support of the Party and the state, after more than 20 years of development, universities have established educational ideas and political theories, which are “facing the problems of curriculum reform”, “teaching beliefs and politics”, and “deviation between teaching and related research”. On the basis of years of teaching practice, they creatively put forward scientific concepts based on beliefs and political characteristics, and an important method to help understand the current situation, In recent years, great progress has been made in relevant research, but there are still many defects, which seriously affect the sustainable and healthy development of ideological and political theory in universities. This paper takes the computerization and algorithm model of complex information network as the starting point, and the best method of science education and politics as the research standard. The document combines the principle of combining scientific and humanistic methods with the best method of university education knowledge and politics, provides the best method for the knowledge and political model of education, and analyzes the credibility. The empirical results show that the accuracy of the ideal model is 91%. The improved computerized model provides supporting data for stimulating the intellectual and political vitality of university theoretical courses, improving the educational effect, strengthening educational ideas and policies, and ensuring the quality of university education.

Special attention has been paid to China’s socio-economic development, the gradual improvement of the living standards of the population, and the family and society have begun to pay attention to preschool education. However, the process is affected by a series of factors, such as school factors, family factors, teachers and social factors, which reduce the quality of kindergarten brand image and learning and are not conducive to effectively empowering children in various fields. To effectively accomplish the goals of kindergarten, teachers should make use of the comprehensive nurturing value of labor education to maximize and optimize the value of education. Kindergarten brand image evaluation is an important part of kindergarten teaching, which can help teachers and researchers better understand how effective kindergarten brand image is and how to develop. This paper discusses the problems existing in China’s kindergarten brand image evaluation, and puts forward an evaluation method of kindergarten brand image quality based on support vector mechanism (SVM) and component analysis, so as to improve the evaluation quality.

The promotion of industrial digital transformation is an important breakthrough in the change of economic structure and physical space layout, which can promote the entire industrial chain to the high-end value chain and win more profit space and voice for the integration of domestic and international industries into the international cycle. This study takes the cities in the Yangtze River Delta Economic Belt as an example to deeply explore the spatial effect of digital transformation on the healthy transformation of traditional industrial structure, and constructs relevant spatial coupling models to carry out empirical verification by taking the opportunity of putting forward relevant assumptions. The experimental results show that the model is significant at a significance level of more than 5%, which is suitable for the selection of spatial measurement model. The mean square error of its network simulation output is 0.1333, which verifies the expected hypothesis and proves that the digital transformation of the model has a significant spatial driving effect on industrial upgrading.

At present, electric shock accidents are still the main safety issue for distribution workers. In recent years, the promotion of video AI applications has made it possible to detect whether the human body has crossed the safety line, and how to detect the actual height of the workers and the actual spatial distance between them and live equipment and live lines has become a challenge for researchers. Therefore, this article proposes to use LiDAR, edge processing module, and warning module to build a pre control device for safe operation behavior in power distribution scenarios, scan the power distribution operation scene in real time, use deep learning methods to identify objects such as distribution stations, human bodies, high-voltage equipment, and transmission lines in the point cloud, and calculate the spatial distance between operators and high-voltage equipment and lines in the station area through the edge point coordinates of object point cloud aggregation. When the spatial distance approaches or exceeds the voltage safety distance, the warning module sends voice reminders to achieve the pre control effect of safe operation behavior. The experimental results show that the device can significantly reduce the false alarm rate compared to the video scheme, accurately detect the actual distance between operators and high-voltage live equipment and high-voltage transmission lines in the substation area, and provide timely and effective alarm prompts. Therefore, this scheme can be applied in power distribution operation scenarios to protect the personal safety of workers.