Power distribution and automation stand to gain greatly from the widespread use of connected devices made possible by the advent of the Internet of Things (IoT). The reliability of a SCADA (Supervisory Control and Data Acquisition) system has been extensively shown in the substation environment. The fundamental problem with distribution automation is the lack of distribution-side management, mostly from the field’s geographically dispersed workforce. As a result of their dispersed locations, there has been inadequate tracking of their distribution channels. The smart grid is a power system incorporating evolutionary computing, bidirectional communication, two-way electrical flow, and real-time monitoring. Hence, this paper Internet of Things based Integrated Smart Grid Distribution Management System (IoT-ISGDMS) with fog computing has been presented that addresses issues such as power quality assurance, pole transformers health, and customer consumption in distribution automation. In this paper IoT-ISGDMS uses fog computing which analyzes distribution automation in real-time, making this possible. As a first step, IoT-ISGDMS uses intelligent acceptance systems (IAS) to improve coordination between smart grids and other electronic infrastructures. The second step is to perform comprehensive data analysis, automatically recognize any possible problems, and offer more intelligent fault detection and diagnosis to cut down on time and money spent on maintenance. In conclusion, as the degree of system intelligence rises safeguarding data privacy and the safety of networks will become critical priority areas.

In higher education, physical education courses and ideological and political education are often carried out independently, lacking effective integration and interaction. This paper explores the informationization reform of physical education courses in universities and evaluates its impact on online ideological and political education. First, interdisciplinary integration and informationization methods are used to optimize the physical education course design and integrate ideological and political education content. Then, an online learning platform is constructed to break the limitations of time and space, encourage students to deeply understand the ideological and political education content in physical education teaching, and provide real-time feedback and personalized learning support. Multimedia technology is also adopted to enhance students’ understanding and internalization of sportsmanship and ideological and political education concepts. Students’ learning behavior and ideological and political education absorption in physical education courses are individually evaluated through data analysis. Finally, the profound impact is evaluated through experiments. The results indicate that the informationization reform of physical education courses in universities significantly improves students’ participation and knowledge mastery and effectively promotes the improvement of students’ ideological and political literacy, with the average score increasing by about 14%. These results provide strong empirical support for future teaching design and also provide valuable experience for further exploring the deep integration of physical education courses and online ideological and political education.

As society progresses and science and technology evolve, the need for skilled professionals in the country continues to rise. Ideological and political education (IPE) in courses, crucial for fostering students’ overall development, has shifted from theoretical exploration to practical implementation. Sports colleges should incorporate ideological and political elements into professional courses like sports and computers based on their unique characteristics, aiming to develop high-quality, multidisciplinary talents that align with national requirements. Professional courses are often disconnected from IPE in traditional sports college teaching methods. There is excessive focus on knowledge and technology instruction, while insufficient attention is given to guiding students in developing correct values through the courses. Additionally, there is a lack of well-targeted course design and a comprehensive evaluation system. In response to the above problems, this paper studies and constructs a framework that integrates IPE with basic computer application knowledge, and designs a variety of course designs and teaching methods. In addition, a progressive assessment is designed to introduce the Convolutional Neural Networks (CNN) model and the Bidirectional Encoder Representations from Transformers (BERT) model in the early stage of teaching to conduct preliminary assessments of students’ basic computer skills and ideological and political qualities. In the later stage of the course, the Long Short-Term Memory (LSTM) network model is introduced to analyze student learning behavior and assess overall student quality based on prior evaluations. The experiment shows that the course design studied in this paper can enable students of the School of Physical Education to learn basic computer knowledge while also receiving certain ideological and political quality education. The designed teaching method is better than traditional offline teaching, online teaching and online and offline mixed teaching. When conducting comprehensive quality assessment, a 40% excellent rate can be achieved. Students are randomly selected to track their growth analysis. After the application of this teaching design, 90% of the students’ comprehensive quality assessment remains the same or improves. The findings suggest that the teaching model discussed in this paper not only improves students’ computer skills but also plays a crucial role in their JIANG ideological and political education, effectively achieving the intended teaching goals. The success of the innovative teaching design has been confirmed through practical application, providing a feasible teaching model for sports universities and other specialized institutions.

With the rapid development of video surveillance and multimedia applications, video data is requiring higher bandwidth demands for its transmission, storage, and retrieval. This paper presents a novel approach to video processing based on skeletal information and the recognition of identities. The skeletal data enables the extraction of skeletal data features from video frames and integrates this with the recognition of identities in such a way that the video data gets segmented into skeletal data, identity information, and other relevant data. A multimodal approach like this one spans a broad range in data transmission volume, optimizes bandwidth use, and significantly improves storage efficiency and increases retrieval speed. Experimental results have verified that the proposed method is able to transmit information with efficacy even in complex scenarios and further enable significant improvement in the accuracy and speed of performing storage and retrieval tasks. Such improvements turn into an effective solution for real-time monitoring, behavior analysis, and identity recognition applications featuring strong robustness and adaptability.

Rural population loss is a common phenomenon in northeast China, even in the whole country and all over the world, has significantly hindered economic and social development in rural areas, leading to a weakening of growth momentum and even stagnation. In view of this, this paper focuses on Jilin Province, a typical region, and uses key data such as rural resident population, rural employed population, and job supply in the region from 2008 to 2021. Through the comprehensive application of spatial autocorrelation analysis methods and the geographical detector model, it deeply analyzes the spatio-temporal evolution patterns of the rural occupational and residential function-efficiency at the county scale in Jilin Province, the trade-off and synergy relationships, and the driving mechanisms behind them. The results show that: the synergy level of the rural occupational and residential function-efficiency index in Jilin Province has gradually increased over time; the index shows a steady upward trend and spatial clustering characteristics; the index is influenced by a variety of driving factors, and the mechanisms of these factors vary. These findings will help the government formulate sustainable rural development policies and provide a useful reference for promoting comprehensive rural revitalization and development.

With the development of big data (BD) technology, tourism route planning of historical blocks relies on a large amount of real-time data. The existing research data sources are limited and difficult to integrate, which cannot meet the personalized needs of tourists. This paper combined BD and intelligent algorithms to realize personalized tourism route planning of historical blocks. By collecting tourists’ behavioral data, scenic spot spatial data and real-time traffic information, the paper built tourist portraits and used the neural collaborative filtering algorithm to make personalized scenic spot recommendations. It used genetic algorithms (GAs) to optimize routes, taking into account factors such as tourists’ interests, distances between scenic spots, and traffic conditions. With the help of the real-time data streaming platform Apache Kafka, the paper dynamically adjusted routes to deal with sudden traffic or crowded attractions, thereby improving the tourist experience. The experimental results analyze the consumption preferences and behavioral characteristics of different tourists. Tourist 1002 spent 500 yuan on shopping, and high-end shopping malls and food courts were recommended for him. Tourist ID 1005 preferred “snacks and coffee” in terms of dining, and showed no interest in souvenir consumption. This tourist preferred to stay in leisure places for a longer time rather than a compact travel route. The neural coordination filtering algorithm + GA performed well in terms of total travel time of 4.2 hours, total walking distance of 7.8 kilometers, and traffic congestion coefficient of 0.35, which was better than other algorithms, showing its significant advantages in digital tourism route planning in historical blocks. This method combines BD and intelligent algorithms to improve the tourist experience through personalized recommendations and route optimization, optimize the traffic management of scenic spots, flexibly respond to emergencies, promote the intelligent and refined management of historical district tourism, and provide innovative ideas for future tourism route planning.

Based on accounting informatization, this paper constructs a financial risk prediction system by applying the CNNs (Convolutional Neural Networks)- BiLSTM (Bi-directional Long Short-Term Memory)-Attention model to accurately identify and classify various risk types in enterprise FM (financial management), and improve the accuracy and efficiency of financial risk prediction. CNN was used to extract local features in financial data, BiLSTM was used to capture time dependencies, and finally the importance of financial indicators was weighted and fused through the Attention mechanism. During the training process, the Adam optimizer and cross entropy loss function are used for optimization, and appropriate learning rates and training rounds are set to ensure the stability and performance of the model. The experimental results show that when the epochs is 50, the accuracy of risk classification is 98.9% and the loss value is 0.012. In the analysis of each data level, the average response time of the proposed system and the traditional system is 1.80s and 7.17s respectively. The system in this paper shows obvious advantages in response time and prediction accuracy. The response time is greatly shortened, and it can provide effective support in real-time decision-making. This paper model has significant application prospects in financial risk prediction, and can provide enterprises with efficient and accurate risk warnings, which has important theoretical significance and practical value. Keywords: Enterprise Financial Management, Risk Classification, Financial Data, Accounting Informationization, Convolutional Neural Networks

The model that evaluates the integrated condition of the ecological environment of the lake Taihu is created in conjunction with remote sensing satellite images, ground monitoring data, and other such geo-sourced information. This paper provides a comprehensive assessment framework integrating water quality measures, vegetation indices, and atmospheric conditions to assess-temporal and spatial variations of lake ecosystems. Analysis of five years (2019-2023) of monitoring data reveals significant spatial heterogeneity in water quality parameters, with distinct increase in degradation within the northern and western parts of the lake. Characterised pan-regional eutrophication indicators show clear zonation patterns which are largely distributed in areas of increased human use and zonal hydrodynamic conditions. Seasonal analysis indicates distinct differences in water quality parameters prompting an increase in algal bloom within the summer months. Target areas are designated and analysed in this study and are reflective of critical conditions that require immediate management control measures german Meiliang Bay and the Western Zone. Methodological testing reflects a congenial result resulting in models with high accuracy (R² > 0.89) and reliability within diverse temporal and spatial range. Data obtained partially or largely complement ecological management policy and enable such policies to be formulated where monitoring the health of a lake’s ecosystem and addressing its restoration is key.

The intelligent transformation of the apparel design industry needs to simultaneously meet the requirements of both efficiency improvement and personalization promotion. This paper proposes an intelligent design framework that integrates curve theory, garment prototyping and meta-learning technology. It optimizes the design of apparel by using the smoothness constraints of interpolation curves, the flexibility expression of parametric curves, and the local optimization characteristics of Bspline curves. Combine the prototype-based thinking model with meta-learning method to solve the generalization problem under small sample data and improve the model adaptation speed. The practical efficiency enhancement level and application value of the methods in this paper are verified through practice and testing, etc. The results show that the parametric design can realize the fast garment styling change of single parameter and multi-parameter. The optimization algorithm combining prototyping and meta-learning always takes less than 20 seconds in the 10-parameter range adjustment experiments, which is faster than the comparison algorithm. In the comprehensive fuzzy evaluation of experts and consumers, “very satisfied” and “good” account for 63.99% and 58.42%, respectively. The method based on technology fusion in this paper can significantly improve the design efficiency and user satisfaction of clothing personalization.

With the global informationization boom, information security has become a problem for all of us. In order to be able to effectively detect the physical health status of criminals in prison and ensure the data security of the process, an image encryption method is designed to effectively protect the monitoring information. The process is based on generative adversarial network with generator and discriminator for image generation and data discrimination processing respectively, and optimizes the feature transmission process of image with the help of residual network. The key is generated by chaotic sequence method during the image transmission process. The encrypted image is transmitted to the staff port and the destination image is obtained after the decryption process of data key. The results of the study indicated that the decryption accuracy of the GAN algorithm in the dataset test increases gradually with the iteration process. The accuracy of the image after the completion of the iteration reached 98.69%, indicating that the algorithm has a good restoration effect for recovering the image after transmission. The structural similarity of the data image after the GAN algorithm processing decryption can reach 0.988. The peak signal-to-noise ratio index of the image was 37.78dB, which indicates that the clarity of the image after encrypted transmission is high. The research method can provide an effective theoretical support for the encrypted transmission of video images.