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.

The sorting residue of copper clad laminate stacked on site contains polybrominated diphenyl ethers (PBDEs) and novel brominated flame retardants (NBFRs). If not properly treated, they will be discarded into the surroundings and cause secondary pollution. The PBDEs and several NBFRs were detected in the sorting residue of copper clad laminate (SRCCL) of the storage yard. The ∑9PBDEs and ∑5NBFRs concentrations ranged from 2.71 to 122.83mg/kg. Different storage yards displayed three composition patterns of PBDEs, indicating that their sources were different, with domestic and imported ones. All results indicate that untreated SRCCL dumping sites are an important source of PBDEs and their emissions.

The emergence of ride-hailing services has revolutionized the transportation industry for passengers, prompting taxi services to evolve from the conventional method of street-hailing to a combined “online-offline” operational approach. In this new model, taxis combine on-street pickups with platform-based orders. When market supply and demand are imbalanced, leading to excess orders, taxis prioritize street-hailing for faster customer acquisition. Meanwhile, ride-hailing platforms address surging passenger demand by offering subsidies to attract more vehicles to participate in online dispatching. This study focuses on the strategic choices of ride-hailing platforms and taxis during order overflow scenarios. An evolutionary game model is constructed to simulate taxi street hailing behavior under such conditions. Simulations are conducted to generate interpolation-based probability curves, including the probability of taxis accepting offline orders and the probability of regional orders being served. These findings offer recommendations for ride-hailing platforms on designing subsidy strategies in response to changes in regional order density. Additionally, the study examines how factors such as order distance, passenger-seeking costs, and platform commission rates influence taxis’ order acceptance strategies.

Aiming at the problem that military equipment resources are easily affected by high-frequency random disturbances such as emergency order insertion, abnormal processing quality, equipment operation failure, etc. in the process of processing task execution in the cloud manufacturing environment, which causes the quality of service (QoS) of product processing to fail to meet the personalized needs of customers, a dynamic selection method of equipment resources in the cloud manufacturing environment is proposed. According to the running characteristics of cloud manufacturing services, a dynamic evolution model of service quality towards the process of processing task execution under cloud manufacturing environment is constructed. Taking the state vector and control vector in the dynamic evolution model as node variables, combined with Bayesian network, a decision model for dynamic selection of military equipment resources under random disturbance is established. By solving the model, the corresponding scheme of the optimal QoS value is obtained, and the dynamic selection of military equipment resources is realized. The experimental results show that this method can effectively and dynamically select military equipment resources, reduce the price and time cost of military equipment manufacturing, and improve the reliability of product processing, platform satisfaction and comprehensive QoS score.