The development of globalization has led to increasingly stringent requirements for translation accuracy. This paper designs a cross-language English translation model based on deep reinforcement learning and translation quality assessment, and selects the Transformer architecture with multi-layer encoder-decoders. Through the reward and punishment mechanism of the intelligent NMT system, real-time probability calculations of contextual information are performed to select the most appropriate words at the semantic level as components of the target sentence. Combined with a supervised quality assessment module, the translated text is scored, and the next word selection is guided. Experiments show that after 934 iterations, the BLEU score stabilizes around 97.62%. The F1 score reaches 99.87% after 316 iterations, and the accuracy achieves a stable value of 90.74% after 815 iterations. In two-class cross-language English translation tasks, the model’s average BLEU scores were 90.67% and 93.08%.
The construction of government-sponsored affordable housing projects requires a significant amount of capital, poses challenges in accounting and financial management, and entails high financial risks. This paper establishes a financial risk warning indicator system for affordable housing construction funding. Through the KW test, indicators that fail to pass the significance test are excluded. Factor analysis is then employed to streamline the indicators, identifying seven common factors: funding security, cost control, project management, operational efficiency, government compliance, market and external environment, and social impact. The extracted public factors are used as input variables for the BP neural network, forming a financial risk warning model for government-sponsored affordable housing construction based on the SSA-BP neural network. The financial risk prediction performance of the model was evaluated. The accuracy rate, recall rate, F1 score, and precision rate of the SSA-BP neural network model in this study were 93.2%, 96.1%, 92.8%, and 93.4%, respectively, all higher than those of the compared BP neural network model, logistic regression model, and KNN model, demonstrating excellent predictive performance.
Aiming at the mixing and separation phenomenon of particles in a gas-solid fluidized bed, a multiscale CFD computational model compatible with a fluidized bed reactor is constructed based on a multiscale model, the gas-phase and solid-phase conservation equations are proposed, and the main structural parameters and non-homogeneous resistance model are determined. Under the condition of considering the bubble mesoscale and the interaction of particle phases in the emulsified phase, a numerical model of mass transfer for gas-solid two-phase flow in the fluidized bed was established, and the complex heat and mass transfer characteristics of two-component particles (quartz sand-rice husk) in the fluidized bed and the parameter influencing law were analyzed. The results show that the flow velocity of solid particles in the fluidized bed reactor increases with the increase of apparent velocity at the inlet gas; the change of mass ratio has a small effect on the transverse velocity of mixed particles, but the effect on the longitudinal velocity is relatively obvious. Meanwhile, it was found that the inlet gas velocity, bed temperature, and operating pressure all had significant effects on the mass transfer efficiency between the gas and solid phases, and a reasonable matching of the external operating conditions is conducive to improving the mass transfer efficiency between the two phases in the fluidized bed reactor.
With the advancement of science and technology, unmanned aerial vehicles (UAVs) are playing an increasingly important role across various fields, making multi-UAV formations a cutting-edge research area. This study establishes a UAV communication topology model based on graph theory and proposes a UAV motion model based on spatial position deviations. A distributed structure is then introduced to design a UAV control system model and establish a UAV formation coordination mechanism. Based on this, a coordination formation controller using a consensus algorithm, a task allocation strategy using the Hungarian algorithm, and an obstacle avoidance strategy are designed. Simulation experiments were conducted with a five-drone formation executing reconnaissance tasks and a triangular formation. The simulation results indicate that the designed control algorithm can maintain the formation of the drone formation, the proposed collaborative control strategy can achieve precise formation maintenance, and it can quickly and accurately track the predefined flight path. During formation flight, collisions between adjacent drones can be avoided, and the formation has excellent reconstruction capabilities, achieving stable and accurate collaborative formation.
Taking Xiamen, a densely populated city with rapid economic development, as an example, this study employs surface temperature data derived from MODIS remote sensing data optimized through remote sensing and artificial intelligence technologies to extract urban heat island and vegetation coverage information. The study conducts dynamic monitoring and spatiotemporal analysis of the urban heat island in the study area. Taking Yunnan Province as another example, based on Landsat OLI/TM and NPP_VIIRS remote sensing data, the study extracts urban built-up area information and examines the expansion characteristics of urban built-up areas from three aspects: the number of expansions, spatial distribution patterns, and nighttime light scale, covering the period from 2000 to 2020. Remote sensing data based on urban heat islands indicate that high-temperature zones are primarily distributed in densely populated urban areas with developed industries and commerce, while secondary high-temperature zones are scattered in the urban-rural fringe areas surrounding high-temperature zones. Areas with better water bodies and vegetation exhibit low-temperature and secondary low-temperature conditions. For example, parts of Xiamen’s urban core are primarily controlled by high-temperature and secondary high-temperature zones. Remote sensing data based on urban expansion indicate that there are significant differences in expansion rates and intensities across different cities and stages. For instance, the expansion intensity of the Dianzhong Urban Agglomeration generally follows a trend of “first decreasing—then increasing—and finally decreasing again.”
The withdrawal of rural households from their homestead sites is an inevitable step in rural revitalization and a product of urbanization reaching a certain stage of development. This study analyzes the practical challenges faced in the withdrawal of homestead sites in traditional agricultural areas. Based on questionnaire data from a traditional agricultural area, statistical analysis and binary logistic regression models are employed to investigate rural households’ willingness to withdraw from their homestead sites and the factors influencing this decision. The study also explores potential pathways to alleviate the challenges associated with homestead site withdrawal in traditional agricultural areas. The results show that the surveyed farmers have a low level of understanding regarding homestead withdrawal, and their overall willingness to withdraw is weak, with less than 30% expressing such intent. The primary factors influencing the willingness to relinquish homestead land include the educational level of farmers, their understanding of whether homestead land can be bought and sold and its ownership status, the type of insurance they participate in, whether they already own housing in urban areas, as well as the geographical conditions of the village, annual household income, and the utilization status of homestead land. The first five factors have a positive influence on the willingness to relinquish homestead land, while the latter three have a negative influence. Strengthening publicity and promotion, establishing exit incentive mechanisms, improving relevant laws and regulations, and developing diverse compensation funding sources are the key pathways to enhance farmers’ willingness to exit homestead land.
To achieve path planning for dual-robot collaboration in forestry robotics, this paper uses visual imaging principles and image processing methods to locate and segment tree images. Then, a kinematic model of the forestry robot is constructed and solved. The Shoal Optimization Algorithm (SHO) and Informed-RRT* algorithm are combined to propose the SHOInformed- RRT* algorithm for path planning in dual-robot collaboration in forestry robotics. Through simulation experiments, it is found that compared with other methods, the proposed SHO-Informed-RRT* algorithm performs best in terms of path planning time, planning speed, path length, average number of sampling points, and number of path nodes. After adopting the SHO-Informed-RRT* algorithm, the mechanical arm of the forestry robot achieves the optimal set target, and the average movement time of the mechanical arm is the shortest.
With the widespread application of cloud computing technology, the security of financial accounting information systems has increasingly come under scrutiny. This study explores the construction of an accounting information management cloud platform based on cloud computing. By analyzing the basic operational mechanisms of online accounting cloud platforms, it designs a DaaS process for the financial early warning module, providing a technical foundation for the operation of the financial crisis early warning module. Subsequently, a financial early warning model for enterprises based on a hybrid LSTM-GRU structure is proposed. Using M Company’s financial data from 2019 to 2024 as the research sample, a set of 24 potential financial indicators is established, covering five aspects: profitability, solvency, operational efficiency, cash flow capability, and growth potential. The research results show that incorporating Benford factors plays a certain role in improving the overall performance of the financial crisis early warning model. Additionally, the model enables more accurate and stable prediction results, with prediction accuracies of 95.74%, 94.83%, and 94.31% for T-1, T-2, and T-3 years, respectively, enabling accurate judgment of future corporate financial trends.
By introducing emerging digital technologies, this study effectively addresses the current challenge of vocational college students’ career guidance and entrepreneurship support being disconnected from the talent market. The article is based on the decision tree method in data mining, proposing appropriate decision factor extraction methods tailored to data samples from the employment market and entrepreneurial experiences. A prediction model combining the C4.5 algorithm and CART algorithm is constructed to forecast career and entrepreneurial tendencies, and based on the prediction results, intervention pathways for ideological and political education are proposed. Through the constructed decision tree, the C4.5 algorithm achieved the highest F-value of 0.841 in predicting students’ employment tendencies. After the integration of ideological and political education, 88.65% of students believed it could help them further enhance their understanding of future career planning. The data-driven method proposed in this paper for addressing employment and entrepreneurship issues among vocational college graduates has achieved certain.
Brain-computer interfaces (BCIs) are an emerging human-machine interface technology that establishes a direct connection between the human brain and machines, thereby offering significant application potential in the field of sports rehabilitation training. This paper focuses on the overall control scheme design of a sports rehabilitation training system, which primarily consists of three components: the host computer, the underlying motion control system, and detection feedback. Magnetic control damping force is incorporated to enhance muscle strength in the patient’s injured limb. In terms of rehabilitation training action matching, preprocessing of electroencephalographic (EEG) signals is performed by removing baseline drift, power frequency interference, and electrooculographic (EOG) interference. Additionally, for the traditional dynamic time warping (DTW) algorithm, a similarity measurement method tailored to the specific requirements of rehabilitation training action matching is introduced to impose temporal constraints on the matching points of two action sequences. This leads to the proposal of an improved DTW algorithm for efficient matching and recognition of rehabilitation training actions. Furthermore, based on the proposed rehabilitation training action matching model, an evaluation method and scoring formula for rehabilitation training actions are proposed. The designed rehabilitation training action matching model achieves an identification accuracy rate of 79.00% or higher for three randomly selected matching action groups.