The change of landscape pattern is closely related to the quality of ecological environment, and the study of urban and rural landscape pattern, especially three-dimensional landscape pattern, is of great significance for urban-rural integration spatial planning. Based on the theory of landscape pattern, this study constructs a numerical simulation method for the characteristics of urban and rural threedimensional landscape pattern, and explores the formation of optimization strategies for the threedimensional development of urban and rural areas. Taking Chengdu City as an example, firstly, based on multi-source remote sensing data, the landscape pattern index method and gradient analysis method are utilized to explore the spatial and temporal coupling characteristics of urban and rural three-dimensional landscape patterns. Then the CA-Markov coupling model is used to predict the landscape pattern of future land use, so as to provide a reference for decision-making. The results of the study show that the landscape type changes in Chengdu City from 2005 to 2020 are dominated by the transformation between cultivated land, forest land and construction land, and the reasons for the changes are closely related to the urban development plan. In addition, the accuracy indices of the CAMarkov model all reached more than 80%, and the simulation results were reliable. The model prediction results show that construction land and cropland are the largest transformed landscape types, with a large-scale increase in the landscape area of construction land and a large-scale decrease in the landscape area of cropland. Spatially, the degree of fragmentation of the landscape pattern in Chengdu City gradually decreases, the landscape patches are more regularized, and the overall pattern shows a highly aggregated trend. The research results of this paper can be used as a reference for the optimization policy of three-dimensional landscape pattern in urban and rural areas, and provide data support and innovative ideas for the innovative development of urban and rural three-dimensional landscapes

As a core course of Business English majors, Business English translation plays a crucial role in the cultivation of Business English talents, and how to realize the assessment of translation efficiency in teaching has become a hot topic nowadays. This paper builds up a translation efficiency assessment index system in the teaching of business English translation around five aspects: vocabulary, syntax, context, society, and translator’s factors. Random forest and Lasso regression methods were used to select 15 feature variables including sentence order and collocation between words. The multiple regression linear model was chosen to construct a model for assessing translation efficiency in business English translation teaching, and the model was estimated and tested. The least squares method was used for estimation, and all the parameters were significant (Sig<0.05) except for the variables compound sentences, sentence structure and situational intermingling. The distribution of the residuals of the model approximates to the normal distribution, which satisfies the assumption of normality and the assumption of independence, and possesses a good fit and some explanatory power.

This paper optimizes the K-means clustering algorithm based on the RFM model improved by the entropy weight method and then using the distribution between the samples, and adopts the combination of both density and distance to accurately classify the cross-border e-commerce customers. Finally, the capsule network recommendation model is used as the benchmark model, and the CCN4SR model is designed to accurately recommend goods to customers. The results show that cross-border e-commerce customers are categorized into five-star to one-star customer groups, which focus on “return on investment, pursuit of social value, the pursuit of cost-effective, the pursuit of low prices, while having their own different consumer preferences”. The capsule network outperforms CNN on both training and test sets, and its precision, recall and F1 value are above 92% on the test set, which shows that the capsule network is well adapted in the ϐield of implicit feedback recommendation.

There are many mature traditional navigation algorithms, but most of them are insufficient in the function of environment perception and understanding, and reinforcement learning can give robots the ability to learn and make decisions. This paper proposes a robot reinforcement learning navigation algorithm and optimal control strategy based on deep reinforcement learning. Firstly, Markov decision modeling for local planning of the robot navigation system is implemented, and then a POMDP belief space dimensionality reduction algorithm based on the NMF update rule is proposed to address the situation of excessive dimensionality and combined with PRM to achieve global reinforcement learning planning. Finally, considering the external information interference problem, a power controller based on the TD3 algorithm is designed to ensure that the robot navigation system can accurately track the signals even under the external interference environment.The position error of the robot under the TD3 controller tends to be close to 0, which is much lower than that of the robot under the PD controller. The experimental results of this paper show that the designed TD3 controller can effectively improve the trajectory tracking accuracy of the robot navigation system and better realize the optimization of the robot tracking control function.

Cataract, as an extremely common visual impairment disease, seriously affects the normal work and life of patients, and the optimization of cataract IOL model is of extraordinary significance to the diagnosis and treatment effect. The article collects ocular biological measurements of cataract surgery patients as experimental data, explores the radial basis function (RBF) neural network belonging to the field of artificial intelligence in the process of IOL calculation, and then introduces genetic algorithms to optimize the RBF neural network, and constructs the cataract IOL calculation model based on GA-RBF. The experimental results show that after combining the improved cataract IOL calculation model for telemedicine, the patient’s hospitalization days were shortened by 3.06 d, and the hospitalization cost decreased by $1,383.7, meanwhile, the patient’s satisfaction increased by 4.69%.

This paper investigates and analyzes the optimal allocation of educational resources and the expansion and innovation of the content system of e-commerce English courses in vocational education institutions in Fujian and Taiwan, and proposes methods and strategies for the optimal allocation of educational resources and the innovation of the course system. The evaluation index system of educational resources allocation was established, the factor analysis method was used to establish the educational resources allocation measurement model of vocational colleges and universities, and the K-Means clustering algorithm introducing profile coefficients was applied to cluster vocational colleges and universities on the level of educational resources allocation. The study classified 42 vocational colleges in Fujian and Taiwan into four categories, and based on the results of cluster analysis and factor ranking, the four categories of vocational colleges put forward suggestions for optimizing the allocation of their own educational resources allocation level. The results of the curriculum system innovation practice show that after the teaching design of the e-commerce English curriculum system innovation, the performance of the experimental class is significantly higher than that of the control class, increasing from 22.84 to 26.81 points. It shows that the teaching design of ecommerce English course system innovation is suitable for the needs of English teaching and can provide important guidance for teachers of e-commerce English in vocational colleges and universities when they are teaching.

This paper combines the multifactorial influence of the actual situation, adds the objectives of user interest preference and traditional music overseas communication budget into the influence maximization model, and constructs the Multi-Objective Influence Maximization Model (MOIM) of Chinese traditional music overseas communication to deal with the problem of objective inconsistency in the process of music communication. After that, the seed node selection algorithm of MOEA/D based on decomposition strategy is proposed to improve the search optimization strategy of seeds in the MOIM model. The cross-variance operator designed in the algorithm optimizes the set of solutions generated by the chromosome in the iterative process and finally obtains the Pareto non-dominated solution. The results show that the distribution of Pareto optimal solutions for each graph in the three datasets of TFM, TCC and TCO is very uniform when T=300, and the distribution of Pareto optimal solutions is more uniform with the increase of the number of iterations. The more influential nodes in the multi-objective optimization model of this paper, the higher the cost. The influence and cost of the seed set need to be considered in the overseas dissemination of music, and the seed set should be selected to maximize the influence within the budget. When the network structure and user behavior conform to different characteristics, the MOEA/D model can also get the corresponding undominated solution.The MOEA/D model integrally optimizes the influence index and cost index, so it provides a more flexible set of decision-making solutions for the overseas dissemination of Chinese traditional music.

In the garment production industry, garment cutting size matching plan is an important step in the process, which plays a decisive role in production management and cost control. In this paper, we first model the size matching problem of garment cutting, then use the improved fast particle swarm algorithm (APSO) to optimize the multi-objective optimization solution, and finally verify the performance of the APSO algorithm and the actual effect of garment size matching with cases. Comparing the test results of APSO, PSO and LDWPSO algorithms in the six test functions of Griewank, Ackle, Levy, Rastrign, Schwefel and Sphere, it can be seen that: with the improvement of the problem dimensions, the APSO algorithm used in this paper can still maintain a better optimization accuracy, and the optimization accuracy and stability are significantly improved compared with the PSO and the LDWPSO algorithms. LDWPSO algorithms. In the actual case, the APSO algorithm is more reasonable in the size combination and the number of layers of fabric, for four different types of apparel orders have obtained a superior optimal solution set, cutting production error is far less than the enterprise requirements. At the same time, compared with other optimization methods, the APSO algorithm has better optimization accuracy and solving efficiency, and can obtain a more superior cutting and bed splitting scheme. The algorithm proposed in this paper can effectively optimize the cutting size matching process, reduce fabric waste and production equipment investment, and has good application value and reference significance.

The global urbanization process is rapidly increasing, and a reasonable and scientific analysis of the relationship between urban land expansion and land resources plays an important role in the rational allocation and coordinated development of land resources. This paper constructs a spatio-temporal geographic weighted regression model coupled with geospatial and temporal coordinates, and incorporates temporal and spatial non-stationarity into the model. Then, using the method of hypothesis testing, the temporal non-stationarity and spatial non-stationarity of the spatio-temporal geographic weighted regression model are examined, and at the same time, the multiple covariance test and the variance expansion factor method are proposed to carry out further statistical inference of the model. As the degree of urban sprawl increases, the land resources weaken year by year from the center to the surrounding area.The global Moran’s I for the three periods from 2003 to 2023 are 0.6289, 0.7159, and 0.7368, respectively, which show a trend of increasing year by year. It shows that land resources are strongly influenced by urban expansion, and the spatial distribution of land resources shows spatial aggregation. Several variables, such as building volume rate, population size, regional economic development, regional cultural level, infrastructure construction and urban fallow area, have significant effects on the spatial differentiation of land resources. The above differentiation characteristics provide insights into the rationalization of urban expansion and the scientific allocation of land resources.

In the process of sharing accounting information using cloud computing technology, the integrity of the data is related to the security of the transmission and utilization of accounting information. For this reason, this paper studies the algorithm optimization based on the multi-branch path tree LBT. Multi-branch path tree LBT adopts distributed data storage method to reduce the number of hash operations. The data integrity auditing scheme is designed for different phases of cloud auditing, and the dynamic update process of cloud data is optimized to improve the data integrity verification effect. This algorithm can still maintain a high challenge success rate after more than 300 challenge data blocks, and the total overhead of the experimental computation does not exceed 8 ms, and the verification efficiency is also better than the comparison algorithm. Therefore, the research idea of this paper has validity and has improved effect on data integrity verification in the process of cloud computing smart accounting informatization.