In order to explore the relationship between multi-source terrain features and lightning activity in Inner Mongolia, monitoring data and digital terrain elevation data of thunderstorm activity in Inner Mongolia from 2014 to 2025 were collected, and the spatio-temporal data mining method of mathematical and statistical analysis was used to analyze the distribution characteristics of lightning activity in Inner Mongolia. Based on the selected terrain feature factors, the machine learning method of multiple regression analysis is used to establish a research model of multi-source terrain features and lightning activity for quantitative analysis. The results show that the frequency of ground flashes in Inner Mongolia is mainly concentrated in May-October, accounting for more than 92% of the whole year, and the seasonal characteristics of its ground flash activities are significant, and the current intensity is mainly concentrated in the range of 20-40 kA. Correlation analysis reveals that multiple features of multi-sourced terrain are positively and negatively correlated with the frequency of lightning ground flashes and the current intensity (p < 0.05), and the prediction error of the constructed regression model for the ground flashes' frequency and the current intensity is 7.31%. The prediction errors of the constructed regression model on ground flash frequency and current intensity are 7.31% and 5.08%, which can provide a reference for lightning disaster prevention and mitigation in Inner Mongolia.

In response to the rapidly developing market demand, this paper proposes the use of genetic algorithms in industrial product design optimization under simulation environment. Design the product base gene coding, use the fitness function to determine the fitness value of different individuals, the genetic operator to support the optimization of industrial product design, by clarifying the optimal individual in the population in order to determine the optimization of industrial product design to meet the conditions. Then build up the industrial product design system based on genetic algorithm, plan the functional modules such as product information collection and coding, genetic generation of product solutions, and formulate the system process and function realization method. Exploring the performance of this paper’s industrial product design model in the simulation environment, this paper’s model in the operation efficiency, convergence speed and other aspects of performance are better than its other comparison model, in the iteration to about 300 times to achieve convergence. In the application practice of this paper’s design system, the values of this paper’s system are close to 1, and the RMSE values of each design parameter are lower than 0.5, and the average product quality score reaches 0.157, which is excellent in real-world applications.

The double bass, as the instrument with the lowest timbre and the largest volume in the string section of a symphony orchestra, is the “mainstay” of the orchestra’s acoustic effect, and grasping the bass performance mode in double bass performance is a problem that all double bass players need to explore in depth. A cluster-weighted multi-view kernel k-means clustering model (CWK2M) is proposed to study the local quality differences of the bass performance score views at the cluster level. The proposed weighted multiview clustering algorithm is then compared with several multiview clustering algorithms on several real multiview data for experiments and analysis of pitch change patterns. The experimental results show that, on the whole, the proposed algorithm in this paper obtains a relatively good clustering effect on each multiview data, especially on the Sens IT dataset of bass performance scores, the performance of each metrics is significantly improved, and the precision, recall, F1 value and NMI metrics are 0.632, 0.653, 0.687, and 0.713, respectively.In addition, the algorithm of this paper is utilized for the three bass playing patterns such as TaS1, Py11 and Mla1 are further analyzed, which further validates the universality and performance effect of the improved weighted clustering algorithm proposed in this paper for the analysis of pitch change patterns in bass playing.

The energy consumption problem of building complexes has become increasingly prominent along with the acceleration of urbanization. In order to achieve efficient energy saving in building complexes, this study proposes a Bayesian network-based uncertainty modeling in decision-making system for energy consumption management. By analyzing the uncertainty factors in the energy consumption data, a Bayesian network model is constructed to predict and analyze the energy consumption. And the uncertainty factors are used as decision variables to construct the energy consumption management decision-making system based on Bayesian network. The experimental results show that the uncertainty model and decision-making system constructed in this paper have more favorable performance compared with other benchmark methods, and exhibit smaller measurement errors in experimental tests. At the same time, the application of this paper’s decision-making system for energy consumption management of building complexes can significantly reduce management costs, and obtain the double benefits of reducing energy consumption and saving costs.

Based on the demand of load balancing in distributed system scenarios, this paper introduces the concept of dynamic priority in the algorithm and designs the dynamic feedback load balancing (DFLB) algorithm for numerical analysis. Through the closed-loop process of collection-feedback-utilization-collection, the overall performance of the system is realized. The Mininet tool and the Floodlight controller are used when building the load balancing system experimental environment to verify the reliability of the algorithm from the response delay, throughput and other indicators. The study shows that the DFLB algorithm reduces the response time of the system by about 20% compared with the static deployment method, and the DFLB algorithm reduces the load variance, saves computational resources, and makes the load of the system more balanced and efficient. The average throughput of the DFLB algorithm is improved by about 10% compared with the PALB algorithm and DALB algorithm, and 6% compared with the PALB algorithm and DALB algorithm, respectively. Starting from 1000 concurrent connections, the DFLB algorithm has a higher access rate. Thus, the algorithm leads to an improvement in the overall performance of the system.

The field of machine translation has made significant progress in recent years, but how to improve translation accuracy and context consistency is still an urgent challenge. In this paper, a context-aware translation accuracy improvement strategy based on deep reinforcement learning is proposed for English translation. Based on CNNs neural machine translation model, the multi-intelligence deterministic deep policy gradient algorithm is utilized to combine the output of the translation model with the human evaluation index (BLEU), and the reward function is constructed to guide the model learning. In addition, in order to enhance the context-awareness of the model, the study introduces a context encoder in the deep reinforcement learning framework to capture sentence-level contextual information and incorporate it into the translation process. The experimental results show that the optimized model has better training performance, with 40 epochs of iterations, the Loss converges to 0.135 up and down, and its English translation F1 value is 94.95%. And as the number of encoder layers rises, the number of semantic high-level features increases. The N-GRR difference between the generated translation and the standard translation of the model in this paper is the smallest, and the over-translation phenomenon is less. The number of out-of-set word interference is more than 6, and the BLEU value of this paper’s model is improved by 17.89% to 55.55% compared with the comparison model. And the algorithm has good translation performance, with METEOR scores of 0.562~0.803 on different topics. The research results fully verify the effectiveness of deep reinforcement learning based on deep reinforcement learning to improve the accuracy of English machine translation.

This paper proposes a risk indicator system for mental health management of college students that takes individual developmental status, social environment, human-computer interaction, and negative emotions as the first-level indicators, and clarifies the path of obtaining mental health management monitoring data, the weights of the indicators, and the safety warning interval of mental health management. Because of the uncertainties in the mental health management of college students, fuzzy logic is introduced to deal with the uncertainties of environmental changes, student behavior and other factors in the mental health management, and to improve the level of mental health management in colleges and universities. A fuzzy logic-based risk warning model for mental health management of college students is designed. The mental health status of students is further refined by the SCL-90 scale, and the mean score level of each factor of the scale is compared with the youth norm and adult norm. Input the fuzzified student mental health data in the fuzzy logic risk early warning model, and output the risk score of the fuzzy logic model for mental health management of college students. When the set threshold is 60, the fuzzy logic risk early warning model can effectively identify the abnormal values of students’ mental health, and the early warning model has practical utility.

How to communicate with users in a timely and effective manner and determine the intentional purpose of customers plays an important role in promoting continuous user interaction and improving service efficiency in the power marketing industry. The article firstly researches on a single-round natural language understanding algorithm based on intent-slot bi-directional interaction, which adopts a bi-directional information flow to realize the bi-directional information interaction between intent and slot. In the intention recognition layer, the interaction attention mechanism is utilized to introduce slot context information. Then the overall design scheme for the construction of an intelligent customer service system for power marketing from dialogue state keeping, multi-round question and answer, model storage to answer visualization is proposed, and the potential functional requirements are analyzed exhaustively. Finally, experiments from various aspects prove the effectiveness of the proposal in this paper. In the comparison experiments on MixATIS with MixSNIPS dataset and DSTC4 dataset, the metrics are improved by 0.3%, 1.5% and 0.5% respectively when comparing GL-GIN model on MixATIS dataset. This leads to the feasibility of the intelligent customer service system for power marketing constructed in this paper.

In recent years, socio-economic development and the process of massification of vocational education have been accelerating. The article surveys the current situation of the articulation between vocational education and undergraduate education through questionnaires. On this basis, in order to better realize the cultivation of employment-oriented talents, it designs a teaching resource acquisition method based on computational optimization, constructs a crawler search method by fusing genetic algorithm and ant colony algorithm, and realizes automatic clustering by using a clustering algorithm based on the combination of K-mean and particle swarm algorithm in random search direction. The results show that only 23.3% of the students think that there is no duplication of content between vocational and undergraduate education, 89.6% of the students want to set the teaching content according to different needs, and the current talent cultivation for the articulation of vocational and undergraduate education suffers from poor wholeness and monotonous tendency. The proposed crawler search method and automatic clustering method show superior performance and can accurately extract teaching resources and process structured information. Finally, the employment-oriented talent cultivation model is proposed to actively explore the path of integrating vocational and undergraduate education and promote the development of vocational education.

Key frame extraction is an important research content for human motion capture data analysis and processing, for this reason, a key frame extraction method for motion capture data based on quantum particle swarm optimization algorithm is proposed, which can either extract a definite number of key frame sequences or extract key frame sequences according to the objective function. In this paper, the spatio-temporal graph convolutional network is selected as the benchmark network for tap dance action recognition, and the dance action recognition is realized by combining adaptive and attention mechanisms. The comprehensive index of tap dance is introduced and used as a constraint, and the golden section algorithm is used to optimize the training path of the dance action to obtain an ergonomic training path. The experimental results of this paper show that the key frame extraction method of motion capture data based on quantum particle swarm optimization algorithm meets the need of real-time compression of motion capture data. By constructing the validation dataset, the accuracy improvement of AAST-GAN algorithm and the effect of gesture extraction are compared and verified, and the recognition accuracy reaches more than 86%, which is a good recognition accuracy for each tap dance action. The dance movement training path proposed in this paper ensures the effectiveness and comfort of tap dance movements.