Service quality is the key for takeaway platforms to maintain their advantages in the fierce market competition. In this study, we construct a mathematical model to solve the takeaway delivery problem by ant colony algorithm, so as to realize the takeaway delivery path planning based on ant colony algorithm. The grey neural network model is used to predict the order demand in the takeaway platform, and the fruit fly algorithm is used to fine-tune and optimize the parameters in the grey neural network model to avoid the model from falling into the local optimum and to improve the accuracy of the model in predicting the takeaway demand. Through simulation experiments, it is found that the planning algorithm in this paper can successfully realize the reasonable planning of takeaway delivery paths when the initial positions of merchants, users and delivery workers are known. The gray neural network optimized using the fruit fly algorithm is also able to accurately predict the takeout demand of platform users based on the order data provided by the takeout platform. Using the method of this paper for the improvement of the service quality of the takeaway platform can significantly improve the delivery efficiency of takeaway orders and develop personalized service strategies according to user demand, thus enhancing user satisfaction with the takeaway platform.

With the rapid development of China’s economic level and the significant improvement of people’s living standard, the quality issue of peaches has become more and more strict. In this paper, based on deep learning algorithm, we propose the recognition method of peach fruit color, size and fruit shape features, combined with near-infrared spectroscopy detection technology, to quantify the peach fruit components and discriminate its maturity. Differential algorithm, standard normal transform, and multiple scattering correction are applied to pre-process peach fruit data. Based on M-YOLOv5s target detection framework, spectral analysis and image characterization techniques were used to jointly detect the degree of peach fruit disease. The distribution of peach fruit quality parameters was investigated, and the test results showed that 39.19% of the samples with measured values of fruit size were concentrated at 1.60-6.40 cm, and 61.79% of the samples with predicted values were concentrated at 2.50-7.50 cm, which was located at around the mean value of 4.763 cm.The classification accuracies of the information modeling set and validation set for the combination of the spectral analysis and image eigenvalue detection techniques were 91.439% and 88.487%, respectively, and the combined use of the two techniques had a high accuracy for the differentiation of diseased peach fruits. Based on the experimental results, the application of spectral detection technology in food freshness detection as well as pesticide residues and illegal additives is explored.

This paper designs the system structure to meet the impact test of aircraft landing, and utilizes finite element calculation to derive the maximum impact stress of the impact platform and the maximum bearing stress. Analyze the attitude combination measurement system, based on the coordinate transformation theory to build a digital level, attitude probe and inclination sensor combination of attitude measurement model, the horizontal attitude angle of the object to solve the calculation. And the robustness overall least squares method is applied for plane fitting. The overall flow of the attitude measurement experiment is designed to analyze the stability and accuracy of the spatial attitude measurement system based on the combination of multi-sensors, and analyze the measurement error of the measured target in different states (translation or deflection). Different attitude solving algorithms are used to measure the attitude angle of the dynamic simulation experiment, and the measurement errors of the roll angle, pitch angle, heading angle and the root-mean-square error are compared. The RMS errors of the roll angle, pitch angle and heading angle measured by the attitude solution model in this paper are 0.2982, 0.2214 and 1.0333, respectively.Comparing with the data in the charts and graphs, it can be seen that the measurement errors and RMS errors of the attitude solution algorithm used in this paper are smaller, which are more in line with the requirements of the target spatial attitude measurement.

The development of communication technology and the rapid growth of the number of mobile network service users have made the competitive situation in the market of communication service increasingly fierce, and maintaining the stock of users is of great significance to the sustainable development of telecommunication enterprises. In this paper, we collect relevant data features of telecommunication users, and after pre-processing the features with RFM model, we use XGBoost model to analyze the importance of each user’s feature value. Then we use the secondary classification Stacking integration model that combines the base learner and the meta-learner to predict the telecom subscriber churn. Comparative validation reveals that the prediction model in this paper shows excellent prediction performance in all four datasets. Practical application results show that the effectiveness of churn maintenance efforts by telecom companies is improved after applying the model, and the average maintenance response rate reaches 50.63% in the first quarter of 2024. The prediction model proposed in this paper based on the binary classification method can assist telecommunication companies to manage the stock of subscribers, optimize the maintenance work plan, and reduce the subscriber churn rate in the telecommunication work period.

The employment and entrepreneurship career choice planning of college students is an important constituent module of the talent training system of colleges and universities in the new era. Aiming at the traditional ant colony algorithm with poor realm adaptability and a large number of inflection points, this paper proposes an ant colony algorithm based on Sigmoid statistical iteration. The Sigmoid activation function distribution strategy is adopted to reduce the blindness of the algorithm’s presearch, and the heuristic function is dynamically adjusted by the introduction of the adaptive factor to reduce the convergence time of the algorithm, and finally the pheromone update function is dynamically adjusted according to the number of iterations to construct the career choice path planning model and apply the model to the career choice planning path recommendation system. When the number of users is 1000, the average response time of the proposed system is only 322ms, the throughput is 394, and the pass rate is 100%, and the CPU occupancy and memory usage are lower than those of the traditional system (35.32% and 39.83%).

With the rapid development of distribution networks in China and the increasing penetration of renewable and traditional energy sources, it is necessary to study the optimal allocation of capacity and optimal operation for the two stages of pre-planning and practical application of distribution networks. In this paper, the probability density function is used to model the uncertainty of “source” and “load” respectively, and the optimal allocation model of distributed power supply capacity of distribution network system is constructed by the equipment models of “wind generator”, “photovoltaic generator”, “diesel generator” and “battery”. Comprehensive cost and power supply security are taken as the objective function and constraints, respectively, to improve the distributed power supply capacity optimization, and adaptive sparrow search algorithm is applied to solve the model. In the comparative analysis of source-load synergy, source-load synergy and energy storage system joint optimization configuration scheme, the joint planning of DPV and ESS enhances the installed capacity of DPV by about 13.45%, and the average power generation of the joint planning scheme is 88.35 kW/h. The joint planning obviously enhances the installed capacity of DPV under the condition of slightly increasing the DPV curtailment. Examples are examined to verify the practical application of the proposed adaptive sparrow search algorithm in configuring the power supply capacity of the hybrid generation system, and the cost of using the cyclic charging operation scheme is 81,067 yuan lower than that of using the load-tracking scheme, and the economic effect has been significantly improved.

In order to solve the adverse effects of uncontrolled charging of electric vehicles on the distribution network, the study constructs a Monte Carlo-based uncontrolled charging load model to calculate the effects of uncontrolled charging on the electric vehicle side on the distribution network load and voltage. Based on this, the electric vehicle trip chain is modeled by Bayesian network so as to manage the charging options of electric vehicles. The charging loads of EVs managed by the Bayesian network at different sizes and different charging locations are predicted to explore the impact of the Bayesian network on EV charging and distribution grid loads. The peak weekday grid base load occurs at 11:00 AM (3695 kW) and 20:00 PM (3656 kW). On weekdays, the grid base load occurs at 12:00 pm (3495 kW) and 20:00 pm (3725 kW), and the peak load increases significantly with the increase of penetration rate and the time is gradually advanced. The end node 18 has the lowest voltage and the lowest value of voltage at node 18 is 0.9135 and 0.9140 on weekdays and bi-weekdays respectively when only the base load is present. At 100% penetration, the minimum voltage is 0.9015 and 0.9008 on weekdays and bi-weekdays, respectively. When the penetration rate of electric vehicles is 20% and 30%, the average value of peak load of electric vehicle charging power increases to 150.05kW and 220.85kW. When the charging scheme of residential charging + office charging is used, the peak load of EV charging in residential areas is reduced by 60.3%.

Higher-order cognitive computational modeling focuses on the large amount of data generated by learners during their educational activities in order to make predictions and inferences and obtain their cognitive characteristics. In this paper, the original ant colony system algorithm is improved. Considering learners as ants, through state transfer probability calculation, pheromone updating, and continuous iteration of multiple ants with the same cognitive characteristics, the optimal teaching path suitable for the learner can be derived. After analyzing, it can be seen that comparing with the data of other GA and ACO algorithms, the improved ACO algorithm in this paper achieves the optimal training effect. By setting up the experimental group and the control group, it can be found that the teaching paths of the five students who did not use the method of this paper were all longer. Therefore, a concise and precise teaching path can be designed from the complicated learning resources and activities. Compared to the control group, the students in the experimental group presented more significant grammar scores and grammar learning attitudes (p<0.001).

Smart grid technology is developing rapidly around the world and is gradually applied to the operation and maintenance management of power systems, and its main advantage lies in its integration capability, which can effectively realize the high efficiency, security and reliability of power system operation and maintenance. This paper explores the integration of grid operation and maintenance by integrating computing and information theory using multidimensional data mining and analysis methods. The operation data of smart grid is first preprocessed, including resampling and PCA dimensionality reduction of multidimensional data signals. Then, a CNN-based power operation state prediction model and an R-CNN-based grid fault diagnosis model are constructed to ensure the stable operation and timely maintenance of the smart grid, and the predicted and actual values of the smart grid operation state of the CNN model are basically consistent with each other, with the MAE, MSE, and RMSE of 0.00104, 0.00014, and 0.012, respectively, and the prediction results are good. The effect is good. Compared with CNN and SVM, the performance of R-GNN model is better, and after PCA dimensionality reduction, the fault identification rate of R-GNN model is as high as 98.91%. And the delay of the R-GNN method for fault diagnosis is only 0.04s, while it can realize the comprehensive and accurate localization of the fault area. This paper provides methodological reference for the utilization of multidimensional data mining and analysis technology to realize the operation and maintenance integration of smart grid.

In this paper, the Gamma process is used to describe the change of cutting force coefficient and analyze the time-varying stability of chattering, and then the time-varying reliability model of chattering of turning machining system is established. The optimal Coupla function model is selected by the AIC criterion, and the reliability analysis of the turning machining system is carried out by using the Monte Carlo method and the VC-MCS method which introduce the Coupla function, and at the same time, the fuzzy factors of the turning machining process are taken into consideration, and the fuzzy optimization mathematical model of turning machining is set up with the goal of the lowest machining cost, and then the model is solved by using the multi-objective particle swarm optimization algorithm, which realizes the fuzzy optimization in the aerospace manufacturing. Then the model is solved using a multi objective particle swarm optimization algorithm to realize the reliability optimization of turning machining process in aerospace manufacturing, and the fuzzy optimization mathematical model of turning machining is experimentally verified by taking common plane milling and cylindrical turning as an example. The experimental results show that the analysis results of the VC-MCS method and the Monte Carlo method with the introduction of Coupla function are almost the same, which verifies that ignoring the correlation between the parameters affects the turning reliability results, and secondly, the turning machining system operates well at full rotational speeds when the turning width b=0.63mm. Finally, according to the case results, the effectiveness and feasibility of the proposed optimization method is proved, which can provide certain optimization objectives for improving the efficiency of turning machining.