The rapid development of the electric power market makes the scientificity and rationality of grid investment decision-making particularly important. In this paper, firstly, we design a grid investment benefit assessment method based on fuzzy comprehensive evaluation. And taking the grid investment benefit of M city in 2022 as an example, the fuzzy comprehensive evaluation method is used to quantitatively evaluate the grid investment benefit. Based on the evaluation results, the weaknesses of power grid investment in M city are found. Then the multi-level optimization strategy of grid investment is further proposed to achieve the maximization of investment benefits. The strategy considers the objectives and constraints of different levels, such as grid structure, power supply reliability, operation efficiency, and power sales revenue, and coordinates the interests between all levels by establishing a multi-objective optimization model to achieve the global optimization of the grid investment decision. Finally, after adjusting the allocation ratio and the allocation amount by the multi-level optimization strategy, the overall evaluation of the city’s grid investment efficiency in 2023 is improved from “average” to “excellent”. It shows that the multilevel optimization strategy designed in this paper can provide scientific guidance for grid investment decision-making.

Aiming at the shortcomings of traditional relay protection, an adaptive multi-area protection coordination model is studied and designed. Firstly, combining different control strategies such as master-slave control and sag control, a method of AC/DC distribution network trend calculation and network loss analysis based on the alternating iteration method is proposed and realized to ensure that the adaptive relay protection can act correctly. The proposed method is analyzed for AC/DC hybrid distribution network trend calculation, and the alternating iteration solution method is used for trend analysis and calculation, and the effectiveness of the proposed method is veriϐied by two examples of AC/DC hybrid distribution networks. Then the adaptive Agent with reinforcement learning is introduced, and its constructed multi-agent system has more system adaptive capability. The adaptive current interruption protection is compared with the traditional current interruption protection, and its protection principle and protection scope are analyzed, on the basis of which an adaptive coordinated protection method based on MAS grid is proposed to realize the MAS adaptive current interruption protection, and its simulation is veriϐied. The experimental results show that the method of this paper can signiϐicantly improve the ϐlexibility, effectiveness and stability of AC and DC distribution network operation.

As a key link in international trade, the price volatility of container transportation has a profound impact on the global supply chain, and uncertainty shocks are one of the main causes of price volatility. With this topic, this paper measures the level of uncertainty at the policy level through the uncertainty index construction method, which lays the foundation for subsequent research. Dynamic correlation and impulse effect analyses of container transportation market prices under uncertainty shocks are conducted using DCC-GARCH and SVAR models. China’s economic policy uncertainty index showed four stages of significant increase in 2001, 2008, 2015 and 2019. The overall price volatility of the container transportation market shows an upward trend, and in 2023, the transportation price is 23,835 yuan. Container transportation prices are affected by the uncertainty of China’s economic policies as well as China’s trade policies, with correlation coefficients ranging from -0.69 to 0.60. The influence of China’s economic policy uncertainty index on container transportation price does not have a long time lag effect.

This paper studies the application of numerical simulation in visual communication design from two perspectives of artistic expression and technical application, and explores the facilitating effect of numerical simulation method on the intersection of artistic expression and technical application. Based on the improved K-means method, the extraction of the main color of the image is completed, and the extraction results are input into the color matching model integrating visual aesthetics as the label of the color palette. The visual communication design method is constructed based on image processing technology, and the method is realized through numerical simulation, so as to test the effectiveness of the technology application in visual communication design. Compared with other algorithms, the improved K-means algorithm in this paper can effectively realize the extraction of the main color of the image. The visual aesthetics score in the color matching model ϐluctuates within the range of [1.10,7.09], and the main color extraction result of the improved K-means algorithm combined with this score as a parameter can realize the coordinated matching of colors. At the same time, the visual communication design method based on image processing technology shows superior performance in terms of communication success rate and communication consumption time. According to the role of numerical simulation method in artistic expression and technical application, this paper explores the intermingling of artistic expression and technical application, highlighting the important inϐluence of numerical simulation method in the process of intermingling.

People’s performance requirements for air conditioning along with people’s requirements for indoor air quality also continue to improve, air conditioning heat exchanger as an important part of the refrigeration system in the air-conditioning products in the largest proportion of space. Therefore, this paper is based on ϐinite element analysis of air conditioning heat exchanger optimization design, oriented to the needs of air conditioning heat exchanger, heat transfer to the mechanism of depth analysis. The ϐinite element analysis is used to study the heat transfer simulation theory of air conditioning heat exchanger, and the heat transfer optimization design method is proposed, and the heat transfer model based on ϐinite element analysis is constructed. Through the physical model and its numerical simulation method for veriϐication, the numerical simulation value and experimental value of the pressure drop and convective heat transfer coefϐicient error of ± 6.50W/m² ℃ and ± 12.7Pa, respectively, which veriϐies the model of this paper and the feasibility of numerical simulation method for. Comparing the performance of the optimized air conditioning heat exchanger, the optimized heat exchanger in this paper improves the cooling capacity by 0.04~0.50kW and the total pressure drop by 11.19~50.84kPa compared with the comparative models, which proves that the optimized heat exchanger in this paper has better performance and can meet the performance and reliability index requirements of engineering applications.

Currently, the severity of information leakage is increasing, and attacks and protection against cryptographic devices have become a research hotspot in the ϐield of information security. In order to increase the security of SM4 algorithm structure against side channel attack, the paper focuses on the protection scheme of adding masks to cryptographic circuits to resist DPA attack, and proposes a cipher algorithm design method of ϐinite domain additive coding. Experimentally, it is proved that the additive coding SM4 algorithm used in this paper can correctly and efϐiciently perform encryption, and the encryption efϐiciency is improved by 56.54%~82.42% than the general SM4 algorithm. Meanwhile, it has the security against 1st-order and 2nd-order side-channel attacks, and the success rate against attacks reaches 93.67%, which is higher than that of the compared algorithms by 5.34%~21.00%. It also proves that the scheme has high security against side channel attacks and can provide a reliable solution for the information security of wireless LAN.

Ceramics have many applications, covering scientiϐic research, medical, industrial, jewelry, etc. Ceramic materials are stable and have a silk-like touch. Ceramic 3D printing technology is based on laser curing molding as a rapid manufacturing technology. This paper proposes a personalized design strategy for ceramic artwork, determines the degree of inϐluence of ceramic process parameters on the quality of laser 3D printed ceramic artwork by calculating the Pearson’s correlation coefϐicient, and adopts numerical simulation to obtain the ceramic 3D printing quality data, calculates the error of the number of printed layers, and controls the quality of the printed ceramic artwork. The ceramic quality parameter optimization model is established. Five algorithms of SVR support vector regression, BP neural network, RF random forest, RBF radial basis function, and Kriging model are used to set up the relevant parameters of 3D printing, input the six ceramic process parameters that have been processed by the uniϐied magnitude, and complete the optimization of the quality ceramic process parameters of laser 3D printing. Through the investigation and analysis of the effect of ceramic artwork design, the ceramic color designed in this paper makes the user generate positive emotions; a total of 235 positive emotions were generated, accounting for nearly 60%. The mean value of user preference for ceramic samples is analyzed. The samples with the highest user preference are sample 4, sample 6, and sample 1, and the mean values of preference are 3.425, 3.245, and 3.148, respectively.

Graph neural networks are widely used in image recognition. This paper introduces a two-node graph neural network DouN-GNN model based on a traditional graph neural network. By constructing two nodes, the features in the sample image that are difficult to extract by the shallow embedding network are extracted so that the network model can incorporate more multi-dimensional information about the sample image, thus enhancing image recognition accuracy. Aiming at the problem of the overall performance of the DouN-GNN model not reaching the ideal state, this paper adds three optimization modules to improve the DouN-GNN model and form the IGNN model. The optimized IGNN model is trained, tested, and applied to real-world scenarios such as agricultural weed recognition, natural resource enforcement, and video surveillance to explore the performance of the IGNN image recognition model constructed in this paper in real-world applications. The model achieves the highest accuracy of 98.39% in agricultural weed image recognition, and the classification accuracy for weeds is also high. In natural resources law enforcement and video surveillance, the model in this paper performs better than other image recognition models and can effectively meet the requirements of image recognition in practical application scenarios.

In the current information age, image tampering detection technology is crucial to ensure the integrity and authenticity of digital media, and remote image tampering detection technology combined with deep neural networks has become a research hotspot. This paper adopts convolutional neural network as the main detection tool, and on the improved DPN network model, the feature fusion module based on the attention mechanism is used to fuse the two features in this paper. In this way, the image tampering detection technique based on dual-stream feature fusion is proposed in this paper. The precision, recall and F value of the detection algorithm in this paper are better than the comparison algorithm. When the image compression quality factor is reduced to 20, the precision rate, recall rate, and F value of this paper’s algorithm do not appear to be greatly reduced, and the reduction is only 0.028, 0.041, and 0.042. This paper’s image tampering detection algorithm, which fuses the frequency domain branching module and the attention mechanism feature fusion module, has a higher detection efficiency. And the Accuracy rate, Recall rate and F Value of this paper’s algorithm on image level detection are 17.8%, 15.3% and 16.3% higher than that of DCT algorithm respectively. In conclusion, the remote image tampering technique combined with deep neural network provides an effective solution to ensure the authenticity and integrity of images.

In order to improve the impact toughness and service life of GF/EVE composites, this paper applies the thermoplastic nonwoven fabric structure to the preparation of GF/EVE composites. The thermoplastic polyurethane was used as the raw material, and the meltblown method was used for the preparation of thermoplastic nonwoven fabrics, and then the prepared thermoplastic nonwoven fabrics were used for the preparation of GF/EVE composites through the VARTM device. For the properties of GF/EVE-TPU composites, specific test methods are given to define the moisture absorption rate and the erosion performance based on the consideration of the stress change of its hygrothermal properties, and the determination of the interlaminar fracture toughness is given.The critical damage threshold load of GF/EVE-TPU composites is 1.57kN, and its contact force increases with time, and the composites are aging in After 60 days, its moisture absorption and erosion weight loss in alkaline environment were 0.736% and 81.19%, respectively.The optimum fracture toughness value of 9g/m² thermoplastic nonwoven structure incorporated into GF/EVE composite was 0.97kJ/m², and the GIIC value of GF\EVE-TPU30 was increased compared with the GF/EVE material without interlaminar toughening by 183.83%. Combining the thermoplastic nonwoven fabric structure with GF/EVE composites can enhance the erosion resistance and interlaminar toughness of the composites and improve the service life of GF/EVE composites.