In this study, generative adversarial network is used as the basic architecture, and the multi-head attention mechanism is introduced to enhance the model’s ability to perceive and process image features. The image generation process is optimized by bilinear interpolation to further enhance the detail expression of character design. The generation efficiency of the model and the quality of the IP image are improved by the improved network structure. A personalized recommendation model with implicit feedback and explicit feedback is also used to achieve targeted placement of IP image characters for agricultural and sideline products cartoons. The study combines the local characteristics of Jilin Province, taking Jilin rice as an example, and designs two rice brand IP images with regional characteristics, “Rice Xiaoji and Rice Xiaoling”, which have a good migration effect. When the recommended list length is Top=10 and 20, the recommendation effect of internal diversity of Jilin rice brand reaches 83.47% and 89.09% respectively, and the recommendation effect of overall diversity reaches 88.43% and 95.31% respectively. It can be seen that the method of this paper can improve the market competitiveness of agricultural and sideline product brands in Jilin Province, which provides a technical path and practical reference for rural revitalization in Jilin Province.

Supply chain inventory forecasting and control is an integral part of supply chain management system, and it is a focus that industries must pay attention to in their operation and management. In this paper, the supply chain inventory demand forecasting model is constructed from the perspective of supply chain end, combined with the Transformer model in AIGC technology. The DL-Informer model is used to improve the Transformer model, realize the feature fusion of graph convolutional neural network, design and solve the feature graph adjacency matrix and complete the information fusion of each feature subgraph to improve the prediction accuracy. Aiming at the problems faced by supply chain inventory demand forecasting, the traditional algorithm with strong local optimization ability is combined with the genetic algorithm, and the hybrid genetic algorithm (HGA) is proposed to solve the nonlinear optimization problem. In the supply chain inventory forecasting practice, when the forecast length is 12, the MSE, MAE and RMSE index values of this paper’s forecasting model are 0.202, 0.174 and 0.416, respectively, which have more stable long-term forecasting performance compared with other models. And in the nonlinear simulation optimization experiments, the HGA algorithm shows good convergence and outstanding optimization effect in the nonlinear problem of supply chain inventory.

Under the guidance of relevant theories and techniques, this project binarizes and segments red cartoon images, and then extracts their contour features. Neural network classifiers are used to identify and classify the outline features to realize the acquisition of visual symbols of the revolution in the history of Chinese red cartoons in the past 100 years. With the help of Pierce semiotics, the system of revolutionary visual symbols is constructed, and the system is explored in depth. Compared with other models, this paper has a high superiority on the recognition of revolutionary visual symbols in Chinese centuries-old red cartoons, and seven items of revolutionary visual symbols are extracted, specifically, flag, badge, gear, pentagram, wheat ear, hammer and sickle. In addition, the visual symbol system of the revolution has a high degree of recognition, for example, the CMYK value of the flag is 0, 100, 100, 0, and its color is red, which symbolizes the red of “passion and revolution”, which well reflects the “red years” of China’s development and the fruitful results of the revolution and construction. The fruit of construction.

In this paper, first of all, the data preprocessing of ethnic patterns is carried out through image segmentation and grayscaling processing methods, and then the image processing technology is applied to the feature extraction of ethnic dress patterns, and the improved SIFT algorithm is used for the feature extraction of images. The original DCGAN algorithm feature extraction ability is weak generates style picture fuzzy, the effect of the problem of poor, proposed the use of 32-layer deep neural network with residual structure instead of the original 5-layer shallow feature extraction network, significantly enhanced the algorithm’s feature extraction ability, enhance the model of the style migration effect. By introducing the objective evaluation index PA of the improved SIFT algorithm, the algorithm was compared with other algorithms, and the segmentation algorithm experiments were carried out with the local patterns of several images, and the results of pixel accuracy PA were obtained to be greater than 0.95, which confirmed that the improved SIFT algorithm was able to realize the accurate extraction of the contours of local patterns. In terms of pattern quality evaluation dimension, the subjective average scores of the amateur group and the expert group are 4.87 and 4.89 respectively, indicating that the ethnic patterns generated by the algorithm of this paper have reached a high standard in quality.

With the accelerated pace of society and the increasing pressure of competition, the issue of mental health has received increasing attention. Especially in the field of education, students’ mental health status directly affects their student outcomes and overall development. The aim of this study is to design a mental health status monitoring system based on large-scale data streaming computation, to realize dynamic real-time monitoring of individual mental health through multi-source data acquisition and efficient algorithm processing, and to explore its application in educational scenarios. Sliding window algorithm and Hidden Markov Model are used to analyze and process the collected multi-source data such as physiological signals, and the experimental results show that the system is able to significantly test the difference between people with high and low scores on psychological test scales in the monitoring of mental health status, and it can provide educators with valuable decision-making support and help students’ mental health education and intervention.

Solving the health problems of key populations such as people with disabilities is an important way to realize universal health coverage and promote social equity. Sports as the main means of rehabilitation for disabled people at present, this study clarifies the concept of disabled people and sports rehabilitation, and uses empirical investigation to analyze the plight of cruel sports, laying a realistic and theoretical foundation for this paper based on high-dimensional rehabilitation data optimization. In the process of sports rehabilitation exercise for the disabled, it is generated through the modular control of multi-skeletal muscles by the central nervous system as well as the regulation of neural oscillations. And the surface EMG signal is the combined effect of superficial muscle EMG and electrical activity on the nerve trunk on the skin surface, which belongs to the high-dimensional data characteristics. For this reason, this paper constructs a rehabilitation exercise muscle synergy model using matrix decomposition for the rehabilitation of disabled people’s sports. The data were refined in the time domain by adding time windows, and then the data were decomposed into targets based on the non-negative matrix decomposition method to extract the muscle synergy features in each time window, so as to analyze the muscle synergy differences in different exercise processes and different feature frequency bands, and to further obtain the muscle synergy law during exercise and the physiological change mechanism of the nervous system during exercise control. Finally, the experiments were carried out in both non-electrical stimulation and electrical stimulation modes, and the results showed that the number of muscle synergism in wrist flexion and extension was the same in both modes, which was 3. However, the number of synergistic pairs of muscles in the electrical stimulation mode was significantly increased. It also proves the effectiveness of the method of this paper on the analysis of muscle synergy of multi-channel surface EMG signals, which provides a new method for exploring the muscle synergy characteristics and the control mechanism of rehabilitative movement in the process of disabled people’s sports.

In order to cope with the damage of urban electricity and the dilemma of residents’ electricity consumption caused by flooding disaster, we study the dynamic planning of intelligent operation and maintenance equipment scheduling and distribution network restoration under flooding disaster. Consideration is given to both pre-disaster deployment and post-disaster scheduling levels, while dynamic planning is carried out for collaborative repair and energy storage scheduling to construct a scheduling model with multi-source collaboration. Based on this, a multi-resource cooperative post-disaster recovery strategy for distribution networks is further proposed. The usability of this paper’s multi-source cooperative strategy is studied in depth through case analysis. Among the six Cases of the simulation experiment, the total cost in Case 1, which is operated and restored according to the strategy proposed in this paper, is the lowest, which is only 257080.2 RMB. The maximum, minimum, and average values of the solution time of the multi-source cooperative strategy are much faster than those of the comparison methods, and it has obvious advantages in fast decision making. The multi-source synergy model in this paper is able to recover all the loads within 285 min, while the finite synergy model takes 330 min. The multi-source synergy model was able to recover 7,500 kW of load, while the limited synergy model was only able to recover 6,850 kW. The multi-source cooperative model has strong applicability.

Organic light-emitting diodes (OLEDs), as a new generation display and lighting technology, are critical for charge transport and luminescence efficiency enhancement. The study determines the potential, electron and hole concentrations in OLED devices based on Poisson’s equation and current continuity equation, and fits the charge transport process in the devices with the drift-diffusion model. The differential equations are solved by improved Euler’s method and iterative solution method to simulate the operating state of the OLED device. In conjunction with experiments, the enhancement effects of the OLED devices optimized based on the differential equation model in terms of charge transport and luminescence efficiency are analyzed. The optimized device and the comparison device exhibit the same partial pressure and a largely overlapping luminescence curve at 450~460 nm, but the optimized device lifetime and brightness are better than the comparison device. The charge transfer efficiency of the optimized device exceeded 99.99%, while that of the comparison device was lower than 95%. In addition, the light extraction efficiency of the optimized device is more than 20% higher than that of the comparison device, and it has the highest current efficiency, i.e., the optimized device has a better luminescence efficiency. The differential equation model is used in OLED devices to describe the processes of charge transport, optical properties, etc. The model can be used to systematically optimize the material properties and improve the overall efficiency of OLED devices.

This paper proposes to design the power meter based on TMR current sensor, screen the chips that meet the requirements of the power meter, and stipulate the technical specifications and technical parameters of the power meter based on TMR current sensor. Design the system structure of power meter with TMR current sensor including MCU module, storage module, communication module and so on. And design the main and vice system clocks in the single-phase energy meter with TMR current sensor. Analyze the design of signal acquisition module, bias adjustment and temperature compensation module, communication module and circuit protection module in the current monitoring system. According to the characteristics of the TMR sensor, establish the objective function, improve the GWO algorithm, and optimize the design of the multi-stage magnetic ring structure current sensor. The performance parameters of the TMR sensor are analyzed, and the DC current test and AC current test are conducted to verify the performance of the TMR current sensor measurement module. The accuracy, precision and linearity of the current measurement module are tested, and the relative error between the actual current value and the theoretical current value derived from the formulae in the DC current test and the AC current test are controlled within 5% in the TMR current measurement system. The measurement system based on TMR current sensor meets the current measurement requirements.

Artificial intelligence technology can effectively improve the quality and efficiency of industrial design and manufacturing, so the study takes Shuangdun Carved Symbols of cultural products as an example, utilizes the generative adversarial neural network to carry out style migration processing in the design of Shuangdun Carved Symbols and their products, and constructs the DCGAN model to assist the design and generation of Shuangdun Carved Symbols of cultural products. After semantic analysis of the color symbols of Shuangdun Carved Symbols products generated with the aid of DCGAN model in this paper, quantitative and qualitative measurements are carried out. Users of Shuangdun Carved Symbols products rated the products after the style migration significantly higher than before the migration in terms of volumetricity, distance, emotion, character, and texture.CycleGAN and DCGAN models achieved the best overall results in terms of PSNR, SSIM, FID, and KID indicators. The DCGAN model with added spectral normalization and Res2Net outperformed the CycleGAN model in the ablation experiments. The overall user rating of the Shuangdun Carved Symbols product designed by the DCGAN model in this paper is 4.24, and the product has obtained more satisfactory evaluation results.