Accurate short-term load forecasting of distribution networks can ensure the normal life and production of the society, effectively reduce the cost of power generation, and improve the economic and social benefits. Aiming at the multivariate information that affects the power load, this paper utilizes factor analysis to reduce the dimensionality of the original influencing factors, and obtains the main influencing factors with the highest contribution rate, so as to guarantee the accuracy of the neural network prediction. On this basis, the neural network structure is improved by combining AlexNet and GRU, and the short-term load prediction model of distribution network is finally constructed. The relevant charge data of N village in 2023-2024 is used as a research sample to analyze the main influencing factors of its short-term load change, and three main influencing factors affecting the load change in the short term are identified as temperature, air pressure, and humidity factor. Based on the real data of N-village distribution network to carry out prediction simulation experiments, the load short-term prediction curve of this paper’s model has a better fitting degree and good stability, and the values of the prediction result evaluation indexes MRE, RMSE and MAE are smaller than those of the other comparative models, which are basically able to maintain a prediction accuracy of more than 90%.

Aiming at the problem of large prediction error caused by the complex background of macroeconomic prediction, this paper proposes a macroeconomic prediction model based on time series clustering. The model adopts sparse self-encoder to deeply mine the features of the input vectors, constructs a bidirectional threshold cyclic unit network, and predicts the preliminary trend of the macroeconomy, and proposes a time series deep clustering algorithm that integrates the multi-scale feature extraction and clustering objectives of time series data into the same network. A sample generation strategy based on data augmentation and a multiclassification assistance module are used to extract the invariant patterns contained in the time series data to obtain a better representation for targeting time series clustering. Comparing this paper’s model with different forecasting models, the RMSE metrics are 0.0038 and 0.003 for the two time horizons, which are better than the other two models. The prediction range of this paper’s model for future GDP is 5.8%-5.9%, which is smaller than the GDP prediction range of the ARIMA model, indicating that this paper’s model is suitable for the realistic application of macroeconomic forecasting.

Computer image-assisted design, as a product born in the era, provides more inspiration and creativity for art design. Based on the study of the basic theory of color design and the theory of color harmonization, an intelligent color matching model integrating visual aesthetics based on conditional generation adversarial network is proposed. Then a candidate graphic layout generation method based on visual saliency is proposed, which not only considers the visual saliency of each element in the image, but also considers how to generate candidate text regions under the constraints of aesthetic rules. In the visual analysis analysis experiment, under different color transformations, the F-value of the subject’s gaze time was 2.548, with significance P=0.051, which is not significant. The F-value of average gaze point is 6.398, significance P=0.002, significant difference is obvious. From this, it can be concluded that the artistic innovation design method proposed in this paper can make the subject’s point of interest change with a large difference, and the color that highlights the target object can significantly attract people’s attention, which is a feasible artistic innovation design scheme.

Through the investigation of Chinese reading comprehension ability, the evaluation index system of Chinese reading comprehension ability is constructed, combining the hierarchical analysis method (AHP) and the data characteristic method (CRITIC) to combine the indexes to assign weights, and then using the fuzzy comprehensive evaluation model to calculate the indexes to quantify Chinese reading comprehension ability. After that, the indicators affecting Chinese reading comprehension ability in language education were screened and sorted out using a binary logistic regression model, and the Chinese reading comprehension ability education was optimized based on machine learning. This paper constructs a systematic evaluation model of Chinese reading comprehension in colleges and universities with 5 first-level indicators and 22 second-level indicators, and obtains the final score of the system of 87.73 points, the fuzzy comprehensive score of the five first-level indicators of “reading ability, general comprehension ability, deep comprehension ability, evaluation appreciation ability, and comprehensive application ability” is between 86.63 points and 88.68 points, and the fuzzy comprehensive score of 22 second-level indicators such as vocabulary, language comprehension ability and logical reasoning ability is between 80.68 points and 90.38 points. The final score of each indicator was 88.67, and the model was evaluated extremely well. In addition, the empirical analysis showed that all the indicators had a significant effect on Chinese reading comprehension (P < 0.05), and the language education should be optimized in terms of vocabulary mastery and the cultivation of critical thinking.

Reasonable and scientific supplier selection and resource allocation is a prerequisite for enterprises to optimize the quality of supply chain and avoid business risks. In this paper, we select multiple supplier evaluation indexes, use decision tree algorithm to train and calculate the hierarchy of suppliers to determine the supplier options that can be selected. Then the main body of procurement resource planning decision-making is divided into three types: purchaser, database vendor, and customer, to establish a multi-objective model for optimal allocation of procurement resources, and the model is optimized by genetic algorithm to solve the optimal allocation scheme of procurement resources. The supplier selection method based on decision tree can realize the optimal selection of suppliers by constructing a decision tree and transforming it into If-then classification rules. The procurement solutions based on genetic algorithm are 10.44%, 4.31%, and 5.14% higher than B, C, and D solutions, respectively, for better allocation of procurement resources.

As the most intuitive visual phenomenon of animated films, color has emotional characteristics that are closely related to the viewers’ emotional experience. From the perspective of chromaticity and psychology, we explain the method of color emotion quantification, calculate the fuzzy affiliation degree and grey correlation degree for the uncertainty and fuzziness between color and emotion mapping, put forward the method of fuzzy grey correlation for emotion mapping in animated movies, and carry out the experiment of color emotion mapping in animated movies. Through the experiment, it is found that the character color schemes of warm, cold and neutral colors are suitable for the design of character color emotion experience in animated movies. Taking the animated film “Ne Zha: The Descent of the Magic Boy” as the research object, the correlation between color emotion mapping and character matching is further explored. Most of the H-value color blocks in Ne Zha are distributed between 0-60, which indicates warm and neutral tones, and the distribution of S-value and V-value color blocks shows a clear trend of decreasing color saturation, while the overall luminance remains basically stable. The whole film takes the proportion of red, blue, color purity changes and other aspects of color design to achieve the position of the characters, the character of the transformation of the transformation of the matching and implied.

Aiming at the allocation of teaching resources for school affairs scheduling, a decision-making model for school affairs scheduling is designed based on a multi-objective optimization model. The “conflict detection and repair” module is added after the “initial population generation” operation in the traditional genetic algorithm, which decouples the scheduling model and meets the needs of scheduling decision-making. The designed method is compared with the standard genetic algorithm and stochastic two-point crossover genetic algorithm on the data set, and then the efficiency of resource allocation for school scheduling is improved by solving an example problem. The average faculty satisfaction with scheduling is 2.8, which is about 17% higher than the second place NPGA. Applying the algorithms to a college scheduling project, the feasible solutions of the algorithms in this paper satisfy all the various constraints, and the results of the three-stage style algorithm in the self-selected course scheduling mode yield better solutions than the baseline algorithm based on the course set in any of the arithmetic cases. This paper provides an informative solution path for the allocation of school scheduling resources, which can satisfy the course allocation needs of the three parties: teachers, students and schools.

Speech-text multimodal large model as a key tool in the operation of the power industry, its fault prediction performance directly affects the operational safety of mechanical equipment, this paper designs a detailed scheme for the optimization of its performance. Firstly, the structural design of the unimodal model is discussed, and the audio classifier based on Wav2Vec2 and the text classifier based on BERT are used to pre-train the model. Based on the above foundation, a multimodal model is introduced, with the cross-attention mechanism as the fusion strategy, so that the different modal information in the deep neural network is fused with each other, thus improving the accuracy and robustness of the recognition task. After completing the fault feature extraction task, on the premise of introducing the relevant theory of BNN, the structure of BBN is optimized, and after fusing the HC algorithm, BIC and annealing idea, the fault diagnosis method based on the improved BBN network is constructed by combining the fault feature extraction method in the electric power industry and the optimized BBN method. The effectiveness of the method is verified through simulation experiments. The prediction accuracy of this paper’s method for nine categories of fault data is above 90% at a high level, and the prediction accuracy of faults in some categories can reach 100%. The multimodal model fusion strategy proposed in this paper significantly improves the performance of fault feature recognition, in addition, the fault diagnosis method based on the improved BBN reduces the computational volume of the model and improves the fault prediction ability of the model.

With the accelerated pace of life and outdoor running constrained by the environment and other factors, the consumption in treadmill is on the rise, and at the same time, the design of treadmill is more and more concerned. Starting from the customer demand, the user demand analysis method is formed by synthesizing KJ method, rough set theory, KANO theory and AHP, and combining with the prediction theory of destructive innovation technology. And the design requirements and their weights derived from the QFD model are used as the criteria for PUGH decision evaluation to select the optimal treadmill design solution. Finally, the treadmill design scheme is applied specifically. In the planned turnover analysis after the treadmill is put into operation, the turnover scale is increased from 0.73 billion yuan in 2016 to 180 million yuan in 2020. After the experimental test, both the percentile 10% of the female human body and the percentile 90% of the male human body in the treadmill to carry out some of the necessary actions are in a more comfortable state, at the same time, the various joints of the force and angle are in a reasonable range. The design program of this paper’s method outputs better evaluation results, and meets the user’s expectations.

The development of off-grid wind power to hydrogen systems is crucial for promoting renewable energy, reducing dependence on fossil fuels, and achieving sustainable energy development. However, the volatility of wind power can lead to problems such as shortened service life of batteries and electrolyzers. This study proposes an optimized scheduling strategy for off-grid wind power hydrogen generation systems, considering the degradation of batteries and electrolyzers, with a focus on the impact of battery state of charge (SOC) overrun and electrolyzer overload on system operation. A voltage degradation model for electrolyzers was established by analyzing different operating conditions, aiming to improve utilization capacity and reduce degradation costs. Additionally, a degradation model for energy storage batteries was developed, considering factors such as cycle depth, cycle number, and SOC overrun, to optimize charging and discharging operations, extend battery life, and reduce degradation costs. The effectiveness of the proposed scheduling strategy was verified through detailed simulation analysis, demonstrating improved wind power consumption capacity, slowed degradation of batteries and electrolyzers, and ultimately enhanced economic benefits for the system.