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.

Taking Xi’an metro station as an example, we analyze the artistic methods of metro public space to explain and spread the ancient rhyme culture, modern industrial civilization, modern revolutionary culture and other regional cultures, and put forward the concept of further innovative design in terms of increasing the occupancy, enlarging the pattern, highlighting the cultural characteristics of the ancient capital, and strengthening the comprehensive utilization of the metro public space in multilevel and multi-carrier. The study first establishes a multi-objective metro public space configuration model based on genetic algorithm to realize the reasonable layout of metro public space. Then it proposes a style migration method of regional cultural elements based on the improved circular consistency generating adversarial network, and realizes the color migration under multiple reference objects to realize the interface and color design of the entrances and exits of the metro public space, the station hall level and the platform level. The results of user experience show that the overall public space of Xi’an Metro Line 4 has an intermediate centrality of 8.8216 and an intermediate centrality potential of 0.5859, and its overall suitability is relatively balanced.

In order to promote the development of medical rehabilitation industry, the study deeply analyzes flexible wearable devices and utilizes joint moment estimation based on skeletal muscle model in order to calculate the joint moments of elbow and wrist joints, so as to carry out the design of flexible pneumatic wrist joint system. And a fuzzy-PI dual-mode control strategy is used in the position control of the flexible pneumatic wrist joint to construct an intelligent flexible rehabilitation device for the wrist joint. The wrist joint rehabilitation equipment is systematically tested to analyze its practical application effect. The response speed of the fuzzy-PI dual-mode control method is faster than that of the traditional PID control strategy, and it can effectively reduce the vibration noise. The accuracy of the hybrid recognition method in this paper is 97%, which is better than the single recognition model. The average time taken by the wrist rehabilitation device on the seven tasks of lifting, grasping, undertaking, pulling, pushing, probing down and probing up is between 2.06 and 2.67 seconds. The output moments of the wrist and elbow positions were 17.1 and 11.6 N.m respectively for the human body-worn wrist joint rehabilitation device with 50N driving force output, and the joint output moments decreased significantly, and the joint comfort of the human body was improved greatly.

Currently, visible and infrared image fusion (VIF) technology has a wide range of applications in road safety monitoring, anti-surveillance, etc. However, the traditional image fusion algorithms in the feature fusion process will have limitations such as part of the information is lost, etc. For this reason, this paper proposes an infrared visible image fusion algorithm based on the double-branching and decomposition of the results. The algorithm firstly adopts the dense block method, extracts visible image features, and uses a feature pyramid network to extract infrared features. The algorithm firstly adopts the dense block method to extract the visible image features, and uses the feature pyramid network to extract the infrared features, then, based on the deep learning network structure to extract the image information of different modalities, and designs the fusion network constrained by the three loss functions of the gradient loss, intensity loss and decomposition loss, so as to obtain a good fusion effect of the image. The experimental results show that the proposed algorithm achieves the optimal value in five indexes, and reaches sub-optimal value in one index, indicating that the proposed algorithm fuses the images with the optimal value and sub-optimal value. At the same time, the proposed algorithm retains the main thermal radiation information of infrared images better than other algorithms such as DenseFuse and IFCNN, which is superior to some extent.