The accelerated pace of life and social competition become more and more intense, and the problem of psychological pressure faced by people in their study, work and life becomes more and more serious and common, this paper proposes a multi-channel physiological feature fusion method of psychological state assessment for the identification of students’ psychological state in the ideological and political education of college students. The collected multidimensional physiological signal data, such as pulse and picoelectricity, are feature extracted, and the wavelet transform is used to reduce the noise of the physiological signals and realize the waveform filtering, and then the DS evidence theory is combined with the SVM, and the extracted physiological parameters of pulse and picoelectricity are used to realize the effective assessment of psychological stress. Experiments show that the method proposed in this paper of using wavelet decomposition coefficients instead of the original physiological signals as model input can improve the accuracy of psychological stress detection, and the MAPE value of psychological state assessment using the SVM-DS algorithm is 12.28%, which can realize the assessment of students’ psychological state in ideological and political education of college students.

In recent years, art education in colleges and universities has been more and more emphasized by the state and education departments, and has been comprehensively promoted and developed. The study builds the evaluation index system of art education development and assesses the development of art education in a university in order to identify its realistic dilemma. On this basis, the dung beetle algorithm is used to optimize the random forest algorithm to construct a decision tree assessment model of art education development. Through comparison experiments, the prediction accuracy and stability of the DBO-RF model are confirmed, and the deviation of its assessment results from the real value is below 4%, and the RMSE (12.247), MAE (9.133), and MSE (178.829) are lower than that of the comparison method, and the EV (0.721) and R² (0.719) are higher than that of the comparison method, which is applicable to a certain extent. The long-term and overall development of art education in colleges and universities can be promoted by establishing art education mechanisms, strengthening art practice activities, establishing resource sharing channels and developing scientific systems.

In order to improve the design of intelligent products, user cognition and perceptual engineering are integrated into intelligent product design. And through subjective survey and physiological measurements and other techniques to measure the user’s emotional experience of the product, to construct the user’s emotional evaluation model based on BP neural network. Multimodal interaction technology is used to optimize the product design method, and the implicit needs of users for intelligent products are obtained through the method of multimodal perception, which is matched with the product interaction, so as to propose the intelligent product design strategy based on multimodal interaction. In order to verify the effect of the strategy, physiological indicators and perceptual imagery are obtained to evaluate the products. Finally, the user satisfaction of intelligent products under this strategy is studied. The benefit ratio of the smartwatch designed based on the design strategy of this paper (0.438811) is better than other market competitors. The user satisfaction of the 10 experience dimensions of this smartwatch is distributed in the range of [80%, 93%], the dimension with the highest satisfaction is functionality, the lowest is attractiveness, and the overall satisfaction is 86.6%, and the smartwatch designed by this paper’s design strategy obtains a high level of user satisfaction.

As an important clean energy project, the optimization of the construction and operation of photovoltaic (PV) power plants is crucial in the context of the global active promotion of low-carbon development. This paper focuses on the optimization of cable wiring scheme for PV power plants based on the taboo search (TS) algorithm. A mathematical model is established by comprehensively considering the constraints such as power loss objective and tidal current calculation in the wiring optimization process. The wild dog optimization algorithm is improved using the Lévy ϐlight algorithm, and the initialization phase of the taboo search algorithm is improved by the improved wild dog optimization algorithm, and the established cabling optimization model is solved using the improved taboo search algorithm (LDOA-TS). The experimental results show that the LDOA-TS algorithm has a signiϐicant performance advantage over other algorithms in the model solving process. At the same time, the simulation results obtained from the optimization model in this paper are basically consistent with the actual wiring pattern under different working conditions. And through the model of this paper for cable optimization wiring compared to the original wiring scheme in the point cable length and power loss were reduced by 30.30% and 49.95%, to meet the constraints at the same time to effectively achieve the model objectives, and has obvious economic beneϐits, in line with the needs of the low-carbon era of photovoltaic power plant construction and operation.

With the gradual depletion of fossil energy resources and the increasingly severe environmental problems, photovoltaic power generation as a typical new energy industry has been highly favored in recent years. In the face of the low efficiency of components often faced by photovoltaic power plants in actual operation, this paper proposes a maximum power point tracking algorithm (IGWO) based on the Gray Wolf optimization algorithm, which optimizes and joins the dynamic weights to expand the search range of the algorithm, and improves the efficiency of solar energy utilization. The gray wolf algorithm is further applied to the optimization of photovoltaic (PV) arrays in power stations, and a PV array reconfiguration algorithm based on the gray wolf optimization algorithm is proposed to randomly generate a radial structure by the broken circle method, and the best reconfiguration scheme is obtained through iterative optimization search. The optimization experiment of photovoltaic power station was carried out, and the photovoltaic array reconstruction algorithm in this paper was used to reconstruct in the static shadow occlusion mode, and the GMPP after reconstruction was significantly improved, and the shadow occlusion mode was increased to 14515.565W, 10626.844W, and 10636.467W, respectively, and the tracking accuracy of the IGWO algorithm in this paper also reached 99.9%, 99.5%, and 99.6%, respectively. The tracking accuracy of the IGWO algorithm in this paper for MPPT tracking control is consistently above 99% level under dynamic shadow shading mode.

Fe-based soft magnetic composites have important applications in reactor core manufacturing due to their superior magnetic properties. In this paper, the vibration noise characteristics of the reactor with core made of this material are investigated and simulated and optimized by finite element method. First, a three-dimensional finite element model of the reactor is established to analyze the electromagnetic force distribution and vibration displacement velocity, and then the accuracy of the finite element analysis model is verified by combining the simulation experimental data. On this basis, the oxidation time parameters of the Fe-based soft magnetic composite material are adjusted, and the optimal parameters are selected to improve the vibration of the reactor, so as to achieve the purpose of improving the working condition of the reactor. The results show that the magnetic loss and other properties of the material have an important influence on the core vibration, and the reasonable optimization of the composite material structure parameters can reduce the vibration amplitude of the reactor and effectively suppress the noise. This study provides a theoretical basis for the design and optimization of this type of reactor.

Fe-based soft magnetic composites are widely used in power electronics and power system equipment due to their excellent magnetic properties and low iron loss. As a key component, the performance of the core reactor directly affects the operation efficiency and stability of the power system, and the traditional design method is difficult to take into account the electromagnetic performance and noise control at the same time. In this study, genetic algorithm is used to co-optimize the core structure, electromagnetic parameters and noise characteristics to reduce losses, improve electromagnetic compatibility, and reduce the noise generated during operation. In terms of methodology, a multiphysical field calculation model is constructed based on finite element analysis, electromagnetic performance and noise source characteristics are simulated, and genetic algorithm is used to optimize the parameter combinations under the constraints to form an optimized design scheme. During the optimization process, a suitable objective function is selected and combined with a multi-objective optimization strategy to balance the electromagnetic performance and noise suppression effect. The results show that the optimized core reactor is better than the traditional design in terms of loss, magnetic field distribution and noise level. The optimization scheme derived from the study can effectively improve the electromagnetic characteristics of the equipment and significantly reduce the noise level, providing strong support for the design and improvement of related equipment.

The level of informationization infrastructure of the power system is constantly improving, and it is of great practical significance to carry out real-time perception and early warning of environmental risks during the construction period of the project based on image processing algorithms. This paper proposes a multi-scale parallel real-time detection algorithm based on SSD, which optimizes the network structure of SSD algorithm, combines and splices different sizes of inverted residual blocks and different types of activation functions with each other, and designs a kind of lightweight feature extraction network EPNets. Then, it proposes a lightweight parallel fusion structure, which is applied to the multi-scale prediction of the lightweight feature extraction network, and optimizes the environmental risk real-time detection speed of the algorithm. The algorithm is optimized for realtime environmental risk detection speed. A Bayesian network-based environmental risk behavior warning model is constructed to provide real-time warning for the detected risk behaviors. By comparing with the original algorithm and existing target detection algorithms, the multi-scale parallel fusion detection algorithm based on SSD proposed in this paper can maintain good detection speed with low loss degree, and its environmental protection risk identification time is only 9ms.Meanwhile, the early warning algorithm in this paper realizes the accurate early warning of the soil erosion risk in the study area through the soil erosion environmental protection risk during the construction period of transmission and transformation projects detected. It provides an objective guideline for the control of environmental protection risks and work priorities.

The regulatory capacity sufficiency of the grid is not only a technical indicator for dispatchers to measure the safe and stable operation of the grid, but also an important indicator for assessing the reliability of the grid, and an important basis for the planning and transformation of the grid. This paper combines the objective function and constraints of time and space scale optimal scheduling of provincial power grids with a high proportion of new energy, and establishes a model for optimal scheduling of power grids. Improved DE-ICA stochastic optimization search algorithm is used to seek the optimal value of the model, to obtain the optimal regulation method of the power grid driven by the multilevel spatio-temporal combinatorial optimization algorithm, and to put forward the quantitative assessment method of the adequacy of the power grid regulation capacity. Simulations and empirical case studies show that the regulation cost of the provincial grid is reduced after the application of the optimization algorithm, and the power balance effect and the regulation capacity adequacy are improved compared with the traditional scheme. The quantitative evaluation method of grid regulation capacity adequacy proposed in this study can comprehensively and accurately describe the transmission capacity of the grid under long-term supply and demand, which can provide more accurate reference information for power system security and planning.

Maximum supply capacity calculation is an important issue in grid planning, and with the large amount of renewable energy sources connected to the grid, the voltage instability problem becomes more and more prominent. In this paper, the maximum power supply capacity (TSC) of partitioned flexible interconnected grids under multi-temporal and spatial scales is dynamically modeled, and the TSC model is solved by using the deep deterministic policy gradient (DDPG) method to achieve quantitative assessment of the TSC of the grid. Meanwhile, the effectiveness of the model and algorithm is verified through simulation experiments. The stepwise approximation method and DDPG algorithm without considering the transient voltage stabilization constraints and the obtained TSC in which the node voltages are less than 0.80 p.u. are all greater than 1.0 s, and the transient voltages are destabilized. While the DDPG algorithm considering transient voltage stabilization, the obtained node voltage is greater than 0.80p.u., and the transient voltage is in a stable state, which indicates that the algorithm can effectively reduce the risk of transient voltage instability in the power grid. The sum of the TSCs of A and B divisions after the zonal flexible interconnection is 9348 MW, which is higher than the sum of the TSCs of 8696 MW during the zonal open-loop operation, indicating that the zonal flexible interconnection can improve the overall TSC level of the power grid. In addition, compared with the traditional algorithm and other reinforcement learning algorithms, the TSC calculation based on the DDPG algorithm is more efficient and accurate. This paper provides methodological guidance for evaluating the power supply capacity of power grids at multi-temporal and spatial scales.