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Thermal error of the machine tool spindle is one of the main factors affecting the machining accuracy. For the complex operating environment of the machine tool, the difficulty of thermal error prediction modeling, and the low accuracy of the traditional thermal error prediction model, a spindle thermal error prediction model based on the improved particle swarm optimization (IPSO) optimize back propagation (BP) neural network is established in this paper. The temperature measurement points are clustered by SOM neural network, and the correlation analysis method is used to explore the correlation between the thermal sensitive points and the thermal error of the spindle. The S-type function is used to improve the inertia weight coefficient of the IPSO algorithm so as to improve the particle optimization effect. IPSO is used to optimize the parameters of BP neural network, such as the initial weights and thresholds. Compared with the GA-BP prediction model, the modeling efficiency, robustness, and accuracy of IPSO-BP neural network prediction model are all superior GA-BP prediction model. Taking the thermal error of the electric spindle of precision CNC machining center as the research object, the intelligent temperature sensor and the laser displacement sensor are used to obtain the machine tool temperature values and the spindle thermal error values. The prediction accuracy of the GA-BP model for the spindle thermal error was 93.1%, and the prediction accuracy of the IPSO-BP model was 96.5%. The results show that the IPSO-BP model can accurately predict the thermal error of the spindle under different working conditions. The model can obtain higher thermal error prediction accuracy and is more suitable for the thermal error compensation model.

In order to improve the precision of clothing development of fast fashion brands, consumers’ sense of experience, and brand loyalty, a design method of clothing pattern is proposed by combining Kansei engineering theory and improved particle swarm optimization (IPSO)–back propagation neural network (BPNN) model. First, based on the theory of Kansei engineering, the perceptual image experiment of clothing patterns was designed, and the mean value of perceptual image evaluation of clothing patterns by young consumers was obtained through an online questionnaire survey. Second, based on the IPSO and the BPNN, the nonlinear correlation mapping model between the design elements of clothing pattern and consumers’ perceptual image is established. Finally, based on the calculation of target image weight by analytic hierarchy process (AHP) method and IPSO-BPNN model, the optimal combination of clothing pattern design elements under the requirement of multi-target image is output. Taking the paper-cut pattern of sweater shirt as an example, the feasibility of this research method is verified. The research not only helped the designer to design a costume pattern that meet the individual emotional needs of consumers, but also provided a clear design index and reference, and made the costume design process more targeted, precise, and intelligent.

The classroom teaching mode combining the flipped classroom and the paired classroom can better emphasize the exchange of information between teaching and learning, learning and learning, and then solve the problems of students’ explanation, demonstration, and practice ability. This paper explores the English teaching process in public security colleges under the guidance of the new teaching mode on the basis of the flipped classroom and analyzes the applicability and feasibility of the flipped classroom to English teaching in public security colleges. In order to explore whether the English teaching mode of public security colleges under the flipped classroom can improve students’ English performance, this paper constructs an English teaching quality evaluation model for public security colleges under the flipped classroom by improving the BP neural network. Through the teaching experiment to explore the changes in students’ English performance, the English performance of the experimental class fluctuated greatly, with an average score rising from 60.563 to 77.582, which is an improvement of 28.1%. It shows that the effectiveness of English teaching is better in the flipped classroom teaching mode.

This paper utilizes the generalized Fisher index (GFI) to decompose the factors of carbon emission and exploits improved particle swarm optimization-back propagation (IPSO-BP) neural network modelling to predict the primary energy consumption CO2 emissions in different scenarios of Beijing-Tianjin-Hebei region. The results show that (1) the main factors that affect the region are economic factors, followed by population size. On the contrary, the factors that mainly inhibit the carbon emissions are energy structure and energy intensity. (2) The peak year of carbon emission changes with the different scenarios. In a low carbon scenario, the carbon emission will have a decline stage between 2015 and 2018, then the carbon emission will be in the ascending phase during 2019–2030. In basic and high carbon scenarios, the carbon emission will peak in 2025 and 2028, respectively.

With the acceleration of urbanization in China, haze pollution has become a problem that cannot be ignored. PM 2.5 is one of the main components of haze, and this paper aims to find a stable and accurate prediction method for PM 2.5 prediction. Combined with existing studies, BP neural network is commonly used for prediction and optimization, but its accuracy is not satisfactory due to the randomness of the initial parameters of BP neural network. In order to solve this problem, this study proposes a new type of fusion model-improved particle swarm optimized backpropagation neural network (IPSO-BP) model. In this paper, we use the BP neural network to predict the value of PM 2.5 , and at the same time, we use the improved particle swarm algorithm to optimize the initial parameters of the BP neural network, which makes the prediction performance improved. Taking a simulation experiment in Nanchang City as an example, the prediction accuracy is 86.76%, the correlation coefficient R 2 is 0.95734, and the root-mean-square error (RMSE) is 5.2407. Compared with a single BP neural network model, the advantages of the IPSO-BP model are: (1) Asynchronous learning factor is used, particle swarm algorithm (PSO) exists two learning factors, individual learning factor c1 and population learning factor c2, the former affects the local search ability while the latter affects the global search ability. Through the iterative formula proposed in this paper, the algorithm can be made to satisfy the strong global search ability in the early stage and the strong local search ability in the later stage. (2) Adaptive inertia weights are introduced, where larger values of inertia weights mean that it is more difficult to change the direction of the particles. In the initial stage of the model, a larger inertia weight helps to improve the global search ability of the algorithm, while a smaller inertia weight helps to improve the local search ability of the algorithm as it enters the end of the search. Adaptive inertia weights are the iterative formulas proposed in the paper that make the inertia weights of the model large at the beginning and small at the end. (3) Incorporating the Levy flight search strategy, which aims to solve the shortcomings of traditional particle swarm algorithms that often fall into the suboptimal solution, it can be judged according to the evolutionary effect of the particle position, and if the particles are still unable to enter the more optimal position in many iterations, the Levy flight will be used to update the position of the particles, which is a strategy that increases the vitality of the particles. In summary, the IPSO-BP model proposed in this study has excellent predictive ability and, makes some positive contributions to the cause of air pollution prevention.

Abstract In order to achieve ice disaster warning for transmission lines, this article proposes an improved method based on particle swarm optimization (PSO) backpropagation (BP) neural network for ice thickness prediction. The study selects multiple factors such as temperature, humidity, and wind speed, introducing neural network prediction methods to explore the situation of ice thickness on transmission lines. The predictions from this model demonstrate closer alignment with actual values and achieve superior prediction accuracy compared to both the BP model and the PSO-BP model, and the average absolute percentage error of improved PSO (IPSO)-BP is 0.007. Using a 110 kV line as the experimental object, the study conducts ice melting without power outage by changing of the grid currents. Additionally, this predictive model method is employed for ice warnings, assessing ice coverage levels by computing the ice coverage ratio. This facilitates precise control over the activation and deactivation of ice melting devices. The method proposed in this study addresses the issue of low accuracy resulting from the singular data types used in traditional early warning models. Future efforts should focus on further validating the applicability of this method under varying climatic and environmental conditions to achieve real-time, precise control over line ice melting.

Kitchen anaerobic wastewater contains a high concentration of insoluble organic matter, and the degradation of organic matter in the wastewater is the key to treating kitchen anaerobic wastewater. The Fenton oxidation process is used to treat kitchen anaerobic wastewater, and the effects of H 2 O 2 dosage, Fe 2+ dosage, reaction time and pH value on chemical oxygen demand (COD) degradation efficiency are explored. The improved particle swarm optimization (IPSO) algorithm is used to optimize the back propagation (BP) neural network, and a prediction model of COD degradation is established based on IPSO‐BP neural network. H 2 O 2 dosage, Fe 2+ dosage, reaction time and pH value are selected as the main influencing factors of the COD degradation, and 30 groups of experimental data are selected to train the IPSO‐BP neural network. The results predicted by the trained IPSO‐BP neural network on 10 groups of test data are compared with the actual values, and the results predicted by BP model and genetic algorithm‐BP (GA‐BP) model are compared. The IPSO‐BP model has the highest fitting accuracy. The root mean square error (RMSE), RMSE coefficient of variation (CV‐RMSE), and coefficient of determination ( R 2 ) were used to evaluate the prediction performance of the model. The indicators of the IPSO‐BP model were significantly better than those of the GA‐BP model and the BP model, indicating that IPSO‐BP model had better generalization ability and could predict COD concentration more effectively. The optimal conditions for Fenton oxidation obtained from IPSO‐BP model are as follows: the dosage of H 2 O 2 is 2000 mg/L, the dosage of Fe 2+ is 1200 mg/L, the pH value is 2.8, the reaction time is 75 min, and the COD removal rate is 80.125%, which is consistent with the experimental results. Through gas chromatography‐mass spectrometry (GC‐MS) analysis, most organic compounds in kitchen anaerobic wastewater are oxidized and decomposed, indicating that the IPSO‐BP model has good predictive quality.

Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements.

For air quality index prediction problem, this paper puts forward the optimization based on improved PSO - BP algorithm, the method using particle swarm weights and threshold of BP neural network is optimized, and the update each particle’s position and speed of the weight of adaptive adjustment strategy, to balance the global optimization and local optimization ability, and it has been verified by experiment that the improved PSO - BP neural network model is compared with PSO -BP and GA - BP and BP on prediction accuracy improved.