Drawing on the main characteristics of police professional education, this paper proposes an evaluation index system consisting of four subsystems and their index sets, in which society, police units, colleges, and students themselves jointly participate. The objective of this paper is to extract both subjective and objective evaluation information to develop an effective evaluation index system for professional education in police colleges. This paper uses a method based on improved information entropy to determine the weight of each index in the evaluation system, employs this weight calculation function to determine the subjective and objective weights of each index comprehensively, evaluates the professional education of police colleges using the improved fuzzy matter-element model, and verifies the training situation faced by graduates of the police university. The results show that combined with the evaluation system thus constructed, the entropy weight and fuzzy element model exhibit strong integrity and systematicity with respect to the evaluation of professional education in police colleges and that the evaluations produced by this approach are consistent with expert analysis. The evaluation thus becomes more comprehensive and objective and receives more social recognition.

The enhanced infrared shielding function of antimony tin oxide (ATO)-doped polymer-dispersed liquid crystal (PDLC) film enables its use for smart windows, because it can switch between transparent and scattered states, which can protect people’s privacy. When PDLC film is used for a building’s doors and windows or external walls, we hope that it can have a higher infrared shielding capability, in order to reduce the indoor temperature affected by solar irradiation, so as to reduce the energy consumption caused by refrigeration equipment. However, the infrared shielding capability of the existing PDLC is far from sufficient. In this work, modified ATO nanoparticles of different sizes were introduced into the PDLC system to improve its infrared shielding capability. It was found that when the ATO particle size is 20 nm and the doping content is 0.6 wt%, the modified PDLC sample provides optimal infrared shielding function while maintaining excellent electro-optical properties.

Addressing the prevalent challenges of inadequate detection accuracy and sluggish detection speed encountered by police patrol robots during security patrols, we propose an innovative algorithm based on the YOLOv8 model. Our method consists of substituting the backbone network of YOLOv8 with FasterNet. As a result, the model’s ability to identify accurately is enhanced, and its computational performance is improved. Additionally, the extraction of geographical data becomes more efficient. In addition, we introduce the BiFormer attention mechanism, incorporating dynamic sparse attention to significantly improve algorithm performance and computational efficiency. Furthermore, to bolster the regression performance of bounding boxes and enhance detection robustness, we utilize Wise-IoU as the loss function. Through experimentation across three perilous police scenarios—fighting, knife threats, and gun incidents—we demonstrate the efficacy of our proposed algorithm. The results indicate notable improvements over the original model, with enhancements of 2.42% and 5.83% in detection accuracy and speed for behavioral recognition of fighting, 2.87% and 4.67% for knife threat detection, and 3.01% and 4.91% for gun-related situation detection, respectively.