Patent application is one of the important ways to protect innovation achievements that have great commercial value for enterprises; it is the initial step for enterprises to set the business development track, as well as a powerful means to protect their core competitiveness. The emergence of a large amount of patent data makes the effective detection of patent data difficult, and patent infringement cases occur frequently. Manual measurement in patent detection is slow, costly, and subjective, and can only play an auxiliary role in measuring the validity of patents. Protecting the inventive achievements of patent holders and realizing more accurate and effective patent detection were the issues explored by academics. There are five main methods to measure patent similarity: clustering-based method, vector space model (VSM)-based method, subject–action–object (SAO) structure-based method, deep learning-based method, and patent structure-based method. To solve this problem, this paper proposes a calculation method to fuse the similarity of patent text and image. Firstly, the SAO structure extraction technique is used for the patent text to obtain the effective content of the text, and the SAO structure is compared for similarity; secondly, the patent image information is extracted and compared; finally, the patent similarity is obtained by fusing the two aspects of information. The feasibility and effectiveness of the scheme are proven by studying a large number of patent similarity cases in the field of mechanical structures.

This study aims to detect gait events using a smartphone combined with deep learning and evaluate the remote effects and clinical significance of this method in different elderly populations and patients with cerebral small vessel disease (CSVD). In total, 150 healthy individuals aged 20–70 years were asked to attach a smartphone to their thighs and walk six gait cycles at self-selected low, normal, and high speeds, using an insole pressure sensor as the reference standard for gait events. A deep learning model was then established using BiTCN-BiGRU-CrossAttention, and two models (TCN-GRU and BiTCN-BiGRU) were compared. In total, 48 elderly (25 healthy, 12 with mild cognitive impairment, 11 with Parkinson’s disease) participated in an online home assessment, completing single-task and cognitive dual-task walking. Overall, 35 CSVD patients participated in an offline clinical assessment, completing single-task, cognitive dual-task, and physical dual-task walking. The BiTCN-BiGRU-CrossAttention model had the lowest MAE for detecting gait events compared to the other models. All models had lower MAEs for detecting heel strikes than toe-offs, and the MAE for low and high walking was higher than for normal speed walking. There were significant differences (p < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Swing time, Stride length, and walking speed) between single-task and cognitive dual-task walking for all online elderly participants. CSVD patients showed significant differences (p < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Stride length, and walking speed) between single-task and cognitive dual-task and between single-task and physical dual-task walking.

In low-amplitude and low-frequency vibration environments, the energy harvesting efficiency of self-powered wireless sensor nodes is insufficient, limiting their long-term autonomous operation. To address this issue, a micro piezoelectric–electromagnetic hybrid energy harvester is designed, aiming to enhance energy capture efficiency through structural integration and parameter optimization. The study is conducted entirely through numerical simulations. A coaxial integrated architecture is adopted, combining a piezoelectric cantilever beam array with an electromagnetic induction module. The piezoelectric layer uses lead magnesium niobate–lead titanate (PMN-PT) solid solution material with a thickness of 0.2 mm. The electromagnetic module employs copper wire coils with a diameter of 0.08 mm, winding 1500–3000 turns, paired with N52-type neodymium–iron–boron (NdFeB) permanent magnets. To improve energy conversion efficiency, the optimization parameters include the length-to-thickness ratio of the cantilever beam, the mass of the tip mass, the number of coil turns, and the spacing of the permanent magnets. Each parameter is set at four levels for orthogonal experiments. A multi-physics coupling model is established using ANSYS Workbench 2023, covering structural dynamics, piezoelectric effects, and the electromagnetic induction module. The mesh size is set to 0.1 mm. The energy output characteristics are analyzed under vibration frequencies of 0.3–12 Hz and amplitudes of 0.2–1.0 mm. Simulation results show that the optimized hybrid harvester achieves 45% higher energy conversion efficiency than a single piezoelectric structure and 31% higher than a traditional separated hybrid structure within the 0.3–12 Hz low-frequency range. Under a 6 Hz frequency and 0.6 mm amplitude, the output power density reaches 3.5 mW/cm³, the peak open-circuit voltage is 4.1 V, and the peak short-circuit current is 1.3 mA. Under environmental conditions of 20–88% humidity and −15–65 °C temperature, the device maintains over 94% stability in energy output. After 1.2 million vibration cycles, structural integrity remains above 96%, and energy conversion efficiency decreases by no more than 5%. The proposed coaxial hybrid structure and multi-parameter orthogonal optimization method effectively enhance energy harvesting performance in low-amplitude, low-frequency environments. The simulation design parameters and analysis procedures provide a reference for the development of similar micro hybrid energy harvesters and support the performance optimization of self-powered wireless sensor nodes.