5results about How to "Accurate automatic identification" patented technology

The invention belongs to the technical field of underwater acoustic communication reconnaissance and artificial intelligence edge computing, and discloses a sea area signal reconnaissance method and device based on deep learning. According to the method, a constructed lightweight LeNet-5 hierarchical convolutional neural network model is embedded and deployed into an STM32H743 microcontroller through an X-Cube-AI tool to process underwater acoustic signals, automatic identification and classified modulation are performed on underwater signals of active sonar signals and non-cooperative communication signals in real time, and the underwater signals are used for analysis of recovered sea area information. According to the method, deep-level features can be automatically learned from the time-frequency graph, better robustness is achieved for noise and a complex marine environment, and the recognition accuracy is higher.

The application relates to a bridge modal parameter automatic identification method and system considering multi-channel information, which comprises the following steps: S1, generating a stability diagram by directly analyzing multi-channel monitoring signals by using COV-SSI, automatically extracting stable axes by combining a DBSCAN clustering algorithm, and realizing automatic identification of modal frequencies; S2, decomposing multi-channel monitoring data by using MvFIF to generate an intrinsic modal function (IMF) group with modal alignment characteristics; S3, calculating the instantaneous frequency and bandwidth of the IMF in step S2 by using HHT, screening out an IMF component containing a target modal frequency, and performing linear reconstruction; and S4, taking the multi-channel monitoring data after the reconstruction in step S3 as system input of the COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating modal damping ratios by using eigenvalue decomposition; the method can consider the space-time correlation between multi-channel data, realize synchronous decomposition of multi-channel monitoring data, and realize automatic and accurate identification of structural modal parameters under different noise levels.

The application discloses a control method and system of a charging module and a charging system. The method comprises the following steps: collecting the operating parameters of the charging module; calculating the comprehensive discriminant value of the coupling multi-factor composite working condition according to the input voltage, output current, module temperature and battery state of charge; adjusting the power distribution ratio of the battery and the energy storage unit according to the comprehensive discriminant value; judging whether the charging module needs to switch the working condition according to the input voltage and output current and outputting the corresponding control signal to control the charging module; solving the first switching frequency with the maximum efficiency of the charging module as the target, adjusting the first switching frequency according to the current working condition to obtain the optimal switching frequency; performing adaptive PI adjustment on the current loop according to the output current to output the optimal switching duty ratio; and controlling the charging module according to the optimal switching frequency and the optimal switching duty ratio. The application enables the charging module to maintain high efficiency and stable operation within a wide input, wide output and wide load range.

This invention relates to the field of vehicle obstacle avoidance technology, and provides a vehicle obstacle avoidance method and apparatus. The vehicle obstacle avoidance method includes the following steps: acquiring laser radar data within a preset angle range ahead of the vehicle's direction of travel using a laser radar installed on the vehicle; generating a corresponding equivalent image based on the laser radar data; processing the equivalent image to identify obstacle information; and providing obstacle avoidance prompts or action control based on the obstacle information. This invention can accurately and automatically identify obstacles in the vehicle's direction of travel in low-light conditions and can take corresponding obstacle avoidance prompts or actions, thereby improving vehicle safety and saving labor costs.

This invention discloses a vehicle violation monitoring method based on edge and cloud collaboration, comprising: real-time acquisition of video streams of a designated area via cameras; real-time reception of the video streams by an edge computing device, performing target detection on vehicles within the streams, and determining whether a preset event has been triggered based on the vehicle's status; if a preset event is triggered, capturing the corresponding video slice and uploading it to a central server; the central server performing semantic analysis on the video slices to generate a structured sequence of vehicle behavior events, matching the sequence of vehicle behavior events with preset violation rules; if a match is successful, determining that a violation has occurred, and generating an alarm message. This achieves a leap from "target detection" to "behavior understanding," facilitating traffic management.