901 results about "Electric fence" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention provides an all-weather autonomous inspection method and system based on cluster task dynamic load balancing, and relates to the technical field of inspection monitoring, and the method comprises the steps: collecting the remaining electric quantity, the positioning position, the task queue length and the sensor health state of an unmanned aerial vehicle cluster in real time through an airborne terminal; fusing the meteorological data and the multi-source environment sensing data, and constructing a dynamic obstacle map and a meteorological influence model; dividing an inspection area into a plurality of sub-areas based on an electronic fence, dynamically adjusting the inspection priority of each sub-area, setting task weights of a water taking head and a raw water pipeline facility, and distributing inspection tasks according to the priorities; based on the remaining power of the unmanned aerial vehicle, the task priority, the current load and the meteorological data, a bipartite graph matching model of the unmanned aerial vehicle and the task is constructed, the matching weight is dynamically calculated, the optimal matching distribution of the unmanned aerial vehicle and the inspection task is completed, and the efficient inspection operation of the unmanned aerial vehicle cluster in the complex environment is realized.

The invention discloses an unmanned aerial vehicle aerial three-dimensional electronic fence construction method and system, and relates to the technical field of unmanned aerial vehicles, and the method comprises the following steps: dividing the region boundary of an electronic fence in a preset geographic range based on a geographic information system and a three-dimensional modeling technology, and calculating the range of the electronic fence; integrating the configured electronic fence to an unmanned aerial vehicle system, establishing a real-time communication and data exchange channel with the unmanned aerial vehicle, and receiving position information and flight state data fed back in the flight process of the unmanned aerial vehicle in real time; and monitoring the flight state of the unmanned aerial vehicle and the area boundary of the electronic fence in real time, and triggering an early warning mechanism when the unmanned aerial vehicle is found to have a boundary crossing behavior. According to the electronic fence system, the size, shape and position of the fence can be automatically adjusted according to the real-time flight state of the unmanned aerial vehicle and the environment change, the dynamic adjustment capability of the electronic fence is directly improved, and it is ensured that the unmanned aerial vehicle can always fly safely in the complex and changeable airspace environment.

The invention provides a safety early warning method and device for abnormal behavior trajectory analysis and a medium, and relates to the technical field of safety early warning, and the method comprises the steps: collecting behavior trajectory data of a target region through a multi-source sensing device, and carrying out the multi-dimensional feature extraction of a behavior trajectory data set; according to the stay frequency characteristics, carrying out abnormity determination on the track space-time distribution characteristics and the speed change characteristics, and constructing an abnormal behavior determination vector; performing wandering mode recognition based on the abnormal behavior judgment vector, determining an abnormal behavior level, and triggering a graded early warning mechanism according to the abnormal behavior level; and generating an abnormal behavior alarm signal according to the grading early warning mechanism, combining the abnormal behavior alarm signal with the electronic fence information of the target area to generate a safety early warning report, and pushing the report to a monitoring terminal. According to the invention, the technical problem of abnormal behavior recognition accuracy in the prior art can be solved, and the technical effect of improving the behavior judgment accuracy is achieved.

The embodiment of the invention discloses a level-to-level management strategy generation method and system for an electronic fence based on a 5G base station iron tower, and the method comprises the steps: collecting a multi-source heterogeneous monitoring data set covering an electronic fence warning region through sensor arrays disposed at different monitoring point positions of the 5G base station iron tower; the data comprise environment sensing data and target motion data; performing space-time correlation fusion processing on the data set to generate a basic analysis data set, determining a space relative relationship between the target motion data and the electronic fence boundary and potential influence of environment perception data on the motion behavior of the target object based on the basic analysis data set, and generating risk assessment information; a hierarchical management strategy is made according to the risk assessment information, strategy execution parameters are adjusted by continuously tracking a behavior evolution process of a target object in an electronic fence warning area to realize optimization iteration, and the accuracy and adaptability of electronic fence management are improved.

The invention discloses an anti-drowning early warning system of a self-adaptive dynamic virtual electronic fence, and aims to solve the problems that an existing anti-drowning early warning system cannot adapt to water level changes and early warning lags behind. The virtual electronic fence setting module is used for setting static and dynamic virtual electronic fences, identifying and analyzing personnel behaviors and controlling alarms; the data acquisition module is used for acquiring environment data information and video monitoring information; and the data storage module is used for storing data, alarm information, virtual electronic fence setting information and water level change information of the virtual electronic fence according to the water level change adjustment range. The system is based on real-time detection of a YOLOv8 model and is combined with a virtual electronic fence technology, has the characteristics of high-precision identification, low-delay response, field environment adaptability and the like, can effectively reduce the occurrence rate of drowning accidents, and is a set of intelligent, efficient and convenient-to-maintain system.

The invention discloses a personnel trajectory drawing method and system based on an AI algorithm. The method comprises the following steps: acquiring a positioning equipment signal and monitoring video data; performing dynamic Kalman filtering denoising processing on the positioning equipment signal to obtain first track data, and performing adaptive illumination compensation enhancement processing on the monitoring video data to obtain first video data; performing trajectory prediction compensation on the signal interruption segment based on an LSTM neural network; personnel motion posture features and scene semantic features are extracted; constructing a space-time correlation graph by adopting a graph convolutional neural network to obtain enhanced trajectory data; performing multi-scale trajectory reconstruction based on an improved Transform architecture to generate final personnel trajectory information; and outputting a visual trajectory report including trajectory precision evaluation, abnormal behavior marking and an electronic fence trigger event. According to the method, deep fusion of multi-source heterogeneous data and accurate reconstruction of the trajectory are realized, and continuity and reliability of personnel trajectory drawing in a complex scene are remarkably improved.