8results about How to "Reduce data collection time" patented technology

The invention discloses a direct visual field testing device, which relates to the technical field of testing and comprises a video auxiliary device and a marker device, the video auxiliary device realizes three-dimensional positioning and real-time video acquisition of preset eye spots through a height adjusting mechanism, an angle adjusting mechanism and a holder adjusting mechanism; the marker device realizes accurate adjustment and stable positioning of a marker in a test area through a height adjusting rod, a knuckle bearing and laser-assisted positioning; the direct visual field testing device is suitable for accurate measurement of the visual field range of a driver, can efficiently collect and record testing data, has the advantages of being convenient to operate, high in adaptability, high in precision and the like, and is widely applied to the fields of vehicle design, driving training, safety research and the like.

The invention belongs to but not limited to the technical field of animal husbandry statistics, and particularly relates to an alpine meadow yak number calculation method and system based on an unmanned aerial vehicle image, and the method comprises the steps: S1, employing an unmanned aerial vehicle to shoot an orthoimage of a sample plot, and carrying out the statistics of the yak number; s2, splicing the shot images by using Pix4D software, screening a proper sample plot, and respectively counting the quantity of wet cow dung and the quantity of dry cow dung in a visual interpretation mode; and S3, carrying out linear fitting on the obtained data by using Origin 2024b, so as to obtain three relation curves. According to the method for estimating the number of the yaks through statistics of the number of the cow dung in the unmanned aerial vehicle image, the workload of data collection is greatly reduced, and the limitation that the number of the yaks cannot be obtained when the yaks are not grazed in the grassland is avoided. According to the invention, the data acquisition frequency of experimenters is effectively reduced, and the research cost is reduced.

The present invention relates to the technical field of operation and maintenance management, and specifically relates to an integrated operation and maintenance management system and method based on AIOps; the method includes: collecting multi-source IT operation and maintenance data and completing standardized preprocessing to form operation and maintenance data input in a unified format; classifying and storing the preprocessed data using a hybrid storage architecture, and implementing data synchronization through a cross-database synchronization tool to break data silos; performing intelligent analysis on the data based on the TensorFlow framework to generate fault diagnosis results and performance trend prediction data; generating operation and maintenance decisions according to the analysis results, and performing automatic repair, work order allocation or resource scheduling operations; outputting operation and maintenance management results, presenting data and decision execution situations through a visualization interface, and providing report generation and management configuration functions; through a full-process automated data processing and intelligent operation and maintenance decision-making mechanism with multi-module collaboration, the technical problems of slow fault response, weak data integration, low accuracy of risk prediction and low operation and maintenance efficiency in the traditional operation and maintenance mode are solved.

The invention discloses a tower grounding resistance data acquisition device and an anti-lightning analysis method, and relates to the technical field of tower grounding resistance measurement. The device for carrying out tower grounding resistance data acquisition by using the unmanned aerial vehicle comprises a grounding resistance sensor which is positioned below a tower and is connected with a ground line body, a mainframe box which is in wired connection with the grounding resistance sensor, and a data receiving device which is in data transmission connection with the mainframe box and is mounted on the unmanned aerial vehicle, the ground resistance sensor collects the ground resistance of the ground line body and transmits the ground resistance data to the mainframe box. When the unmanned aerial vehicle carries out polling between towers, the host box transmits grounding resistance data to the data receiving device through the communication module in the host box, so that a background manager obtains the grounding resistance data of each tower through the data receiving device, the working efficiency of grounding resistance detection is improved, and the large-scale detection requirement is met.

The present invention belongs to the technical field of structured light three-dimensional measurement, and specifically is a single-frame three-dimensional measurement method based on structured light and deep learning. The present invention performs fringe image analysis through a deep neural network obtained by unsupervised learning pre-training and supervised learning fine-tuning to achieve three-dimensional measurement; wherein: the unsupervised learning pre-training uses a single-frame fringe image collected by a structured light system as input, and the supervised learning uses the collected four-frequency 12-step phase-shifted fringe images as input, and the deep neural network used is composed of 3 U-Net modules, which are respectively used for phase calculation, unwrapping and tuning. The present invention completes single-frame three-dimensional measurement through a deep neural network, can be used to measure dynamic scenes, and has high measurement accuracy; the combination of unsupervised and supervised learning reduces the number of collected fringe images and improves the training efficiency of the deep learning method.

This invention discloses a bolt loosening detection method and system based on UAVs and multimodal analysis, belonging to the field of structural health monitoring technology. It includes acquiring structural data of towers to construct a three-dimensional virtual mapping and performing simulation to dynamically generate inspection tracks for the UAV; the UAV simultaneously collects high-definition video and sound field data of the bolts based on the tracks, forming a multimodal data stream; calculations are performed by fusing visual and acoustic features to output quantitative diagnostic results; an active verification process is initiated for abnormal or poorly quality results to obtain supplementary data for correction and form a final diagnosis; a detection report is generated based on the final diagnostic data, constructing a full lifecycle health record. This invention adopts a technical solution combining three-dimensional task planning, multimodal fusion diagnosis, active verification and correction, and full lifecycle management, which can improve detection accuracy and efficiency while achieving dynamic assessment and trend prediction of the health status of bolt components.

The invention discloses an intelligent inspection task planning method with autonomous learning and dynamic strategy optimization, and relates to the technical field of industrial automatic monitoring. The method comprises the following steps: firstly, constructing a heterogeneous association graph comprising equipment nodes, environment nodes, space adjacent edges and process coupling edges; identifying an initial risk source by using an abnormal feature extraction algorithm, and executing risk potential energy propagation calculation to generate a global dynamic risk distribution map; then, by taking maximization of high-risk area information gain as a target, generating a reference inspection path containing non-uniform sampling parameters; and during inspection execution, based on the deviation degree of actually measured and predicted data, event-driven local re-planning is triggered in real time, and a temporary verification path for the abnormal associated neighborhood is generated. According to the invention, through quantification of process and space risk propagation, pre-judgment of potential hidden dangers and real-time response of sudden anomalies are realized, and the inspection efficiency and safety in a complex industrial scene are effectively improved.

The invention discloses a method and a terminal for detecting foot pressure prevention. A single-point laser is installed obliquely downward in front of a robot, and single-point lasers are respectively installed downward in a position close to the left and right rear wheels, a position close to the left and right rear wheels, and a position far from the left and right rear wheels. Human feet can be detected by the single-point laser installed obliquely downward and the single-point laser far from the left and right wheels. Since the single-point laser for detecting human feet is at a certain distance from the left and right wheels, it is convenient to brake in time after the human feet are detected. In order to ensure foot pressure prevention, the single-point laser close to the left and right wheels only detects the height of obstacles, which can reduce the time of data collection and data processing, and ensure that the robot can stop in time when encountering a position close to the height of the human feet and far from the left and right wheels and the human feet are not detected, so as to avoid pressing the human feet that suddenly extend under the vehicle and avoid the occurrence of foot pressure accidents.