3633 results about "Vision sensor" patented technology

The invention discloses an intelligent robot spraying method based on a spraying system. Firstly, three-dimensional contour data of a workpiece are obtained through three-dimensional scanning equipment, a model is constructed, and key information is marked. And then according to the model, the workpiece material, the spraying requirement and the environment condition, a spraying path is planned through an intelligent algorithm, coating parameters are set, a robot executes spraying in sequence, and real-time monitoring is conducted through a high-precision position and a visual sensor in the process. After spraying is completed, the quality is detected through detection equipment, and when the requirements are not met, the computer system adjusts parameters according to deviation and directs the robot to conduct supplementary spraying or re-spraying. Whole-course data are recorded, and technological parameters and a path algorithm are optimized through statistical analysis. According to the method, high-precision, intelligent and efficient spraying is achieved, the method adapts to various workpiece and process requirements, the spraying quality and the production efficiency are improved, good adaptability and expandability are achieved, and intelligent development of industrial spraying is powerfully promoted.

The invention discloses an obstacle avoidance method and device for unmanned aerial vehicle power inspection autonomous flight based on a laser radar and a storage medium, and relates to the technical field of unmanned aerial vehicle obstacle avoidance, and the method comprises the steps: configuring the laser radar and a visual sensor on an unmanned aerial vehicle, and collecting the three-dimensional point cloud data and image data of a flight environment in real time; optimizing the point cloud data to generate a three-dimensional environment model; and based on the three-dimensional environment model, classifying the obstacles into static obstacles and dynamic obstacles, if the obstacles are static obstacles, identifying the types of the obstacles through a CNN model and performing positioning, and if the obstacles are dynamic obstacles, clustering motion tracks through a K-Medoids clustering method. According to the invention, static obstacles can be identified and positioned, and motion trail prediction can be carried out on dynamic obstacles such as flying birds through a dynamic obstacle prediction technology, so that a preliminary flight path is generated, surrounding obstacles are sensed and avoided in real time in the flight process, and it is ensured that the unmanned aerial vehicle can be smoothly executed in an electric power inspection task.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

The invention discloses an unhooking robot dynamic unhooking control method and system based on a feedback mechanism, and relates to the technical field of robot control, and the method comprises the steps: obtaining an unhooking robot, moving the unhooking robot to a preset unhooking position, and starting a visual sensor to collect unhooking image information and environment perception image information; edge detection and feature extraction are carried out on the unhooking image information, and hook space coordinate information is determined; performing feature abstraction on the environment perception image information to obtain a target unhooking path; and dynamic characteristic parameters and motion constraint information of the unhooking robot are obtained, unhooking analysis is carried out, unhooking control parameters are obtained, and dynamic unhooking control is carried out. The technical problems that in the prior art, an unhooking robot is low in unhooking efficiency and insufficient in control precision, and certain safety risks exist are solved, and the technical effects that the unhooking efficiency and the control precision of the unhooking robot are improved, and operation safety is guaranteed are achieved.

The invention discloses an environment modeling method and system for realizing intelligent security and protection, and relates to the technical field of intelligent security and protection, and the method comprises the steps: collecting a visual image and depth data of a monitoring region, and generating a monitoring scene feature matrix; performing hierarchical feature extraction on the monitoring scene feature matrix by using a deep convolutional neural network, and constructing a scene semantic feature map; projecting the scene semantic feature map to a three-dimensional monitoring space according to the depth data, and constructing a three-dimensional scene grid model; extracting spatial topological characteristics of each grid node in the three-dimensional scene grid model, and calculating association weights among the grid nodes; and constructing a scene dynamic knowledge graph based on the spatial topological features and the associated weights, and carrying out modeling analysis on abnormal events. According to the method, through cooperative collection of the multi-view vision sensor and the depth sensor, in combination with information entropy weighted standardization and principal component analysis, accurate generation of the monitoring scene feature matrix is realized, and the problems of information redundancy and feature coupling in traditional vision data processing are effectively solved.

The invention provides a magnetic core intelligent cutting parameter self-adaptive optimization system based on multi-mode perception, and relates to the technical field of data processing.The method comprises the steps that a multi-mode sensor module is integrated on magnetic core cutting equipment, and the module comprises a force sensor, a visual sensor and a temperature sensor; the acquisition units are respectively used for acquiring cutting force dynamic signals, cutting track image sequences and cutter temperature time sequence data in real time; magnetic core surface texture features and three-dimensional contour data are captured through a visual sensor, and an initial cutting parameter set is generated in combination with a magnetic core material type recognition result, associated parameters in a historical process database and preset process constraint conditions; and first workpiece trial cutting is executed based on the initial cutting parameter set, multi-modal data fusion collection is synchronously started, cutting force frequency domain feature vectors, a tool temperature change rate curve and cutting surface defect image features are obtained, and multi-modal data are obtained. According to the invention, multi-objective collaborative optimization of processing efficiency and energy consumption is realized.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a mechanical arm dynamic deviation correction method and system based on visual driving and a medium, and relates to the technical field of mechanical arm control. The method comprises the steps that when the tail end of a mechanical arm enters a preset machining space, an integrated 3D visual sensor is triggered to collect 3D point cloud of a workpiece to be machined; after pose recognition is carried out on the point cloud, the offset is recognized according to the teaching pose and the actual pose, and the initial offset is output; calling the multi-dimensional perception data, performing fusion correction, and outputting a correction offset; performing interference correction through an offset compensation model, and outputting a target offset; parameter adjustment and optimization are carried out according to the target offset, and a joint angle adjustment instruction is output; and performing correction closed-loop feedback according to the updated pose data. The technical problems of precision errors and low efficiency caused by deviation in the operation process of the mechanical arm are solved, and the technical effects that through dynamic deviation correction and multi-sensor data fusion, the operation precision and efficiency of the mechanical arm are improved, and stable operation in a complex environment is ensured are achieved.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention discloses a robot 3D visual guide grabbing method, which belongs to the field of robot application, and comprises the following steps of: comprehensively scanning a working environment by using a high-resolution 3D visual sensor to obtain point cloud data of a scene; carrying out object identification on the processed point cloud data by using a deep learning algorithm; a model capable of recognizing a target object is trained, the model can distinguish objects of different types, shapes, colors and textures, point cloud data are obtained through omnibearing scanning of a high-resolution 3D vision sensor, professional preprocessing is carried out, and the working environment can be accurately perceived. The deep learning algorithm is combined with multiple segmentation methods, objects can be accurately recognized and segmented under the complex background and shielding, meanwhile, geometric models of the objects and the environment are created through a specific algorithm, a comprehensive environment map is formed, an accurate basis is provided for follow-up operation, and object characteristics, gripper capacity and multiple grabbing strategies are comprehensively considered.

The invention discloses an unmanned aerial vehicle flight control system and method with precise positioning and autonomous obstacle avoidance, and particularly relates to the field of control, and the system comprises a positioning module, an obstacle detection module, a data processing and analysis module, an autonomous obstacle avoidance algorithm module, and a flight control module. Static and dynamic obstacles are detected in real time by integrating the laser radar, the millimeter wave radar, the visual sensor and the ultrasonic sensor, and an obstacle information report is generated through a data fusion algorithm based on machine learning; based on the processed environment data, the system uses deep reinforcement learning and a model prediction control algorithm to generate a safe flight path in real time, and an obstacle avoidance decision is optimized; the flight control part adopts a hierarchical control framework, and precise flight of the unmanned aerial vehicle is realized through trajectory tracking, attitude control and actuator control.

The invention discloses an automobile steering controller fault detection method and system, and relates to the technical field of automobile electronic control. The surface image acquisition module captures an image through a visual sensor, the quality is improved through the image preprocessing module, the convolutional neural network accurately recognizes appearance defects, and the three-dimensional contour detection and thermal imaging module is triggered to be linked to re-check an abnormal area; the three-dimensional contour module confirms patch offset or tombstone standing abnormity by using a laser scanning technology; the thermal imaging analysis module is matched with a machine learning technology to analyze welding spot temperature abnormity; the predictive fault diagnosis module predicts a defect risk by using deep learning and performs early warning in advance; and the collaborative decision optimization module integrates a multi-module data dynamic optimization detection strategy. Through integration of defect detection, re-checking and prediction, the quality detection precision, coverage and efficiency of the automobile steering controller are greatly improved, the high-quality standard and production stability of products are ensured, and the requirement of the automobile industry for efficient detection is met.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention discloses a mechanical arm operation method based on a multi-mode large visual language model, which comprises the following steps of: firstly, acquiring image data of a mechanical arm operation scene through a visual sensor, and acquiring a user instruction in combination with a voice recognition module or a text input module; and inputting the natural language instruction and the visual scene data into a multi-modal large visual language model for multi-modal task analysis. The model identifies the category, position and attribute of a target object in a scene, and generates a specific operation sequence in combination with an input instruction. And then, according to the operation sequence generated through analysis, the system directly drives the mechanical arm to execute corresponding tasks including specific actions such as grabbing, moving and placing of the target object, and in the task execution process, the mechanical arm sequentially completes the actions according to the operation sequence generated in advance. The problems that in the prior art, a mechanical arm is inaccurate in complex task instruction analysis, poor in operation flexibility and adaptability and poor in real-time response capacity in a dynamic environment are effectively solved.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention discloses a flight path planning method for unmanned aerial vehicle energy consumption optimization, and relates to the technical field of unmanned aerial vehicle energy consumption optimization, and the method comprises the following steps: 1, a multi-sensor fusion system is arranged on an unmanned aerial vehicle, and the multi-sensor fusion system comprises a visual sensor, an IMU inertial measurement unit and a GPS standby module; according to the flight path planning method for optimizing the energy consumption of the unmanned aerial vehicle, through a flight path optimization algorithm based on machine learning in combination with current position information, an original energy consumption optimization flight path and surrounding environment information, a connection path with the shortest connection path from a current offset position to the original flight path and the highest energy efficiency is calculated in real time; an improved path is obtained through optimization according to the current remaining electric quantity of the unmanned aerial vehicle and the target point distance, and it is ensured that the unmanned aerial vehicle can smoothly transition back to the original energy consumption optimized track and continue to fly in an energy-saving mode; the continuity and reliability of task execution of the unmanned aerial vehicle in a complex environment are improved, and the risk of task failure or great energy consumption increase caused by signal interruption is reduced.

The invention relates to the technical field of video monitoring, and discloses a camera cooperative monitoring method and system based on multi-modal perception, and the method comprises the steps: detecting an abnormal event signal in an environment through a non-visual sensor node, triggering the activation of a camera, and carrying out the visual capture of a target region; the camera performs relay tracking based on target feature matching to generate a continuous motion track; performing multi-view collaborative shielding on privacy sensitive information in the tracking target, generating desensitized monitoring data, and uploading the desensitized monitoring data; and optimizing the energy consumption of the camera in a non-event triggering period, and updating the behavior recognition model based on the desensitization data. According to the method, the contradiction between privacy protection and monitoring efficiency is solved, cross-regional model evolution and energy consumption reduction are realized, and the problems of response lag, data redundancy, privacy disclosure and high energy consumption of traditional monitoring are systematically avoided.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent defect detecting and repairing method and system for a vacuum isothermal forging based on multi-mode sensing data. The method comprises the following steps: respectively acquiring image data, heat distribution data and stress monitoring data of the vacuum isothermal forging through a visual sensor, a thermal sensor and a stress sensor, and preprocessing the data to generate a time-space aligned multi-modal data set; performing fusion processing on the multi-modal data set, extracting multi-dimensional features of defects through a deep learning network, and generating a defect feature sequence; optimizing a repair path planning model based on the defect feature sequence in combination with the stress distribution features, and generating a repair task sequence; and inputting the repairing task sequence into the intelligent repairing equipment to execute repairing operation. According to the method, the multi-mode sensing technology and the deep learning algorithm are combined, high-precision detection, classification and repair of the defects of the vacuum isothermal forging are achieved, the production efficiency and the product quality are remarkably improved, and the defective rate and the repair cost are reduced.

The invention relates to the field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle automatic flight control method and system for underground cable trench inspection, and the system comprises an environment sensing subsystem and an underground tunnel navigation subsystem. The visual sensor module obtains image data, the multi-sensor joint calibration module processes laser radar and camera data, the multi-layer fusion algorithm module fuses the calibration data, the underground tunnel navigation subsystem solves the problem of navigation obstacle avoidance, map construction of a tunnel environment and accurate estimation of the pose of an unmanned aerial vehicle are achieved, and an inspection route is planned and optimized. All the modules are integrated to achieve efficient and safe inspection of the underground cable trench, the inspection safety and efficiency are remarkably improved, the defects of a traditional manual inspection method are overcome, wide application prospects are achieved, safe operation of the underground cable can be effectively guaranteed through implementation of the system, and powerful support is provided for stability of an electric power system.

The invention relates to the technical field of synchronous positioning and map construction, and discloses a multi-sensor fusion SLAM method and system based on data quality comprehensive evaluation, and the method comprises the steps: carrying out the multi-dimensional quality evaluation of laser radar point cloud data and camera image data, and enabling the evaluation indexes to comprise the scene degradation degree, the view field integrity and the intensity information entropy of a point cloud, according to the point cloud data and the definition, self similarity and natural quality of the image, calculating the comprehensive weight of each index by adopting a subjective and objective weight fusion method, and dynamically selecting a laser radar pose, a visual sensor pose or an IMU integral pose increment as an initial value according to the comprehensive quality score of the point cloud data and the image data; and matching optimization of the point cloud and the map and global pose map optimization are carried out. According to the method, the sensor data quality is evaluated in real time, and the fusion strategy is dynamically adjusted, so that the positioning precision and robustness of the SLAM system in a complex environment are remarkably improved, and meanwhile, the calculation complexity is reduced.

The invention discloses a polishing track regulation and control method and system for automatic polishing of hub burrs, and relates to the technical field of electronic engineering.The method comprises the steps that a hub is scanned through visual inspection, the material of the hub is analyzed, a three-dimensional model of the hub is captured, and all burrs are positioned; the burr characteristics are analyzed through a DBSCAN algorithm, and a corresponding polishing path is generated; the robot arm starts grinding according to the grinding path, and data in the grinding process are collected in real time; burr detection is continuously conducted on the surface of the hub through the visual sensor, after burrs are found, the robot triggers an alarm mechanism, the polishing path is adjusted according to the specific position of the burrs, and the hub is polished again till detection is qualified. According to the method, the industrial camera is started, comprehensive hub surface data collection is achieved, the optimal polishing path is generated, optimization of path selection is achieved, instant response and processing of abnormal conditions are achieved through an alarm mechanism and path adjustment, and closed-loop control and high-quality guarantee of the polishing process are achieved through repeated verification and adjustment.

The invention relates to the field of grinding and polishing, in particular to an automobile spoiler mechanical arm grinding system and method.The automobile spoiler mechanical arm grinding system comprises a path planning module, a state monitoring module, a parameter compensation module and a waste collecting module which are in communication connection, and the path planning module is used for scanning a spoiler in real time through a visual sensor and dynamically adjusting a grinding path; the visual sensor is a 3D structured light sensor or a stereoscopic visual camera; the state monitoring module is used for monitoring the vibration state of the grinding head in real time through the vibration sensor and obtaining vibration abnormal data. The parameter compensation module is used for automatically adjusting grinding parameters according to the abnormal vibration data and adjusting a grinding head for grinding; the waste collecting module is used for collecting and treating dust and waste generated in the polishing process in a centralized mode. The polishing path can be precisely planned, the polishing quality is improved, the production efficiency is improved, the cost is reduced, and dust and waste materials are effectively treated.

An agricultural image analysis system comprising at least one vision sensor configured to view a seed trench; a storage module in communication with the at least one vision sensor; a processor in communication with the storage module, the processor executing at least one machine learning module for analysis of images from the at least one vision sensor. The system including at least one laser configured to emit a beam at an open seed trench and at least one vision sensor configured to view the open seed trench and the beam. The system including a thermal camera mounted to a row unit.

The invention belongs to the technical field of intelligent manufacturing and laser arc hybrid welding, and discloses a double-robot laser arc hybrid welding real-time regulation and control method based on digital twinning. According to the method, double robots are used, a laser-electric arc composite heat source digital twinborn model is constructed, a Gaussian distribution laser heat source and a double-ellipsoid electric arc heat source are integrated, and the dynamic behaviors (molten pool flowing and keyhole stability) of a molten pool and the generation probability of defects (air holes, cracks and the like) are predicted. And a visual sensor and an acoustic emission sensor on the two sides of the welding robot are used for capturing molten pool oscillation frequency and internal defect signals. And in combination with a reinforcement learning algorithm, welding path offset self-adaptive compensation and laser arc energy ratio adjustment are achieved, and closed-loop real-time regulation and control are formed by controlling the position of a welding gun of a welding robot and technological parameters. Based on a digital twinborn model, robot welding guns on the two sides can conduct synchronous welding on the two sides and can be independently adjusted while coupling is guaranteed according to the characteristics of specific materials, and high-quality welding is achieved.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.

The invention discloses a control method of an operation type intelligent foot-type robot applied to electric power inspection, and relates to the technical field of electric power inspection robots, and the method comprises the following steps: carrying out the omnibearing scanning and detection of a surrounding environment based on a multi-mode sensor carried by the foot-type robot, so as to collect environment data, and carrying out the fusion of the collected environment data, wherein the multi-mode sensor comprises a front stereoscopic vision sensor, a rear stereoscopic vision sensor and a three-dimensional solid laser radar. Through the carried multi-mode sensor, all-directional scanning and detection of electric power facilities and the autonomous navigation capability of the robot are realized, and the SLAM technology is combined, so that the robot can carry out effective path planning and autonomous inspection in an unknown environment, collision between the robot and an obstacle is avoided, the damage risk is reduced, and the safety of the robot is improved. And the dependence on manpower is reduced, the inspection range is expanded, and the continuity and integrity of inspection are ensured.

The invention discloses an external bias pixel structure of a light receiving stage circuit in a dynamic vision sensor pixel. The external bias pixel structure comprises a photodiode, a logarithmic tube, an amplifier tube, a switch tube and a bias tube, the bias tube, the switch tube and the amplifier tube are sequentially connected in series, a source electrode of the bias tube is connected with a power supply, a drain electrode of the bias tube is connected with a drain electrode of the switch tube, a source electrode of the switch tube is connected with a drain electrode of the amplifier tube, and a source electrode of the amplifier tube is grounded; the grid electrode of the bias tube is in signal connection with Vbias, the grid electrode of the switch tube is in signal connection with Vcpr, the source electrode of the switch tube and the drain electrode of the amplifier tube are connected with the grid electrode of the logarithmic tube, the drain electrode of the logarithmic tube is connected with a power supply, the source electrode of the logarithmic tube is connected with the negative electrode of the photodiode, and the positive electrode of the photodiode is grounded. And a grid electrode of the amplifier tube is also connected between the source electrode of the logarithmic tube and the negative electrode of the photodiode.