358 results about "Vision algorithms" patented technology

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention discloses an unmanned aerial vehicle autonomous homeward voyage charging system based on wireless charging, and relates to the field of flight control and energy management, and the system comprises the steps: monitoring the flight path, remaining power and environmental parameters of an unmanned aerial vehicle, predicting the remaining power required for completing a task, and triggering a homeward voyage condition signal and the current position information of the unmanned aerial vehicle; generating an optimal homeward flight path of the nearest wireless charging platform based on the homeward flight triggering condition signal and the current position information of the unmanned aerial vehicle; obtaining an optimal charging point based on the generated return path, and sending a deployment instruction to the mobile charging platform; the mobile charging platform advances according to the deployment instruction path, the unmanned aerial vehicle returns according to the optimal return path, the course is corrected through inertial navigation and a visual algorithm, and the landing point of the unmanned aerial vehicle is obtained. Fine adjustment and positioning of the flight attitude are formed by using the visual identification and attitude control module, and a closed-loop flight control mechanism based on image processing and attitude adjustment amount calculation is realized.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses an automatic dovetail welding seam grinding control method based on robot visual positioning, and relates to the technical field of visual positioning. The method comprises the steps that after welding is completed, a dovetail welding seam image is collected and converted into a three-dimensional coordinate through a vision algorithm, and a three-dimensional point cloud is generated; smooth interpolation is performed on the three-dimensional point cloud through a B spline curve method to obtain a parameterized curve, and a control point set is optimized through a genetic algorithm to generate a global optimal polishing path; the polishing robot executes a task according to a path, a tail end sensor collects a real-time path and calculates a deviation value with a global path, and when the deviation value exceeds a preset threshold value, inverse kinematics is triggered to solve and correct the path; the hardness of the dovetail weld is measured through laser-induced breakdown spectroscopy, a comprehensive hardness value is obtained in combination with a matrix hardness database, meanwhile, a visual sensor collects the surface state, and polishing process parameters are dynamically adjusted according to the surface state; after the task is completed, the welding seam angle and flatness are detected. According to the method, the optimal path is generated through visual positioning, and automatic grinding of the dovetail welding seam is achieved.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention discloses an unmanned aerial vehicle intelligent inspection system based on AI vision and a detection switch cabinet, and relates to the technical field of unmanned aerial vehicle intelligent inspection, the unmanned aerial vehicle intelligent inspection system comprises an unmanned aerial vehicle inspection platform, and the unmanned aerial vehicle inspection platform is in communication connection with the following modules: an unmanned aerial vehicle end, which is used for collecting and preprocessing video stream data of an inspection area; extracting a key frame from the preprocessed video stream data; and the AI visual analysis module is used for analyzing the extracted key frame by using an AI visual algorithm and identifying key information and abnormal fragments in the key frame. According to the invention, through the AI vision algorithm based on the convolutional neural network model, abnormal features can be automatically learned and identified, the abnormal types and specific conditions can be rapidly determined through deep analysis of the key frames, accurate positioning of abnormal segments and comparison with the preset abnormal feature database, compared with manual detection, the accuracy is greatly improved, and the detection efficiency is improved. Tiny abnormal changes can be found in time, and potential faults can be warned in advance.

The invention discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention provides a marine target real-time positioning and tracking system and method based on multi-source data fusion, and belongs to the field of target positioning and tracking. The sensor portion collects raw data. The data acquisition and time synchronization module is responsible for receiving and aligning data of each sensor; the multi-stage coordinate system registration and transformation module unifies all observation data to a world coordinate system by using IMU data and preset calibration parameters; the AI visual target detection module processes the video stream to identify a target; the adaptive federated Kalman filtering fusion module receives each path of processed data and outputs optimal target state estimation; and the dynamic task and resource scheduling module performs optimal allocation on system resources according to the current tracking state to form closed-loop control. Through deep fusion of high-precision RTK positioning data, laser ranging data, photoelectric pod attitude information and an AI vision algorithm, the positioning precision, tracking robustness and system real-time performance of a sea target under a complex dynamic sea condition are improved.

The invention discloses a multi-sensor fusion unmanned vehicle detection method suitable for a complex environment. The method comprises the following steps: obtaining road surface data through a laser radar and an infrared thermal imaging camera; detecting rain and snow weather conditions in real time, and calculating weather environment factors based on rainfall data and snowfall data; dynamically adjusting data weight coefficients of the laser radar and the infrared thermal imaging camera based on the weather environment factors; using a computer vision algorithm to identify an obstacle area, and extracting the distance and height of the obstacle according to the corresponding data weight coefficient; and the obstacle type is analyzed, the minimum safe distance or the minimum torque between the unmanned vehicle and the obstacle is calculated, and a driving decision is provided for the unmanned vehicle. According to the invention, the self-adaptive capability and safety of the unmanned vehicle in a complex environment are improved, the accident risk caused by sudden obstacles is effectively reduced through an intelligent early warning and intervention mechanism, and a solid safety foundation is laid for the wide application of an unmanned driving technology.

The invention discloses an automatic welding method of a reinforcement cage welding robot, and relates to the technical field of welding robots. Based on an improved ResNet-50 multi-mode visual system, the welding point identification error is reduced to 0.1 mm level, and by matching with an SE (3) space coordinate conversion algorithm, the positioning precision reaches + / -0.03 mm (3 sigma); the visual algorithm fusing the deformable convolution and the coordinate attention mechanism still keeps the recognition rate improvement of 23.6% under the illumination fluctuation of 200-1500 lux; the improved RRT algorithm is combined with the self-adaptive B-spline interpolation, so that the motion smoothness of the six-degree-of-freedom mechanical arm is improved by 400%, and the welding defect caused by path sudden change is effectively avoided; motion control of the mechanical arm is optimized through the friction compensation model, and the welding stability is remarkably improved.

The invention relates to the technical field of garbage classification detection, and discloses a garbage classification supervision system based on multi-sensor fusion, which comprises a multi-view camera, an ultrasonic sensor, an edge calculation layer and an execution layer, the edge calculation layer comprises a data acquisition module, a human body feature detection module, a garbage classification detection module and a garbage overflow detection module; the garbage classification detection module performs garbage classification detection and identification on video image data in target data based on an improved visual Transform algorithm and a front-back time sequence comparison algorithm, so that model updating is automatically started under low-confidence alert, disastrous forgetting is effectively prevented, and meanwhile, the real-time performance and high precision of the system are kept.

The invention provides a vehicle lamp entertainment interaction method and device and a computer readable storage medium, and the method comprises the steps: obtaining a live-action image of an interaction region projected by a projection headlamp in front of a vehicle, and detecting human body key points in the behavior performance of an interaction object participating in a game indicated by the live-action image in the interaction region, and analyzing and identifying through a visual algorithm, and determining the interaction behavior of the interaction object. The interaction behavior is converted into an operation instruction in a game, and the projection headlamp is controlled to project a picture indicated by the operation instruction in the interaction area, so that a user is allowed to directly control a vehicle lamp entertainment system through gestures, and the driving experience of the user is greatly enriched and improved.

The present application relates to a geographic mosaicking method and apparatus for video images, and a computer device and a storage medium. The method comprises: acquiring real-time live streaming images of a real-world scene that are collected by a plurality of unmanned aerial vehicles, and GNSS information of the plurality of unmanned aerial vehicles; using a visual SLAM algorithm to perform input frame tracking on the real-time live streaming images, and performing pose estimation on successfully tracked input frames by combining visual trajectories and the GNSS information, so as to generate georeferenced camera poses; using the camera poses and a surface reconstruction algorithm to perform densification processing on the input frames, so as to generate a depth map of the real-world scene, and mapping the depth map into dense three-dimensional point clouds, so as to construct a three-dimensional surface model of the real-world scene; and using the camera pose and the three-dimensional surface model to perform orthorectification on the input frames, and integrating the orthorectified input frames into a global mosaicked map, so as to generate a global image of the real-world scene. By means of the embodiments of the present application, a real-time picture of a real-world scene can be quickly acquired, thereby providing robust support for a rapid emergency response in a real-world scene.

The invention relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention discloses a mechanical arm simulation system supporting industrial quality inspection, which relates to the technical field of industrial automation and comprises a multi-physics field simulation engine module, a digital twin interface module, a defect sample library module and a cooperative control module. According to the method, traditional entity mechanical arm debugging is replaced by rigid body dynamics simulation based on a physical engine, loss and production line shutdown caused by repeated trial and error of equipment are avoided, rapid construction of the digital twin environment of the production line is achieved by means of an efficient CAD model conversion process and a lightweight loading technology, and high-fidelity optical simulation is achieved through a parameterized defect model library and high-fidelity optical simulation. Various common industrial defects and complex optical effects are covered, the modeling bottleneck of a quality inspection scene is broken through, real-time closed-loop interaction of visual inspection and motion control is realized by using thread separation and an efficient communication technology, and the recognition robustness of a visual algorithm to tiny defects is guaranteed; the reliability of algorithm verification is improved through the quantitative output of algorithm performance indexes and the comprehensive test of extreme working conditions by the simulation system.

This disclosure describes an interactive augmented reality system for improving surgeon's view and context awareness during laparoscopic and video assisted surgeries. Instead of purely relying on computer vision algorithms for image registration between pre-operation (or intra-operation) images / models and later intra-operation scope images, the system can implement an interactive mechanism where surgeons may provide supervised information in initial calibration phase of the augmented reality function, thus achieving high accuracy in image registration. Besides the initialization phase before operation starts, interaction between surgeon and the system can also happens during the surgery. Specifically, patient tissue might move or deform during surgery, caused by for example cutting. The augmented reality system can re-calibrate during surgery when image registration accuracy deteriorates, by seeking additional supervised labeling from surgeons. The augmented reality system can improve surgeon's view during surgery, by utilizing surgeon's guidance sporadically to achieve high image registration accuracy.

The invention relates to a full-automatic slit lamp and an intelligent diagnosis method thereof, and aims to improve the ophthalmic examination efficiency and accuracy. The slit lamp is composed of a slit lamp device, an automatic adjusting device and a man-machine interaction device. The automatic adjusting device comprises a calculation module and an adjusting module, and the adjusting module accurately drives the slit lamp device through gear transmission and automatically adjusts operation parameters according to a preset mode. The display module of the man-machine interaction device can display the working mode, the eye image data and the interaction information, and the prompt module guides the examinee to cooperate with the examination. The camera element of the slit lamp device converts the light into image data, and the communication device transmits the data to the cloud. And during fault diagnosis, the calculation element monitors image data in real time, marks abnormities through a visual algorithm, and enters a diagnosis mode. After different modes are operated for multiple times, the calculation unit analyzes imaging result abnormity, accurately locates and displays fault components, realizes automatic fault diagnosis, and reduces maintenance cost.

The invention relates to the field of pipeline three-dimensional reconstruction and intelligent detection, in particular to a pipeline three-dimensional reconstruction and intelligent detection method based on panoramic stereoscopic vision. According to the technical scheme, a panoramic camera and a radar sensor are used for collecting panoramic images and depth data in a pipeline; carrying out denoising, distortion correction and illumination compensation processing on the collected panoramic image, and carrying out image optimization; stable feature points in the panoramic image are extracted, and feature point matching is carried out; a multi-view stereoscopic vision algorithm and a synchronous positioning and mapping technology are combined, and pose estimation and three-dimensional point cloud generation are carried out by using a panoramic image and depth data; optimizing the generated point cloud data, and reconstructing a smooth and continuous three-dimensional surface; and based on the processed three-dimensional data, intelligently detecting cracks and corrosion defects in the pipeline by using a convolutional neural network, and automatically generating defect types, positions and repair suggestions. The method is suitable for pipeline detection.

The invention relates to the technical field of transformer intelligent carrying scheme design, in particular to an AGV-based transformer intelligent carrying method and system and electronic equipment. The method comprises the steps that when an AGV is controlled to advance along a preset route, an image is acquired through an industrial camera, and a parallel bearing beam is recognized; resolving the pose of the bearing beam based on a three-dimensional vision algorithm, and fitting the pose of a bearing plane through a least square method; the AGV is controlled to accurately place the transformer according to the pose data, the AGV is automatically withdrawn along a Bezier curve path, and meanwhile, the safety distance between the AGV and the bearing beam is monitored in real time by fusing multi-sensor data. Full-process automation of transformer carrying is achieved, the problems that a traditional mode is low in positioning precision and large in potential safety hazard are solved, and the installation efficiency and reliability of power equipment are remarkably improved.

The invention discloses a millimeter-wave radar and vision fused vehicle three-dimensional detection method, which comprises the following steps of: performing space-time fusion on millimeter-wave radar data and a camera image to obtain corresponding pixel position information of an actual position of a vehicle in the image, and calculating vehicle three-dimensional frame information by utilizing a vision algorithm; and positioning to a target position according to a coordinate conversion algorithm, judging a target through a Rayleigh fusion algorithm, and drawing a three-dimensional mark at the position of the target to realize three-dimensional vehicle detection. The long-distance detection advantage of the millimeter wave radar and the fine visual perception capability of the camera are fully combined, the vehicle three-dimensional detection is stably, efficiently and accurately completed in a complex traffic environment, and the accuracy of an intelligent traffic perception algorithm is greatly improved.

The invention provides a shopping order identification method and system based on vision and gravity sensing. The method comprises the following steps: detecting commodity information in a video stream of a shopping area of a vending machine and position information of hand key points; according to the position information of the key points of the hand at different moments, calculating the angular velocity of hand motion, judging whether a hand rotation action on the commodity exists or not, calculating finger distance change, the relative motion velocity of the two hands and the relative motion direction of the two hands, and judging whether a tearing action on a commodity packaging bag exists or not; if the hand rotation action or the tearing action exists, and the weight data on the goods selling shelf is reduced, it is confirmed that the shopping abnormal behavior occurs. According to the method, the visual data and the weight data of the vending machine shopping area are combined, abnormal behaviors in the shopping process are detected and recognized, the problems that a pure visual algorithm cannot solve the commodity number problem and a pure weight algorithm cannot recognize the commodity category are solved, and the shopping order recognition precision is improved.

The invention discloses a wild boar recognition and monitoring system and method based on an advanced vision algorithm, and belongs to the technical field of image recognition, and the system comprises an information collection device which is configured to collect an initial target image, and carries out the preprocessing and enhancement of the initial target image to obtain a second target image; the feature extraction module is configured to perform feature extraction on the second target image, optimize semantic expression and generate a multi-scale feature map; the lightweight detection head module comprises a classification task branch and a bounding box regression task branch; and the evaluation module is configured to optimize the detection result through a non-maximum value algorithm, and evaluate the model performance by taking the average precision mean value, the recall rate and the precision rate as evaluation indexes. According to the method, the rapid identification capability of the wild boars is remarkably improved, the method can be widely applied to real-time monitoring of wild boar invasion, farmland protection and emergency response scenes, and an efficient solution is provided for guaranteeing the safety of residents and preventing the farmland from being damaged.

The invention provides an emergency call system, method and device based on AI vision and a storage medium, and relates to the technical field of active safety of intelligent network connection automobiles. The system comprises a camera module, a DMS module, an OMS module, an AI vision algorithm module, an AI vision application module and an eCall application module which work cooperatively. Aiming at the three defects that a traditional emergency call system depends on collision triggering, data dimensions are deficient and active protection is absent, DMS and OMS are innovatively and deeply integrated with the emergency call system, a multi-modal fusion decision mechanism is realized in combination with a layered AI visual architecture, and through deep cooperation of camera shooting, DMS, OMS, AI visual algorithm, AI visual application and an eCall application module, the multi-modal fusion decision mechanism of the emergency call system is realized. A full-link intelligent safety closed loop of sensing, decision making, control and rescue is constructed, early recognition, active intervention and accurate rescue of sudden diseases of drivers, serious fatigue disability and emergency physiological events of passengers are achieved, and meanwhile the rescue quality of collision accidents is improved.

The invention discloses a sewage treatment plant water level safety detection method based on an AI visual algorithm, and the method comprises the following steps: S1, reading a video frame, carrying out the single-frame water level recognition, carrying out the real-time data cleaning optimization according to a single-frame water level recognition result, and inhibiting the interference of liquid surface impurities and steam through multi-frame fusion and self-adaptive preprocessing; s2, generating sewage pool area positioning by using a mask, and extracting an effective area ROI in the pool; s3, water body features are recognized through HSV color filtering, liquid level segmentation is carried out, and the water level height is calculated; s4, the size relation between the current water level height and the warning threshold value and the size relation between the interval between the current time and the last alarm and the anti-shake interval are judged, if the composite judgment logic is met at the same time, the step S5 is executed, and otherwise, image labeling is normal; and S5, performing multi-mode alarm by using vision and voice broadcast. The water level identification accuracy of the sewage pool in a complex scene can be improved, and the stability of water level abnormity judgment is improved.

The invention relates to the technical field of visual identification, in particular to a tracked robot navigation system based on visual identification, which comprises a visual sensor unit used for collecting environment images in real time; the inertial measurement unit is used for acquiring posture and acceleration data of the robot; the main control processor is used for executing a visual SLAM algorithm, generating an environment map and performing real-time positioning; the track driving mechanism controls the robot to move; environment images are collected in real time through the visual sensor unit, the posture and acceleration data of the robot are obtained in combination with the inertial measurement unit, a visual SLAM algorithm is executed through the main control processor, high-precision environment modeling and real-time positioning are achieved, and finally an optimized path planning instruction is executed through the track driving mechanism. The system can sense the motion state and environmental characteristics of the robot in real time, so that the track driving mechanism dynamically adjusts the driving force, and the problem of slipping or vehicle sinking caused by topographic changes is avoided.

The invention discloses a container truck alignment guide system based on multi-source sensor fusion, which comprises a laser detection module, a visual detection module and a laser and visual fusion processing module, and is characterized in that the laser detection module is responsible for collecting and processing laser point cloud data and tracking a container truck; recognizing and outputting the truck head position information of the container truck and the position and height information of the container and the bracket through a clustering algorithm; the visual detection module is used for collecting images in real time and inputting the images into different visual algorithm models through preprocessing, and the positions, types and related characteristics of container trucks in a working area are recognized. The laser and vision fusion processing module is used for parallel processing of laser and vision data and supports operation in single vision, single laser and fusion modes; through integrating operation results of the laser detection module and the visual detection module, an adaptive fusion strategy is determined according to business requirements, and final container truck alignment guide information is generated. According to the invention, accurate identification and rapid alignment of various transportation devices can be realized.

The present invention discloses a pavement crack extraction method based on a neural radiation field and a semantic segmentation network, and specifically relates to the field of road engineering technology. The pavement image data of a research area is acquired by an unmanned aerial vehicle and preprocessed to form a required image data set. The preprocessed image data set is three-dimensionally reconstructed by combining a motion recovery structure multi-view stereo vision algorithm and a neural radiation field algorithm to obtain a pavement point cloud model and a point cloud data set. A semantic segmentation network is constructed and the point cloud data set is trained. The pavement crack information is extracted and calculated according to the semantic segmentation result. Compared with the prior art, the detection cost is reduced, the real-time detection requirements are met, and the current status of point cloud processing automation, poor expressiveness and insufficient application is significantly improved. In addition, the road scene with cracks can be intuitively and accurately characterized, thereby providing an underlying platform and data support for the digital representation of pavement information.

The invention discloses an experiment teaching system based on augmented reality and machine vision, which relates to the field of laboratory teaching systems and comprises an augmented reality visualization module, a machine vision recognition module, a task flow control module, an error judgment and prompt feedback module, a teaching data acquisition and evaluation module and a teacher control terminal. The experiment steps and the three-dimensional model of the equipment are overlaid in an actual scene in real time through the augmented reality technology, student operation images are collected through a machine vision algorithm, a state vector is generated, matching degree calculation is conducted on the state vector and a standard vector, misoperation is recognized, and visual prompt is given; the recognition robustness is improved by adopting a moving average mechanism, and the teaching process is dynamically controlled through a task graph; and the scoring module comprehensively evaluates student performance based on indexes such as a misoperation rate, completion time and a prompt response rate, updates a personalized knowledge graph and realizes a teaching feedback closed loop. The visual, normative and intelligent level of experiment teaching is improved, and good practicability and popularization value are achieved.

The invention relates to an intelligent fruit tree planting monitoring method and system, and belongs to the technical field of intelligent agriculture. According to the scheme, a multi-source sensor network is deployed to collect soil, crop and environment data, a salinity-moisture collaborative stress decision-making model is constructed, and parameters such as soil salinity, canopy temperature and weather forecast are deeply fused; dynamically generating a non-fixed precise irrigation instruction; meanwhile, a low-visibility robust vision algorithm is integrated, and it is ensured that fruits and diseases and pests can still be reliably recognized in severe weather; by establishing a collaborative scheduling mechanism, efficient linkage of water and fertilizer management, plant protection operation and farming operation is realized. According to the method, the technical span from single threshold judgment to multi-factor intelligent decision is realized, the utilization efficiency of water resources and fertilizers is effectively improved, the production cost is remarkably reduced, and the mode of fruit tree planting is promoted to be converted from traditional experience management to data-driven intelligent management.