267 results about "Vision algorithms" patented technology

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 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 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 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 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.

The invention discloses a cognitive load assessment method based on fusion of behavior characteristics and heart rate variability in a classroom video. According to the method, face and behavior videos of students in a real classroom are collected, behavior characteristics such as eye movement tracks, sitting postures and facial expressions of the students are extracted through a computer vision algorithm, heart rate variability indexes are predicted in combination with a remote photoplethysmography technology, and a time sequence characteristic sequence is formed. And then, a time sequence deep learning model is adopted to carry out joint modeling on the multi-modal time sequence characteristics, and the cognitive load scale level is taken as a supervision signal to construct a classification model to realize cognitive load level prediction. The method has the advantages of non-contact, automation, high adaptability and the like, and can be applied to personalized teaching monitoring and intelligent teaching feedback.

The invention relates to the technical field of machine vision, in particular to a red date defect identification method and system based on machine vision, and the method comprises the steps: obtaining original images of a plurality of red dates at a plurality of overturning angles, enabling the plurality of red dates to move along with a transmission belt and rotate along with a rotating mechanism on the transmission belt, and obtaining the original images of the plurality of red dates; the original images are collected through a camera located above the conveying belt, and the original images of the multiple overturning angles are used for covering all the surfaces of the red dates; and then carrying out mixed identification on the original images of the plurality of overturning angles in sequence to obtain a defect identification result of the images in the image group. The red dates are conveyed and automatically turned over through the conveying belt and the rotating mechanism, and the camera shoots original images of multiple turning angles so as to cover the whole surfaces of the red dates. And then performing hybrid recognition on the original image, and performing automatic recognition on red date defects by using a traditional vision algorithm and an AI algorithm. Compared with manual screening, the problems of low screening precision, low efficiency and the like are solved.

The invention provides an AI vision algorithm processing method based on a vehicle-mounted intelligent terminal and electronic equipment. The AI vision algorithm processing method comprises the steps of collecting driving scene image data in real time based on the vehicle-mounted intelligent terminal; performing multi-scale feature enhancement on the driving scene image data to generate an enhanced feature map with scene priority annotations; inputting the enhanced feature map into an AI vision algorithm based on a lightweight Transform inference model, dynamically adjusting the number of attention heads and a feature channel compression ratio, and outputting an identification result including a driving environment key target; and performing cross validation based on an identification result and heterogeneous data of the vehicle-mounted sensor, and performing real-time closed-loop correction on a feature extraction weight and a reasoning threshold value of an AI visual algorithm to complete dynamic visual perception of the driving scene. According to the method, the defects that the driving scene cannot be dynamically adapted, the identification precision cannot be balanced and the closed-loop optimization of the visual algorithm cannot be realized at present are overcome.

The invention relates to the technical field of vehicle-mounted electronic information and computer man-machine interaction, and discloses a vehicle camera APP display mode and system, and the mode comprises the steps: responding to a starting instruction, and recognizing a currently activated video input source as an in-vehicle or out-vehicle camera; dynamically loading a differentiated interaction strategy according to the type of the input source, if the mode is an in-vehicle mode, loading a man image beautifying control and a visual algorithm, and if the mode is an out-vehicle mode, shielding a beautifying function and releasing the computing power of the algorithm; a shooting trigger instruction is generated by monitoring a physical or visual logic signal, and an echo countdown state is entered after an image is collected; and if the user operation is not detected after the countdown is finished, automatically executing data archiving. The system comprises a display initialization module, a strategy configuration module, an acquisition control module and an interactive filing module. According to the invention, the potential safety hazard of manually operating the screen in the driving process is solved, and the non-inductive safe storage of the image data is realized.

The invention provides a high-voltage electrical equipment live-line cleaning machine system based on an unmanned aerial vehicle, and belongs to the field of electrical equipment maintenance. Comprising a cleaning nozzle, a cleaning agent storage box and a conveying device, and the conveying device conveys a cleaning agent in the cleaning agent storage box to the cleaning nozzle; the visual positioning module is integrated with a high-definition camera and an AI visual algorithm and is used for identifying a dirty area of the high-voltage electrical equipment; and the path planning module is internally provided with a discharge area identification algorithm and path obstacle avoidance logic, and automatically plans to avoid the equipment discharge area. According to the invention, an environment-friendly cleaning module, a visual positioning module and a path planning module are integrated to construct a complete operation closed loop. The environment-friendly cleaning module accurately conveys and distributes a cleaning agent through a conveying device, the visual positioning module is matched, the dirty area of the equipment can be rapidly recognized and locked, compared with traditional manual work, the limitation of complex scene operation is broken through, the coverage range and positioning precision of cleaning operation are improved, and the dirt removing task is efficiently completed.

The invention belongs to the technical field of industrial visual inspection, and discloses a PLC visual inspection linkage control method and system based on AI image recognition. The method comprises the steps of automatically adjusting exposure parameters of a camera based on an evaluation result of real-time brightness and contrast of an industrial product image to be detected, and obtaining a corrected industrial product image; extracting an illumination invariant feature from the corrected industrial product image, and generating an illumination invariant feature vector; inputting the original image of the same industrial product image to be detected and the corresponding illumination invariant feature vector into a pre-trained feature optimization model, and outputting an optimized illumination invariant feature vector; inputting the industrial product images shot under different exposure conditions into a pre-trained neural network model for fusion to obtain a shared feature vector; the problem of missing detection and false detection caused by illumination change in high-speed production of a traditional visual algorithm is solved.

The invention discloses a method for generating an intelligent autonomous navigation map of an agricultural robot, and relates to the technical field of agricultural robots, and the method comprises the steps: carrying out the image segmentation of a satellite map of a target farmland through an image segmentation model, so as to automatically recognize a farmland region, and rapidly extracting a farmland boundary contour through the edge curve fitting. Then a navigation path can be automatically generated based on the farmland boundary contour in combination with the basic attribute parameters of the target farmland, and an intelligent label is automatically added to obtain an intelligent autonomous navigation map for indicating the driving path and the working mode of the agricultural robot. The system realizes end-to-end automatic conversion from a satellite image to an intelligent autonomous navigation map, can adapt to diversified farmland environments with different topographic features, different crop types and different planting modes based on a deep learning computer vision algorithm, can meet the precision requirement of agricultural operation while ensuring the automation efficiency, and has a wide application prospect. And the method has strong generalization ability.

The invention relates to a grabbing parameter calculation method based on garbage three-dimensional modeling, and the method comprises the following steps: S1, based on a multi-view three-dimensional reconstruction technology, employing a multi-view stereoscopic vision algorithm, collecting object multi-angle RGB-D data through a 3D camera, employing a Poisson surface reconstruction algorithm to construct a complete three-dimensional model, and generating a garbage three-dimensional point cloud model, s2, on the basis of the three-dimensional point cloud model in the S1, a Z-buffer depth detection algorithm is adopted, the lowest point of the model in the Z-axis direction is recognized through an axial extremum search method, space reference coordinates are determined in combination with a ground projection method, and reference positioning data are generated. The method has the advantages that the multi-angle RGB-D data is fused through the multi-view three-dimensional reconstruction technology, the high-precision three-dimensional model is constructed by adopting the Poisson surface reconstruction algorithm, the problem of geometric feature deficiency caused by traditional single-view modeling is effectively solved, the spatial representation capability of the special-shaped object is improved, the lowest point of the model is recognized based on the Z-buffer depth detection algorithm, and the accuracy of the model is improved. And space reference coordinates are established in combination with a ground projection method, and the physical rationality of grabbing and positioning is enhanced.

The invention relates to a riverway drowning monitoring method based on a lossless down-sampling network and multi-modal semantic disambiguation, and belongs to the field of computer vision, and the method comprises the steps: collecting heterogeneous data, carrying out the enhancement preprocessing, inputting the data into a target detection network, detecting the geometric detection confidence, and carrying out the initial screening of targets through the geometric detection confidence; for the suspected target subjected to preliminary screening, dense light smoothness of the suspected target in a continuous time window is calculated, an optical flow motion entropy is extracted according to a dense optical flow field, and a key frame image and a front-back time sequence feature map are optimized according to the motion entropy; the cloud end utilizes a visual large model to execute feature-semantic mapping based on a cross-model attention mechanism, and a textualized semantic result is generated; and carrying out Bayesian weighted fusion on the geometric detection confidence detected by the target detection network and a textualized semantic result output by the visual large model, and finally triggering a terminal sound-light alarm. According to the method, the bottleneck that a traditional vision algorithm lacks behavior semantic cognition is broken through, and the false alarm rate of false drowning is remarkably reduced.

The invention discloses an old people health monitoring and behavior recognition system and method based on multi-modal perception, and the system comprises a monitoring recognition system, a user interface module, a data set module, an algorithm module, a real-time monitoring module, a human body posture detection module, a human body tracking module, a fall detection module, a trust area frame selection module, an alarm module, and a health evaluation module. The output end of the monitoring identification system is in one-way connection with the input ends of the user interface module and the data set module, the output end of the user interface module is in one-way connection with the input end of the data set module, and the output end of the data set module is in one-way connection with the input ends of the algorithm module and the real-time monitoring module. The output end of the real-time monitoring module is in one-way connection with the input end of the human body posture detection module. According to machine vision, an advanced machine vision algorithm is adopted, and a self-training model can accurately judge the human body posture.

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an incremental Kd-Tree laser-inertial fusion positioning method based on improvement, the method provided by the invention adopts a practical filtering scheme, the operand is small, the real-time performance is high, and the requirement of a laser radar on an illumination condition is lower than that of a camera, so that the method is very practical. The noise is much lower than that of visual measurement, so that the precision of the method is higher than that of a visual algorithm; according to the method provided by the invention, two kinds of data are fused in a deep level, so that the method is not liable to fail during strenuous exercise, and a GPU is not needed during operation. The filtering algorithm adopts an iterative error state Kalman filtering algorithm, so that the precision of the system is not very low under nonlinear motion, and the map management efficiency is also improved by the improved incremental Kd-Tree algorithm in the aspect of map management. In conclusion, aiming at a special scene without a GNSS signal, the robot can efficiently position the robot and construct a surrounding environment map only by depending on the sensor of the robot.

The invention discloses an industrial part circular hole burr detection method and device based on an image vision algorithm, and the method achieves the precise detection and positioning of a circular hole in an industrial part through the steps of to-be-detected image preprocessing, preliminary circle contour fitting, secondary fitting and positioning correction, standard circle positioning, burr detection and the like. The method not only improves the accuracy and stability of round hole detection, but also effectively avoids the interference of a reflective area on the surface of the workpiece, provides a reliable geometric data basis for the positioning of the round hole of the workpiece, and has remarkable practical and visual effect advantages. Meanwhile, the method is high in generalization, can be applied to detection of workpiece circular holes in different shapes, is high in detection efficiency, can be applied to automatic detection, and remarkably improves the detection efficiency.

The invention discloses a vehicle seat adjusting device and method, electronic equipment and a storage medium, and relates to the field of vehicle seat control, and the device comprises an image collection module which is used for collecting a whole body image of a person in a vehicle; the visual algorithm processing module is used for receiving the whole-body image and judging the height of a person in the vehicle through a visual algorithm, and the visual algorithm comprises the steps that the whole-body image is preprocessed; recognizing a human body region in the image through a human body detection algorithm, wherein the human body detection algorithm is a convolutional neural network model based on deep learning; the visual algorithm processing module is used for extracting feature points in a human body area, the ergonomic data storage module stores an optimal seat angle database corresponding to different heights, and the seat adjusting control module is used for calling the corresponding seat angle in the database according to height data output by the visual algorithm processing module and adjusting the seat angle according to the height data. And the electric adjusting mechanism is controlled to execute an adjusting action.

The invention discloses a photovoltaic power generation prediction error correction device and method, and relates to the technical field of photovoltaic power generation. Sky vision data, irradiance data, photovoltaic power station power data and environment data are collected, and time synchronization is performed on the collected data; inputting the simulated irradiance field distribution diagram and the cloud cluster motion vector prediction data into a trained space-time diagram convolutional network used for outputting power prediction errors of the photovoltaic power station; and outputting a photovoltaic power generation power prediction error by the space-time diagram convolutional network. According to the invention, the three-dimensional space coordinates and contours of the cloud cluster can be accurately obtained through the combination of the multi-view high-speed camera array and the stereoscopic vision algorithm; according to the method, a dense optical flow algorithm and an LSTM network are combined, accurate prediction of a future cloud cluster movement track is realized, optical thickness and light transmittance are inversed through a mapping relation between a cloud cluster gray value and actually measured irradiance, cloud cluster modeling is upgraded from abstract data representation to concrete physical entity, and high-precision physical model support is provided for subsequent photon transport simulation.