582 results about "Binocular single vision" patented technology

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

An ophthalmic lens configured to correct and / or treat at least one condition of the eye (e.g., presbyopia, myopia, hyperopia, astigmatism, binocular vision disorders, night vision image quality, and / or visual fatigue syndrome) comprising: a base power profile; and at least one pulse selected to modify the base power profile.

The invention provides an AI human shape recognition perception system and method based on binocular vision, and the method comprises the steps: carrying out the real-time collection through a binocular camera when a doorbell key is triggered, and carrying out the preprocessing of an original image collected in real time; performing coarse parallax estimation on the real-time image rectification to obtain a full-field coarse depth map, determining a human shape candidate region list by using the full-field coarse depth map and combining the heat source region of interest, and performing fine parallax estimation on the human shape candidate region list to obtain a fine depth patch; converting the corresponding fine depth patch into a three-dimensional point cloud set according to the pose information, and performing scale prior screening based on the corresponding three-dimensional point cloud set to obtain a plurality of human shape candidate reserved areas; and performing fusion identification according to the extracted multi-modal features to obtain a human shape identification result. According to the technical scheme provided by the invention, layered parallax estimation and three-dimensional scale prior screening can be carried out on the real-time image to realize high-reliability human shape recognition under the condition of low power consumption, so that the recognition reliability of a sensing system is improved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention discloses an AGV-six-degree-of-freedom mechanical arm operating system and method based on double-vision fusion, the system comprises a control system and a six-degree-of-freedom mechanical arm, and the control system comprises a master control unit, a slave control unit and a dynamic error compensation module; the method comprises the steps of AGV autonomous navigation and material transfer, cross-station accurate docking and dynamic error compensation, high-precision biological medicine operation under double-vision fusion guidance, and material recovery and circulation. According to the invention, through dual-vision dynamic fusion of eyes outside the hand and eyes on the hand, adaptive switching from global coarse positioning to local fine positioning is realized; due to the integrated design of the AGV and the six-degree-of-freedom mechanical arm, the operation flexibility is greatly improved; an extended Kalman filtering dynamic error compensation algorithm is innovatively adopted, so that the comprehensive error of the AGV, the mechanical arm and the visual system can be corrected in real time; and by combining with a double-layer distributed control architecture, the system realizes multi-task intelligent scheduling and modular collaboration.

The invention relates to a wall-climbing robot system for detecting a coating of a steel pipe truss structure and an application method, and the system comprises a self-adaptive magnetic adsorption mobile platform which can be adaptively and stably adsorbed on steel trusses with different pipe diameters to execute an inspection task; the robot platform is provided with an autonomous positioning navigation system composed of a laser radar, binocular vision and an airborne computer, and the position of the robot is positioned in the steel pipe truss without satellite signals through real-time mapping and a positioning algorithm of tight coupling of laser and vision. The front part of the robot is provided with an electrically foldable surrounding type array camera, so that full-surface coverage type shooting of a steel pipe component is realized, and real-time identification of coating apparent defects is executed in combination with an artificial intelligence disease identification algorithm in an airborne computer; and the lower part of the robot is provided with a telescopic elastic electromagnetic eddy current probe for detecting the thickness of the coating. The method has the advantages that the apparent diseases and the thicknesses of coatings of steel structures such as ceilings of high-speed rail stations and ceilings of large stadiums can be rapidly detected.

The invention discloses a visual analysis method and equipment for the trafficability of a grain transport vehicle tractor track and a storage medium. According to the method, images are collected through a vehicle-mounted binocular vision system, and multi-dimensional geometric attributes of the terrain and obstacles are automatically extracted through semantic segmentation, three-dimensional reconstruction and voxel modeling. And on the basis of the extracted multi-dimensional attributes, constructing a hierarchical evaluation system containing terrain and obstacle criteria. A local weight of each attribute index is determined by adopting an analytic hierarchy process, and a fuzzy membership degree vector of each index on different passage evaluation grades is constructed according to dynamic judgment parameters adaptively set with the current grain transport vehicle model number and the terrain, so that a membership degree matrix is formed. And finally, through hierarchical fuzzy comprehensive evaluation, calculating to obtain a comprehensive trafficability score. The accuracy, reliability and automation level of tractor track trafficability evaluation are improved.

The invention discloses a dynamic visual guidance method and system for cooperative assembly of multiple mechanical arms, and belongs to the technical field of industrial robot visual guidance, and the method comprises the steps: synchronously collecting an image sequence through a binocular visual system, synchronously recording the pose sequence of each mechanical arm through a robot controller, and obtaining an observation data set; obtaining a two-dimensional trajectory flow of vehicle body assembly area feature points and instrument body feature points through an optical flow tracking algorithm, and obtaining a hand-eye matrix correction through a nonlinear optimization algorithm; obtaining an updated hand-eye matrix and a local pixel compensation field through a spatial interpolation algorithm based on the re-projection error of each feature point; according to the method, self-adaptive compensation of flexible deformation of a vehicle body and dynamic reflection of an instrument is realized by adopting unsupervised clustering and spatial interpolation according to a re-projection error, and the precision of cooperative assembly of multiple mechanical arms and the self-adaptive capability of a system in a complex industrial environment are improved.

The invention relates to the technical field of computer vision, in particular to a three-dimensional reconstruction method and device based on optical polarization and stereoscopic vision principles. The three-dimensional reconstruction method sequentially comprises the steps of image preprocessing, polarization parameter extraction, binocular stereo matching, disparity map generation and optimization, normal vector correction, depth information fusion and three-dimensional point cloud reconstruction and modeling generation. A polarization normal vector field is constrained and corrected as a'skeleton ', and the azimuth angle pi ambiguity problem which puzzles polarization three-dimensional reconstruction for a long time is solved; high-frequency surface normal details contained in corrected polarization information are used as textures to enhance and fill up depth information loss of binocular vision in weak texture and repeated texture areas, the inherent limitation of a single sensing technology in a three-dimensional reconstruction task is overcome, and the three-dimensional reconstruction of the surface of an object, especially a diffuse reflection object, is realized. And high-precision and high-integrity three-dimensional shape recovery is realized.

The invention discloses a digital twin modeling method and system based on binocular vision and target dynamic tracking, and the method comprises the steps: constructing a binocular camera system, completing the calibration of internal parameters, distortion and external parameters, and building a coordinate system mapping relation; synchronously collecting and correcting target images, extracting coordinates of feature points, and then removing anomalies and distinguishing multiple targets; the parallax of the feature points is calculated, three-dimensional coordinates are obtained through triangulation, and optimized target point cloud is generated; solving target space poses through a PNP algorithm, and realizing single / multi-target dynamic tracking in combination with time sequence difference; carrying out anomaly detection and smoothing processing on the pose sequence, and constructing a weighted error model closed-loop optimization feature recognition parameter; transmitting pose data to realize virtual and real synchronous visualization of the workpiece; the workpiece space relation and the collision risk are monitored, an alarm is given, and data are stored for backtracking analysis. According to the method and the system provided by the invention, high-precision dynamic measurement, attitude tracking and assembly process digitization and visualization of a complex workpiece assembly process are realized.

The invention provides a road preview method and system based on a point cloud dynamic resolution allocation algorithm, and belongs to the field of automatic driving and intelligent traffic systems. The method comprises the following steps: acquiring an image pair acquired by a binocular camera, and preprocessing the image pair; generating a depth map of the scene based on the preprocessed image pair, and dividing the depth map into a plurality of regions of interest in a depth dimension; for each region of interest, calculating the importance weight of the region of interest based on the depth gradient and the motion vector; dynamically distributing the point cloud resolution of the region through a mapping function according to the depth value of the region of interest and the importance weight corresponding to the depth value; and based on the distributed point cloud resolution, generating point cloud data of dynamic resolution for road preview and path planning of the vehicle. According to the method, the problems of resource waste and poor real-time performance caused by full-scene uniform sampling of traditional binocular vision are solved, and the precision and efficiency of automatic driving road preview are remarkably improved.

The invention discloses a shock wave diffusion process testing method based on double cameras and digital schlieren. The method comprises the steps that two high-speed cameras are used for shooting digital schlieren images generated in the shock wave diffusion process; performing image splicing on the acquired images shot by the double cameras; the position of the shock wave is calculated through the spliced image, shock wave position data at different moments are obtained, and the speed of shock wave propagation in the x-axis direction and the y-axis direction is obtained. Based on binocular vision, a digital schlieren system is adopted to collect an air flow field information image containing a shock wave propagation process; splicing of double-camera images is achieved through an improved homography matrix method, the shock wave testing range is expanded, the whole shock wave propagation process is successfully captured, the shock wave diffusion testing accuracy is improved, and visual monitoring of wave types represented by shock waves in various transparent media can be achieved.

The invention discloses a concrete wall full-section deformation monitoring device and method, and relates to the technical field of engineering structure health monitoring, and the device comprises an integrated measurement unit, a passive stable characteristic target group and a processing unit. The integrated measuring unit is composed of an area array solid-state laser radar and a high-resolution binocular vision camera which are rigidly and fixedly connected, and is used for synchronously collecting three-dimensional point cloud and two-dimensional images of a wall and an environment. The target group is arranged on an independent stable object near the wall body; the processing unit identifies coordinates of the target group based on laser radar data, establishes a world coordinate system, calculates self pose drift of the measuring unit by periodically detecting position change of the target, and performs coordinate correction on a wall surface relative displacement field obtained by visual calculation by using the drift distance; and finally, outputting a full-section absolute deformation field under a world coordinate system. According to the invention, high-precision monitoring of full-field continuous deformation of the concrete wall is realized, and the reliability and comprehensiveness of structural health monitoring data are improved.

The invention provides a display screen dynamic control method and system, and relates to the technical field of data processing, the method comprises the following steps: constructing a space coordinate system comprising a plurality of entity nodes, the entity nodes comprising a display screen preset angular point, a user binocular vision center and a handheld interaction device; analyzing a relative pose relationship among the nodes by calculating a projection dot product of a space vector corresponding to each node; constructing a dynamic topology network based on the relative pose relationship, and synchronously collecting environment illumination intensity, user fixation point coordinates and user pose data; performing time sequence synchronization and Kalman filtering preprocessing on the acquired multi-modal data to obtain an initial feature set; and generating a spatial calibration coefficient according to the distribution characteristics of the dynamic topology network, and performing normalization compensation on the initial feature set through the calibration coefficient to obtain a calibrated multi-modal sensing feature vector. According to the invention, accurate and intelligent dynamic control is realized.

The invention provides a road three-dimensional lane line detection method and system based on binocular vision, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the online calibration of internal and external parameters of a camera and the time sequence alignment of the binocular image and IMU attitude data through collecting the forward binocular image and IMU attitude data of a vehicle; processing the binocular image after time sequence alignment, and determining a dense disparity map; generating a road point cloud based on the dense disparity map and camera parameters, and constructing an adaptive terrain model; constructing a depth enhanced BEV feature map according to the depth feature of the road point cloud and the texture feature of the binocular image, predicting candidate parameters of a three-dimensional lane line, and screening through various constraints; and then executing extended Kalman filtering and trajectory optimization to obtain three-dimensional lane line parameters. According to the invention, rapid and accurate detection of the three-dimensional lane line can be realized, the accuracy of three-dimensional lane line detection under a complex terrain is improved, and the deployment cost is reduced.

The invention discloses a magnetic sensor angle calibration system and method, relates to the technical field of magnetic navigation, attitude measurement and sensor calibration, accurately controls the position and attitude of a sensor through a six-degree-of-freedom transmission load device, and realizes real-time position feedback and closed-loop control in combination with a binocular vision system. It is ensured that the center position of the sensor on each sampling point is strictly kept unchanged, and multi-angle sampling is completed only through posture rotation. According to the system, a multi-sensor data fusion and optimization algorithm is adopted, the calibration precision and efficiency are remarkably improved, and the system is suitable for the fields of high-precision magnetic navigation, motion capture, virtual reality and the like. Through an automatic calibration process, the problems of low efficiency and poor consistency of a traditional method are solved, and reliable guarantee is provided for high-precision application of the magnetic sensor.

The invention discloses a binocular vision-based fish body quality non-contact estimation method, which comprises the following steps of: in a land-based facility circulating water high-density culture environment in which a fish body freely swims and is easy to block, firstly, obtaining a fish head local image through fish head region segmentation, and carrying out position alignment and scale normalization processing on the fish head local image; constructing a stable fish head geometric anchor point; then, the fish head anchor point is used as condition input, and a generative model is used for inferring and complementing the overall shape of the fish body so as to recover a continuous geometric structure of the fish body; on the basis, binocular stereo vision is combined to carry out stereo matching on key points of the head and the tail of the fish body, and the length of the fish body is obtained through triangulation; finally, based on the mapping relation between the body length and the quality, automatic estimation of the fish body quality is achieved, manual contact with the fish body is not needed, stable and automatic measurement of the fish body quality can be achieved in a complex breeding scene, and the method has the advantages of being high in adaptability, low in stress risk and high in application value.

The invention relates to an unmanned aerial vehicle towed body positioning attitude measurement method based on binocular vision positioning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, constructing a measurement system which comprises an unmanned aerial vehicle and a towed body connected with the unmanned aerial vehicle through a cable; the unmanned aerial vehicle carries a GNSS module, an IMU module and a binocular camera, and the upper surface of the towed body is provided with a coding pattern and carries a sonar sensor; s02, establishing a towed body coordinate system by taking the acoustic center of the sonar sensor as an original point, obtaining three-dimensional coordinates of feature points on the towed body in the towed body coordinate system, and generating a three-dimensional space model operation simulation model of the towed body; and S03, the binocular camera is calibrated. According to the invention, the problem of underwater topographic survey of a near-shore high-flow-velocity turbid water area is solved, and high-efficiency, low-cost and high-precision surveying and mapping operation is realized.

The invention discloses a vehicle distance measurement and calibration method based on laser radar and binocular vision fusion, relates to the technical field of measurement and calibration, and is used for solving the problem that the precision of a vehicle distance calibration result is reduced when a laser radar used for calibrating a highway vehicle distance snapshot system is under the conditions of rain, snow, wet and slippery or waterlogging road surfaces. Joint modeling is carried out on spatial distribution and echo energy characteristics of laser radar low-height point cloud, a point cloud echo coefficient and a low-height number proportion are introduced, a specular reflection state code is constructed, a self-calibration mechanism is adaptively triggered accordingly, and a specular reflection state is obtained. The discrete characteristic of a laser radar vehicle distance time sequence and the degradation trend of binocular vision parallax connectivity are subjected to quantitative analysis, the dominant source of ranging abnormity is judged through symbolic analysis, and an offset calibration or fusion correction strategy is selected according to the dominant source. Reliability and engineering adaptability of vehicle distance measurement under the wet and slippery road surface condition can be improved under the condition of not depending on an additional sensor.

The invention relates to a heading machine pose measurement and control method and system based on bidirectional vision. A system used by the method comprises a base station end measuring system in a roadway, a mobile end measuring system on a heading machine body and a motion control module. The method comprises the following steps: S1, establishing a base station end coordinate system; s2, establishing a mobile terminal coordinate system; s3, the base station end identifies and measures the three-dimensional position information of the feature light source of the mobile end under the coordinate system of the base station end through a binocular vision sensor; s4, the mobile terminal identifies and measures the vector information of the laser beam of the base station end through a visual sensor; s5, carrying out combined navigation calculation based on an EKF algorithm; and S6, performing pose control based on a combined navigation calculation result. According to the invention, a means of combining bidirectional vision measurement and inertia measurement is adopted, so that the cost requirement on a high-precision inertia measurement unit is reduced.

The invention belongs to the technical field of underwater vision measurement, and relates to an underwater binocular vision system refraction parameter self-calibration method, which comprises the following steps of: firstly, constructing a space equation containing a water body refractive index, a refraction interface normal vector, an interface distance and a light path coefficient based on a Snell law; constructing a generalized state vector including an environment parameter, a structure parameter and a path parameter; a system residual equation is established by using different-plane light intersection constraint of binocular vision, and nonlinear iterative least square solution is carried out by constructing a generalized Jacobian matrix so as to realize adaptive calibration of an underwater vision system; according to the method, on the premise that water body parameters do not need to be predicted and high-precision mechanical calibration is not needed, the water body refractive index and the system structure parameters are synchronously solved, self-adaptive calibration of the underwater vision system is achieved, and the three-dimensional measurement precision and robustness in the complex water environment are remarkably improved.

The invention relates to the technical field of container loading and unloading operation automation, in particular to a system and a method for identifying container numbers of railway track-mounted cranes based on binocular vision, comprising a binocular vision acquisition module, a data processing and control module, a distance measuring and triggering unit, a container number identification unit and a system interaction interface; the binocular vision acquisition module is electrically connected with the data processing and control module, the binocular vision acquisition module is used for synchronously acquiring a left image and a right image of a small surface of a container, and the data processing and control module is used for processing and controlling data acquired by the binocular vision acquisition module; the data processing and control module comprises a distance measuring and triggering unit and a container number recognition unit, and the distance measuring and triggering unit calculates the accurate distance between the lifting appliance and the small surface of the target container through a stereo matching algorithm according to a depth image given by the data processing and control module; and the container number identification unit carries out container number identification on the acquired image under a triggering condition.

The invention relates to a mobile welding robot weld path planning method fused with binocular vision, and belongs to the technical field of mobile welding robots. Comprising the following steps of S1, weld joint feature recognition, specifically, weld joint images are collected and processed through a global vision unit fixed to a movable chassis and a local vision unit fixed to the tail end of a mechanical arm, and weld joint feature information is recognized and extracted; s2, generating and fusing double-level tracks; s3, track execution and correction; and through a visual odometer of the movable chassis, the translation drift distance of the movable chassis in the linear direction is collected in real time, the controller controls the mechanical arm to move according to the track according to the final welding track, and weld joint welding is completed. The recognition robustness is improved through double vision, welding seam global positioning and local detail recognition are both considered, interference of welding fume and arc light is effectively resisted, and the feature point leak detection / false detection rate is reduced; the adaptive region adapts to multiple processes, the trajectory precision is optimized by dynamic weight, and the overall precision of trajectory planning is greatly improved.

The invention discloses a visual sparse observation and physical model fused flexible body deformation tracking method, which comprises the following steps of: firstly, constructing a flexible body co-rotation finite element model considering geometric nonlinearity, and establishing a low-dimensional flexibility coupling model for describing a mechanical transfer relationship between a virtual actuating source and a characteristic observation point by utilizing a static condensation technology; in the real-time tracking process, a binocular vision system is used for collecting images and resolving three-dimensional coordinates of sparse feature points in a real physical world; constructing and solving a reverse optimization problem based on the low-dimensional flexibility coupling model, and calculating a virtual correction force vector capable of minimizing an observation error; and applying the virtual correction force vector to the finite element model to drive the finite element model to deform, and updating a full-field geometric node position and a tangent stiffness matrix in real time. Through deep fusion of a physical model and sparse visual observation, the problems of visual occlusion and virtual-real model deviation in flexible body assembly are effectively solved, and online real-time full-field high-precision deformation state estimation is realized.

The invention discloses a pile foundation concrete accurate pouring control system and method based on multi-source sensing fusion. The pile foundation concrete accurate pouring control system comprises a sensing layer and a control layer, wherein the sensing layer comprises a phased array ultrasonic probe, a binocular vision sensor and a laser ranging array; the modeling layer comprises a three-dimensional hole shape reconstruction module and a BIM digital modeling engine; the data layer comprises a real-time monitoring database and a multi-source data fusion center; the decision-making layer comprises a concrete dynamic evaluation model and a BIM visual platform; the execution layer comprises a self-adaptive PID controller used for adjusting the concrete pumping rate according to the decision instruction, and a three-dimensional visualization terminal used for displaying the BIM model with the tolerance zone in real time. According to the method, a BIM model containing dynamic compensation is constructed through ultrasonic three-dimensional scanning, closed-loop control is achieved by fusing multi-source sensing data, and tolerance band penetration early warning and self-adaptive PID adjustment are combined. The hidden defect discovery rate is improved, and the comprehensive construction efficiency is improved.

This invention discloses a multi-vision method for measuring the dimensions of small workpieces based on a binocular vision system, belonging to the field of workpiece dimension measurement technology. Addressing the difficulty in achieving accurate three-dimensional dimension measurement using existing methods, the following solution is proposed, comprising the following steps: S1: Chessboard corner point extraction; S2: Calibration plate removal; S3: Plane fitting; S4: Vertex extraction; S5: Stereo matching algorithm; S6: Dimension calculation. The average absolute error of this invention at different angles is 0.33 mm, the total relative error is 0.90%, and the variance is less than 0.01 mm. 2 This enables relatively accurate measurement of the three-dimensional dimensions of small workpieces from multiple perspectives.