410 results about "Visual identification" patented technology

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, in particular to an AI-assisted unmanned aerial vehicle low-altitude flight obstacle avoidance method and system. The method comprises the following steps: carrying out real-time unmanned aerial vehicle low-altitude flight environment perception based on a multi-modal perception network, and carrying out static obstacle identification and obstacle radiation range analysis to obtain a plurality of static obstacle radiation paths; carrying out dangerous potential energy field modeling based on the plurality of static barrier radiation paths, carrying out dangerous environment distribution fitting, and constructing a three-dimensional dangerous potential energy map; performing multi-path flight rehearsal according to the three-dimensional danger potential energy map, performing optimal flight path evaluation, and extracting an optimal flight path; and performing unmanned aerial vehicle flight processing based on the optimal flight path, performing dynamic obstacle visual identification and operation path prediction, and constructing a plurality of obstacle movement prediction paths. According to the invention, the task execution efficiency and safety of the unmanned aerial vehicle are improved through rapid and accurate obstacle avoidance decision making of the unmanned aerial vehicle.

The invention relates to an intelligent visual identification technology-based cold solder joint microcrack intelligent discrimination method, which comprises the steps of collecting an initial RGB image of a to-be-detected welding spot, carrying out two-dimensional discrete cosine transform and inverse two-dimensional discrete cosine transform on the initial RGB image to obtain an enhanced image, and fusing the enhanced image with an R channel of the initial RGB image to obtain a fused image; forming a dual-channel feature map; calculating the phase consistency of the dual-channel feature map, and obtaining a suspected candidate region of the pseudo soldering microcrack through an adaptive threshold segmentation method; acquiring an RGB image sequence of a continuous time sequence of the welding spots, and performing anomaly detection to obtain an abnormal region set; and constructing a welding spot thermal diffusion model, and inputting the geometric parameters and the environmental parameters in the abnormal region set into the welding spot thermal diffusion model to obtain a final judgment result of the pseudo soldering microcracks. According to the method, through multi-dimensional feature fusion and continuous time sequence dynamic tracking, the detection precision of the pseudo soldering microcracks is remarkably improved, the false detection rate is reduced, and the final judgment result is more accurate.

The invention discloses a mobile phone silica gel shell appearance detection system based on an optical image sensor, and relates to the technical field of appearance defect detection and intelligent visual identification. An image acquisition module acquires a high-quality multi-channel image through multi-angle synchronous acquisition; the dynamic light field regulation and control module realizes self-adaptive regulation of a regional light source based on reflectivity perception and a gray gradient change rate; the image processing and defect identification module fuses edge features, texture changes and a deep learning model to realize pixel-level defect classification; the material identification and shielding module identifies a non-silica gel area through multi-dimensional spectral features and eliminates interference; a time sequence defect evolution analysis module models a defect evolution track; the trend prediction module is used for predicting the extension trend of potential cracks and fatigue areas; the control feedback module realizes intelligent judgment and response linked with the manufacturing system; the system can realize high-precision and high-robustness automatic defect detection and risk prediction, and is suitable for online quality monitoring of large-batch flexible products.

The embodiment of the invention provides a warehouse body intelligent unblocking method and system based on visual identification and air cannon linkage, and the method comprises the steps: collecting images in a warehouse in real time through a plurality of high-definition anti-explosion cameras disposed in the warehouse, carrying out the fusion processing of the collected images in the warehouse, and constructing a warehouse three-dimensional dynamic model; based on the warehouse three-dimensional dynamic model, key features are extracted by adopting a neural network; taking the extracted key features as input, analyzing associated parameters through a time sequence of a long-short-term memory network, establishing a congestion cause knowledge base, and labeling congestion types in a classified manner; constructing a congestion risk grading model based on the congestion cause library and a machine learning algorithm; and activating a corresponding air cannon based on the level corresponding to the three-level early warning and a visual positioning matching result, dynamically adjusting injection parameters, and executing unblocking operation according to an optimized time sequence. According to the embodiment of the invention, closed-loop intelligence from visual perception, reason modeling, artificial intelligence decision making to accurate execution is constructed.

The invention discloses a full-automatic intelligent port inspection robot system and method. The system mainly comprises an unmanned vehicle and an unmanned aerial vehicle which are autonomously coordinated. The unmanned vehicle platform comprises a vehicle body, an omni-directional driving wheel set arranged at the bottom of the vehicle body, and a multi-source sensing system, a 5G / Beidou dual-mode communication module, an emergency equipment cabin, an edge computing controller and an intelligent charging system which are arranged on the vehicle body, and real-time data interaction is achieved between the communication module and the unmanned aerial vehicle as well as between the communication module and the port central dispatching system. According to the invention, it is ensured that the robot system can complete core inspection tasks such as container three-dimensional scanning, container body state monitoring and storage yard path planning in an all-weather manner, automatic docking of the unmanned aerial vehicle and the unmanned vehicle with emergency equipment is realized, and a'sensing-decision-execution 'closed-loop system is formed; the functions of high-precision visual identification, multi-mode environment perception, dynamic obstacle avoidance planning, convenient replacement of unmanned aerial vehicle equipment, intelligent charging and the like are integrated, the port inspection efficiency can be remarkably improved, and the new-generation intelligent port equipment of the emergency response process can be optimized.

The invention relates to the technical field of visual identification, and provides an AI visual system for shaft part stain identification and cleaning equipment, an image acquisition module acquires a surface image of a shaft part, generates an image of the shaft part, captures the surface image of the shaft part in real time, and provides a data basis for subsequent stain identification; the stain identification module processes the shaft part image, identifies the local contrast of each shaft sub-block in the shaft part image to obtain a stain candidate area, detects whether a stain edge exists in each shaft sub-block in the stain candidate area, combines the shaft sub-blocks to obtain a stain area, and triggers an equipment signal; the real-time monitoring and identification capability enables the production process to be more efficient, and problems can be found and processed in time. Corresponding cleaning operation can be automatically executed according to equipment signals of the visual system, the shaft parts are cleaned, manual intervention is reduced through the automatic process, and the production efficiency and the product quality are improved.

The utility model discloses the field of agricultural machinery, and particularly relates to a spraying machine tool and an all-terrain spraying vehicle, the spraying machine tool comprises a hollow shell, a visual identification device and a spraying device are arranged on the two sides of the shell in the width direction respectively, the visual identification device comprises a supporting rod, the supporting rod extends in the height direction of the shell, and the spraying device is arranged on the supporting rod. A camera is arranged at one end of the supporting rod, the other opposite end of the supporting rod is connected with the shell, the spraying device comprises a water pipe, the water pipe extends in the height direction of the shell, a signal control switch valve is arranged on the water pipe, a spraying pipe is connected to the top of the water pipe, and a plurality of nozzles are arranged on the spraying pipe at intervals. According to the utility model, the visual identification device and the spraying device are arranged, and the visual identification device is used for judging whether spraying is carried out or not, so that the pertinence is stronger during spraying, the investment of labor cost can be reduced, the physical and psychological health of staff is greatly maintained, and the spraying device is simple in structure and convenient to popularize.

The invention belongs to the technical field of visual identification, and particularly discloses a thick plate weld groove orthogonal section contour three-dimensional point cloud reconstruction method and system. Comprising the following steps: dividing a three-dimensional point cloud of a groove contour into a plurality of parts according to geometric characteristics of a weld groove; receiving designation of a target part, extracting a point cloud subset at the left junction of the target part, and further fitting to obtain an edge line; confirming a point closest to the three-dimensional point cloud of the current frame on the edge line; calculating a tangent vector of the edge line at the nearest point as a normal vector of an orthogonal section; calculating the distance between each point in the three-dimensional point cloud of the current frame and the normal vector of the orthogonal section, and screening out all points which do not exceed a preset threshold value; and calculating a projection point of each screened point on the orthogonal section to jointly form a reconstructed thick plate weld groove orthogonal section contour. According to the method, the coordinate system orthogonally perpendicular to the welding seam in real time is constructed, the three-dimensional point cloud is projected to the orthogonally perpendicular plane, the orthogonal section contour of the groove of the welding seam is obtained, and the measurement error caused by the pose of the sensor is eliminated.

A mapping positioning method based on inertial navigation-laser-loopback-identification positioning tight coupling comprises an IMU odometer module, a laser radar odometer module, a loopback detection module and a visual identification positioning module, wherein the IMU odometer module and the laser radar odometer module are arranged on a mobile robot. According to the method, the basic pose of the mobile robot is estimated, the map is constructed, the global position information is generated according to detection, the corresponding weight is assigned according to the autonomously established evaluation standard, integration is carried out, the mapping function is finally completed, and high-precision positioning is achieved.

The invention discloses a robot automatic grabbing path planning method based on visual identification, and relates to the technical field of intelligent grabbing. The method comprises the following steps: acquiring multi-view visual data of a target scene, and generating a scene three-dimensional compact reconstruction model through a multi-modal image fusion algorithm; performing target detection and feature extraction on the model, and screening an optimal capture point in combination with a visual attention mechanism; constructing a dynamic environment obstacle probability map, updating an obstacle state through time sequence visual tracking, and quantifying an interference weight; an initial grabbing path is planned based on an improved fast expansion random tree algorithm, path smoothness constraints and robot joint movement limit parameters are introduced, and path nodes are optimized through a Bezier curve; visual servo feedback and path deviation prediction are fused, path parameters are corrected in real time, and a continuous movement track is generated. The method effectively adapts to the dynamic environment, gives consideration to path safety, smoothness and mechanical arm motion characteristics, and remarkably improves the grabbing success rate and operation reliability.

The invention relates to the technical field of robot welding, in particular to a mobile intelligent welding robot for seat production, which comprises an autonomous mobile chassis for autonomous navigation and obstacle avoidance in a seat production workshop; the multi-degree-of-freedom mechanical arm is used for achieving complex space track welding; the welding actuator is used for completing high-precision welding operation; the visual identification system is used for capturing the welding seam position in real time, detecting the welding quality and feeding back to the control system; the central controller is used for coordinating the operation of each module; through the steps, flexible movement of the robot in a production workshop is achieved through the autonomous moving chassis and the SLAM navigation technology, so that the utilization rate of equipment is increased, the occupied area of a production line is reduced, the multi-degree-of-freedom mechanical arm and the high-precision visual recognition system are combined, the complex welding seam track of the seat framework can be recognized in a self-adaptive mode, and the production efficiency is improved. And the pose deviation in the welding process is compensated in real time through force sense feedback, so that the consistency of the welding seam forming quality is ensured.

The invention provides a self-moving device and a recharging method thereof, the self-moving device comprises a visual module, and the method comprises the following steps: obtaining a to-be-processed image containing a charging station through the visual module; performing image processing on the to-be-processed image to obtain a target image so as to obtain a contour line of the charging station; controlling the self-moving equipment to be aligned with the charging station by using the contour line; and after the self-moving device is aligned with the charging station, the self-moving device is controlled to move towards the charging station, so that the self-moving device is in butt joint with a charging head of the charging station, and a recharging function is realized. According to the method and the device, the self-moving equipment can realize automatic recharging without a boundary guide line or a visual identifier, so that the reliability and the success rate of the recharging process are greatly improved, in addition, the special requirements on an external environment and the dependence on additional facilities are reduced, and the maintenance cost and the complexity of the system are reduced.

The invention provides a driving anti-slip optimization control method based on vehicle-road cooperation road fusion estimation, and the method comprises the following steps: S1, fusing vehicle-mounted and roadside visual recognition information, and obtaining a vehicle-road cooperation road state recognition result through employing a weighted voting method in combination with road invariant hypothesis and historical information assistance judgment; s2, adaptively adjusting parameters of a dynamic estimator by using a pavement state recognition result, establishing a sample set based on a pavement fusion estimation result, introducing a fuzzy clustering algorithm of particle swarm optimization to realize self-evolution optimization of the structure and the parameters of the estimator, and outputting a fused attachment coefficient estimation result; and S3, designing a slip rate and wheel acceleration combined control strategy to deal with potential slip in advance, and outputting a control torque result of a slip rate controller. Compared with the prior art, the method integrates visual perception and dynamic information to realize accurate estimation of the pavement state, supports advanced intervention of the slip rate controller before slip, and improves the driving stability of the whole vehicle.

The invention provides a peritoneal dialysis solution detection data intelligent analysis method based on visual identification, and the method comprises the steps: obtaining a to-be-identified test paper image, carrying out the test paper region segmentation and color correction of the test paper image, and carrying out the brightness equalization processing of a test paper detection region, and obtaining a processed test paper detection region; obtaining a standardized color feature vector of a test paper color development area from the test paper detection area; carrying out mapping matching on the standardized color feature vector and a standard colorimetric database to obtain a colorimetric matching value, carrying out color abnormality identification according to the colorimetric matching value, comparing the color abnormality with a preset abnormal value to obtain a test paper colorimetric analysis result, and carrying out self-adaptive correction on the test paper colorimetric analysis result with abnormality; and constructing a personalized colorimetric model according to the colorimetric matching value in combination with a historical colorimetric matching value, and performing health trend analysis and early warning.

The invention discloses a cabinet U-bit detection method based on dual-stage visual identification and perspective correction, and belongs to the technical field of computer vision and artificial intelligence. The method comprises the following steps: acquiring cabinet image data through a camera deployed in a machine room; performing irregular cabinet detection on the image by adopting a first-stage visual identification model, and outputting coordinates of four vertexes of the cabinet; performing image perspective correction according to the vertex coordinates to obtain a standard rectangular cabinet front image; inputting the corrected image into a second-stage visual identification model, identifying a U-bit occupation state and constructing a U-bit state matrix; calculating a cabinet vacancy rate based on the state matrix and displaying a detection result in a thermodynamic diagram form; and submitting a detection result of which the confidence coefficient is lower than a preset threshold value to manual auditing, and performing incremental training and optimization on the model. The method has the advantages of high precision, low cost, strong generalization, strong real-time performance, sustainable optimization and the like, and can be widely applied to data center cabinet U bit intelligent monitoring in a complex scene.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention discloses a highway hazardous chemical substance liquid leakage accident monitoring method and a highway hazardous chemical substance liquid leakage accident monitoring system based on computer vision. The method comprises the following steps: arranging and installing an identifier configured for hazardous chemical substance vehicle identification on a vehicle for loading and transporting hazardous chemical substances; the identifier comprises an optical identifier configured to be used for improving the visual identification degree of the dangerous goods vehicle and a digital identifier configured to realize encrypted storage and quick reading of vehicle information; on the basis of an existing road monitoring system, a lightweight identification model configured for detecting the states of an optical identifier and a digital identifier in real time is deployed and loaded in the existing road monitoring system; performing visual positioning on the road surface projection pattern through a camera, and calculating the real-time position of the vehicle by combining map coordinates so as to supplement the positioning blind area of the GPS in the tunnel mountain area; accident probability calculation is carried out based on the identification state to determine whether the vehicle is suspected to have an accident; and when the accident is judged to be suspected, triggering an accident response process. And the accident response efficiency is obviously improved.

The invention relates to the technical field of logistics site intelligent management, and provides a site digital twin management method based on IOT and AI visual identification technology, and the method comprises the steps: collecting multi-source heterogeneous data in a logistics site through a sensor, an RFID device, a GPS device and a camera which are disposed in the logistics site; creating a digital twinborn model corresponding to the logistics site; performing deep association analysis on the multi-source heterogeneous data to obtain potential association relationships and mode information among the multi-source heterogeneous data; according to the multi-source heterogeneous data and the corresponding potential association relationship and mode information, adjusting parameters and a structure of the digital twin model; and obtaining logistics prediction information, intelligent configuration and scheduling information and optimization suggestion information corresponding to the multi-source heterogeneous data, so as to complete digital twinborn management of the logistics site in a digital twinborn model according to the logistics prediction information, the intelligent configuration and scheduling information and the optimization suggestion information.

The invention discloses a filter tip modeling parameter visual identification and adjustment method, and relates to the technical field of filter tip production, a laser diameter measuring instrument and a linear array camera are used for synchronously collecting peripheral images of a filter tip, and high-precision and consistent size and gray scale double baselines are generated; a texture segmentation technology is adopted to identify a suspected recess area and generate a recess position vector, a confocal displacement sensor is utilized to carry out local depth walk-up to obtain depth distribution data, and a size compensation coefficient is generated and a size matrix is corrected through Gaussian process regression; statistical threshold analysis is conducted on the corrected size matrix, when parameters exceed the limit, the gap of a forming nozzle is adjusted, the size stability of the filter tip is ensured, the adjustment result and related data are written into a manufacturing knowledge base, follow-up batch optimization is supported, and the measurement reliability and the production line efficiency are improved; tiny recesses on the surface of the filter tip can be effectively identified and compensated, and the measurement accuracy and the assembly stability of modeling parameters are ensured.

The invention discloses an aircraft precise landing system and method based on multi-source heterogeneous data fusion. The system integrates a GNSS module, a multispectral visual sensor and an omnidirectional laser radar, and realizes high-precision landing through a layered guide strategy: a three-dimensional landing corridor is constructed based on GNSS global positioning in a high-altitude stage, poses are corrected by visual recognition Apriltag codes in middle and low altitudes, and a tail end switches infrared beacons to guide and fuse height measurement data of the laser radar. A dynamic emergency mechanism is adopted, and path backtracking and laser obstacle avoidance emergency landing are triggered when signals are lost. According to the technology, the accuracy and the safety of the aircraft in the landing stage are remarkably improved, the risk and the uncertainty are reduced, and the leading position and the practical value in the aviation field are shown.

The invention discloses a multi-size wafer pre-positioning method and system based on visual identification, and belongs to the technical field of wafer pre-positioning, and the method specifically comprises the steps: collecting a to-be-positioned wafer image of a stage region, employing a visual feature extraction model to recognize the outer edge features of a to-be-positioned wafer, constructing an outer edge candidate curve, and carrying out the recognition of the outer edge candidate curve; performing multi-circle fitting on the outer edge candidate curve, determining a size parameter of a to-be-positioned wafer by adopting a hierarchical confidence rule, generating an eccentric correction path of the minimum rotation amount based on the size parameter of the to-be-positioned wafer, predicting the offset of the wafer relative to the center of the carrying table, and establishing a rotation compensation sequence; performing segmented rotation and micro-displacement adjustment on the carrying table according to the eccentric correction path, and performing pre-positioning on the wafer to be positioned under the condition of multiple sizes; the method does not need to depend on a template with a fixed size or a mechanical positioning structure, can automatically analyze the outer edge features of wafers with different sizes, and completes eccentric prediction and rotation compensation based on visual feedback.

The invention relates to a girth welding device and method for aviation pipeline parts, and belongs to the technical field of aviation manufacturing and welding, and the device comprises a supporting platform, a positioning assembly, a clamp assembly, a welding assembly and a control assembly. The positioning assembly determines the welding seam position through visual identification and laser alignment; the clamp assembly adopts a flexible clamping module and a clamp body to fix a to-be-welded pipeline; the welding assembly completes welding operation through a sliding rail and a welding gun module. The control assembly achieves accurate control over the motion trail and the output power of the welding gun module through the trail control module and the heat influence control module. Clamping can be adjusted in a self-adaptive mode according to the inner diameter of a to-be-welded pipeline, and deformation is avoided; the power of the welding gun module is adjusted through temperature monitoring, the heat influence is reduced, the welding quality stability is improved, the welding effect is further optimized through the circulating cooling system, and the requirements of aviation pipeline parts for high-precision and high-quality welding are met.

The invention belongs to the technical field of grain loading supervision, and particularly relates to a grain transport vehicle grain loading state detection system and method based on visual identification, and the system comprises a visual collection module, an image processing and analysis module, a loading state evaluation module, an intelligent sampling auxiliary module and a cloud management and control module. Real-time pictures of grains in the vehicle are captured through the visual acquisition module, the image processing and analyzing module generates a three-dimensional distribution image of the grains based on the real-time pictures, the loading state evaluation module judges whether the grain loading state of the grain transporting vehicle is qualified or not according to the grain distribution image, the loading problem can be found and corrected in time, and the working efficiency is improved. The intelligent sampling auxiliary module automatically plans the sampling path and depth of the intelligent sampling machine according to the grain loading state information synchronized by the cloud management and control module, so that accurate and efficient automatic intelligent sampling is realized, and the accuracy of grain quality detection is improved.

The invention discloses a numerical control lathe visual positioning system and method, and the system comprises a vibration disc automatic feeding module which is used for automatically providing target workpieces, arranging the target workpieces in order, and outputting the target workpieces one by one; the manipulator grabbing module is used for determining initial coordinate information of a target workpiece grabbed by a manipulator; the visual identification module is used for acquiring a multi-angle image of the target workpiece and calculating the position deviation and the angle deviation of the target workpiece relative to the standard workpiece so as to determine visual positioning information of the target workpiece; the truss feeding module is used for grabbing the target workpiece subjected to visual positioning according to the visual positioning information and transferring the target workpiece to a machining area of the numerical control lathe; the numerical control machining module is used for machining a target workpiece based on a preset machining program, the problems that an existing numerical control lathe positioning mode is low in precision, poor in adaptability and the like are solved, high-precision and automatic positioning of the workpiece is achieved, and the machining precision and the production efficiency are improved.

The invention discloses a plate positioning method and system based on visual identification, a robot feeding method and system and a computer program product. The plate positioning method comprises the following steps: acquiring a plate image containing a plurality of plates arranged side by side, wherein a label identification code is pasted on the surface of each plate according to a preset pasting direction; determining the number and the arrangement positions of the plates according to the number and the arrangement positions of the label identification codes in the plate images; scanning the label identification code on each plate to obtain the size information of the plate; according to the placement direction of the label identification code, determining the placement direction of the plate corresponding to the label identification code; performing overall contour recognition on the plate image to obtain an overall contour; according to the number of the plates, the arrangement positions of the plates, the size information of the plates and the placement directions of the plates, the overall contour is subjected to plate division, and contour position information of the plates is obtained. According to the plate positioning method, even if a plurality of pure black or pure white plates are placed side by side, the contour position information of each plate can be accurately identified and obtained.

The invention discloses a multi-band transient electromagnetic self-adaptive scanning dam leakage channel three-dimensional imaging method. The method comprises the following steps: arranging a plurality of detection nodes on the surface of a dam; the central control unit sends a scanning instruction to the detection node; the detection node scans the dam and uploads the data to the central control unit; and after the data is uploaded, the central control unit calculates an underground three-dimensional resistivity data volume through an inversion algorithm and performs rendering imaging. According to the invention, through combination of multi-band detection and intelligent adaptive scanning, efficient and accurate dam leakage channel three-dimensional visual identification is realized, and the defects of low efficiency, single detection dimension and rigid scanning strategy of a traditional method are effectively overcome.

The invention relates to the technical field of industrial automation, discloses an intelligent welding method and system for tube panel module production, and aims at solving the problems that manual welding is high in dependence, uneven in quality, low in efficiency, high in cost and insufficient in monitoring optimization capacity. The method comprises the steps that a workpiece connector is recognized through three-dimensional vision, and a path is planned; carrying out real-time monitoring, data fusion and feature extraction by a multi-modal sensor; deep learning is performed to predict quality, and reinforcement learning is performed to optimize parameters; and finally, post-welding evaluation and tracing are realized. The system comprises a main control unit, a robot welding arm, a welding execution unit and a positioning clamping unit. A multi-mode intelligent sensing module; and an intelligent processing and auxiliary control unit. By the adoption of the technical scheme, full automation, high efficiency, high precision, high consistency, quality real-time monitoring and intelligent optimization of key welding of the tube panel module can be achieved.

The invention relates to the technical field of electrical parameter testing, in particular to an automatic testing system and method for a microwave component, and the system comprises a node density recognition module, an energy level regulation and control module, a matching gradient extraction module, a dynamic test execution module and a calibration path switching module. According to the method, three-dimensional state coordinates are constructed through frequency injection response, and density classification labels are established by taking frequency block division as units, so that frequency domain state evolution has a visual identification basis, and then signal combination screening is performed through a minimum difference value and a maximum change rate of response fluctuation amplitude and phase deviation dynamic states. Adaptation of excitation granularity and energy levels of different frequency bands is completed, the problem of insufficient resolving power in previous frequency domain response adjustment is solved, step-by-step tuning of a microstructure device is executed in combination with judgment of a path frequency stepping direction and a response gradient direction after signal parameter matching, continuous acquisition of response parameters is ensured, and the accuracy of frequency domain response adjustment is improved. And path offset caused by misjudgment of the tuning direction is avoided.

The invention discloses a famous high-quality tea target detection method based on an improved RT-DETR model, and relates to the technical field of visual recognition. Comprising the following steps: obtaining tea photos under different conditions, and preprocessing the tea photos to obtain a training set; based on a feature enhancement module, an improved multi-scale feature pyramid network module, a feature fusion module, a partial convolution and CGLU module, an OmniKernel module and a frequency domain-space domain feature enhancement module, improving the RT-DETR model and establishing an improved RT-DETR model, and training the improved RT-DETR model by using a training set; the trained improved RT-DETR model can be used for identifying the tea leaves. According to the invention, through multi-module coordinated optimization, the model keeps light weight, and MAP50 reaches 0.87, which is improved by 3.3% compared with a reference model. Especially under a complex background, the omission ratio is reduced by about 40% in tea target identification.