1854 results about "Welding defect" patented technology

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

The invention relates to the technical field of welding defect identification, and discloses a welding defect identification method and system based on a molten pool image. According to the method, a multispectral high-speed camera is used for collecting a molten pool dynamic image sequence in the welding process, after multi-scale morphological filtering preprocessing is conducted, a fusion feature vector is extracted through a depth separable convolutional network, and then the fusion feature vector is input into a defect classification model adopting a heterogeneous graph neural network architecture to obtain a defect probability distribution matrix. Then, constructing a dynamic sparse optimization model to position defects, generating a defect space coordinate set, and finally, outputting welding defect types and position information through hierarchical verification framework processing. According to the method, the problems of welding image noise interference, complex defect characteristics and the like are effectively solved, the accuracy and reliability of welding defect identification are improved, and powerful technical support is provided for welding quality control.

The invention relates to the technical field of image analysis, in particular to a heat exchanger welding quality visual inspection method and system, and the method comprises the following steps: collecting original image data from a welding image, carrying out the preprocessing of the original image data, including noise removal and brightness adjustment, and obtaining a preprocessed image; according to the method, in the whole process from welding image data collection to defect recognition result generation, preprocessing operation based on high-frequency noise pixel filtering and gray value frequency adjustment is carried out on original image data, interference information in an image is effectively reduced, and the reliability of the image data is improved; meanwhile, continuous iteration deconvolution operation and pixel detail optimization are carried out on the basis of preprocessing, the distinguishing precision and texture detail discernibility of the welding image are improved, the discernibility of the welding defect under different scales is enhanced through multi-scale-level image feature decomposition, and the precision of image feature data is comprehensively improved.

The invention discloses a mechanical welding seam defect identification method, and relates to the technical field of welding quality intelligent detection and defect identification, and the method comprises the steps: S1, collecting welding seam surface image data, obtaining a standardized image data set, carrying out the feature extraction of the standardized image data set, and obtaining a feature extraction result; the method comprises the following steps: S1, extracting edge contours, texture distribution and gray change features of defects, and generating a high-dimensional feature vector containing space position coordinates and morphological features of the defects, S2, generating a weld quality evaluation database containing defect distribution uniformity and defect severity scores according to edge continuity parameters and gray uniformity parameters in the high-dimensional feature vector; according to the mechanical welding seam defect identification method, accurate identification, classification and positioning of welding seam defects can be achieved, comprehensive evaluation of the welding seam quality is given, and the intelligent level and reliability of welding quality control are effectively improved.

The invention relates to the technical field of material nondestructive testing, and discloses a membrane structure welding seam defect detection method based on image recognition, which is used for solving the problems of low defect segmentation accuracy and incapability of effectively recognizing internal defects caused by continuous image gray change of lap welding seams of unequal-thickness flexible materials in a traditional method. The method comprises the following steps: firstly, collecting an original image of the lap weld of the unequal-thickness flexible material, carrying out gray conversion, analyzing thickness gradient distribution, adjusting a gray value, carrying out region segmentation to lock a weld range, extracting potential defect edge features to form a candidate region, and carrying out classified verification to confirm internal defects. Aiming at the problem of low defect segmentation accuracy caused by continuous change of image gray in the prior art, the method improves the defect identification precision through segmented mapping and boundary tracking logic, and is suitable for membrane structure engineering quality control.

The invention relates to the technical field of laser welding, in particular to a laser welding system control method for metal accessories. The method comprises the following steps that automobile industry metal fitting data are obtained, welding demand analysis is conducted according to the automobile industry metal fitting data, and therefore welding demand data are obtained; laser welding feature extraction and resistance spot welding feature extraction are carried out according to the welding demand data, so that laser welding data and resistance spot welding data are obtained; carrying out welding simulation according to the laser welding data and the resistance spot welding data so as to obtain welding simulation data; welding form feature extraction and welding strength feature extraction are carried out according to the welding simulation data, so that welding form data and welding strength data are obtained; and carrying out welding defect detection according to the welding form data and the welding strength data so as to obtain welding defect data. Automatic adjustment and accurate control of the laser welding process are achieved based on the laser welding technology.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the field of welding seam detection, in particular to an air conditioner refrigeration copper pipe welding defect detection device which comprises a base, positioning assemblies are symmetrically arranged on the two sides of the upper end of the base, each positioning assembly comprises a shell with a central through hole, a to-be-detected copper pipe horizontally penetrates through the central through hole of the shell, and a fixed cross beam is fixedly arranged between the top ends of the shells. Fixed ring frames are fixedly arranged at the two ends of the bottom of the fixed cross beam, movable ring frames are rotatably mounted in the fixed ring frames, the two movable ring frames are fixedly connected through mounting plates, the number of the mounting plates is four, the mounting plates are distributed in a cross shape, a pressure assembly is arranged on the mounting plate at the top end, and a detection assembly is arranged on the mounting plate at the bottom end. The stress load is gradually applied to the weld joint position of the to-be-detected copper pipe through the pressure assembly, and the deformation process of the weld joint position is monitored in real time in cooperation with the detection assembly, so that the bending strength of the weld joint position of the to-be-detected copper pipe can be dynamically detected in the circumferential direction.

The invention relates to the technical field of welding quality evaluation, in particular to a carbon steel pipe welding quality evaluation system which comprises a welding area three-dimensional scanning module, a welding seam dynamic grid generation module, a welding form change analysis module, a welding seam structure diagram construction module, a welding seam quality diagram convolutional network module and a welding defect intelligent recognition module. According to the invention, through real-time adjustment of the dynamic grid generation module, tiny changes of the geometric shape of the welding seam can be captured, new dimensions are provided for welding quality evaluation through generation of welding form characteristic data and construction of the welding seam structure diagram, and the introduction of the welding seam quality diagram convolution network module can improve the accuracy of welding quality evaluation. Node relations and structural features in a weld joint diagram structure are analyzed through a deep learning technology, an intelligent means is provided for welding quality evaluation, an intelligent welding defect identification module optimizes an identification process, various welding defects can be accurately classified, and a powerful technical support is provided for guarantee of welding quality.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention relates to the technical field of mechanical engineering, in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps that an elevator structure drawing is obtained; a three-dimensional elevator assembly is constructed according to the elevator structure drawing; lift car static load simulation is conducted according to the three-dimensional elevator assembly, and lift car static load data are obtained; detecting the structural strength of the lift car based on the lift car static load data; evaluating welding defects based on the structural strength of the lift car, and generating welding defect data; identifying a weld weak area according to the welding defect data; determining the abrasion loss of the car structure based on the weld weak area; according to the lift car structure abrasion loss, the lift car bottom plate fracture risk is predicted; carrying out welding optimization design based on the fracture risk of the bottom plate of the lift car to obtain welding optimization design data; and traction and traction simulation is conducted according to the three-dimensional elevator assembly, and traction and traction data are obtained. Based on the mechanical engineering technology, the safety and stability of the elevator structure are improved, and the design precision and efficiency are remarkably improved.

The invention provides an electric tricycle rear axle welding quality control system and method, and relates to the technical field of welding, a welding seam parameter preprocessing module is used for conducting welding seam analysis on an electric tricycle rear axle, and an initial welding parameter combination is determined; the simulation welding analysis module carries out simulation welding according to the initial welding parameter combination and draws a welding change trend chart; the welding path planning and evaluating module traverses the welding change trend chart to plan a plurality of welding paths and performs welding evaluation to generate a welding state value; the welding defect analysis module performs welding detection and defect analysis according to the welding state value and the welding change trend chart to generate a welding quality score; and the welding parameter optimization module adjusts the initial welding parameter combination according to the welding quality score to generate a welding optimization scheme. The technical problem that the welding quality cannot be effectively controlled due to the fact that the prior art is difficult to adapt to dynamic changes of the welding environment and workpiece conditions is solved, and control over the welding quality of the rear axle is improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention belongs to the technical field of welding seam detection, and particularly relates to a laser welding seam defect detection machine which comprises support plates, adjusting assemblies arranged between every two adjacent support plates, driving assemblies installed on the support plates and balancing assemblies arranged on one sides of the support plates. Wherein three groups of support plates are arranged in a circumferential array, a front-end support is fixedly mounted on one side of the long edge of each group of support plates, an ultrasonic detection probe, a dot matrix laser and a camera are sequentially mounted on the three groups of front-end supports, an electric control module is mounted on the top surface of each support plate, and a power supply module, a data analysis processing module and a control system are arranged in each electric control module. The device can automatically move on a reducing pipeline, perform positioning detection on a welding seam, and perform circumferential uninterrupted detection in the detection process, so that the detection precision is higher, a mode of combining laser detection and ultrasonic detection is utilized, displacement deviation is automatically corrected after reducing, the error probability is greatly reduced, unnecessary workload is avoided, and the detection efficiency is improved. The detection efficiency is improved.

The invention provides a welding structural part monitoring method and system based on a vibration acoustic method, and relates to the technical field of welding monitoring, and the method comprises the steps: constructing a defect classification model and a defect strategy model, employing an ultrasonic phased array to detect a to-be-detected welding line of a welding structural part, obtaining a first image, inputting the first image into the defect classification model, and obtaining a second image; a second image is obtained, whether welding defects exist in the second image or not is judged, and if not, the welding quality evaluation result of the to-be-detected weld joint is set to be optimal and output; and if yes, inputting the second image into the defect strategy model to obtain a defect repairing strategy of the second image. According to the method, the defects of the welding structural part are monitored in real time, the welding defects are classified on the basis of the defect classification model, then the welding defects are evaluated, the targeted repairing strategy is obtained through the defect strategy model on the basis of the evaluation result, and the purpose that the welding technology is adjusted in time for repairing on the basis of the welding defects occurring in the welding process is achieved.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention discloses a welding quality visual detection method, and relates to the technical field of weld defect detection, and the method comprises the steps: obtaining a pixel neighborhood block corresponding to each pixel point in a to-be-detected welding gray image, and obtaining the illumination influence degree in each pixel neighborhood block according to the gray feature and gradient feature of each pixel point in each pixel neighborhood block; according to the position distribution characteristics between the edge end points of the adjacent edges in the welding gray level image to be detected, the same kind of edges are judged; obtaining a gamma value adjustment coefficient of a pixel point at the central position in each pixel neighborhood block by obtaining the confidence coefficient that each similar edge is the inner edge of the welding seam and the defect degree of each similar edge; the gamma value of each pixel point is adjusted, the gray level output value of each pixel point in each pixel neighborhood block is obtained, and an optimized welding gray level image is obtained; performing quality detection on the to-be-optimized welding grayscale image; the accuracy of welding quality detection can be improved.

Provided in the present disclosure are a welding quality test method, apparatus and system, and a medium and a program. The test method comprises: receiving a plurality of groups of welding data collected by a plurality of sensors during welding; pre-processing the plurality of groups of welding data, so as to obtain a plurality of key features; inputting the plurality of key features into a test model, such that the test model determines, by means of a knowledge graph, whether there is a welding defect; when it is determined that there is a welding defect, outputting an identification result for indicating the presence of the welding defect; in response to the welding defect in the identification result belonging to a new welding defect type, adding new nodes into the knowledge graph, wherein the new nodes comprise a plurality of new welding process parameter nodes, which correspond to the plurality of key features, and a new welding defect node; and on the basis of the plurality of groups of welding data, establishing an association relationship between the plurality of new welding process parameter nodes and the new welding defect node.

The invention provides a parameter control method and system in the friction stir welding process, and relates to the technical field of welding equipment of metal materials. The method comprises the steps that before friction stir welding is started, working condition information of a to-be-welded workpiece is input into a welding process simulation model for virtual test welding; screening out a plurality of groups of standard process parameter combinations meeting preset welding quality requirements; in the friction stir welding process, the change trend of all the technological parameters is determined according to the real-time technological parameter combination within the first preset time period; and if it is determined that the deviation risk exists in the current welding state within the second preset time period in the future according to the change trend, the standard technological parameter combination with the maximum similarity with the real-time technological parameter combination in the standard technological parameter library is determined as the target technological parameter combination to conduct parameter control adjustment on the welding equipment. By implementing the method, the welding defects caused by sudden change of the process window can be overcome, and the stability of the welding process and the consistency of the welding seam quality are improved.

The invention relates to the technical field of analyzing materials by utilizing an ultrasonic technology, in particular to a pipeline welding seam nondestructive testing method and system based on the ultrasonic technology, and the method comprises the following steps: acquiring echo signals collected by each ultrasonic probe at a welding seam position of a pipeline; analyzing the similarity degree of the time domain local features of the echo signals of each ultrasonic probe and the ultrasonic probe at the adjacent position under the same wavelet transform scale, and determining position similar probes and position similar parameters of each ultrasonic probe; according to the position similar parameters of the ultrasonic probe and the change difference of the echo signals of the ultrasonic probe and the corresponding position similar probe under each scale, determining a correction coefficient under each scale, and correcting a detail coefficient of the echo signals of the ultrasonic probe under each scale; obtaining a reconstruction signal based on the corrected detail coefficient under each scale; and comparing the reconstruction signal of each ultrasonic probe with the standard echo signal to detect the weld defect. By adopting the method, false detection and missing detection of tiny defects can be reduced.

The invention relates to a steel structure engineering welding quality defect analysis method based on voiceprint monitoring, and the method comprises the steps: carrying out the multi-channel voiceprint synchronous collection, time-frequency feature fusion, wavelet packet analysis and Mel-frequency cepstral coefficient extraction for a plurality of defect features fused in voiceprint data in a welding process; a hierarchical semantic concept space and a dynamic causal relationship generation model are established in combination with a welding physical knowledge base, a causal knowledge graph is constructed, causal association between semantic concepts is deduced through a gating circulation unit and a graph neural network, anti-fact disturbance and path aggregation analysis is carried out on a causal graph structure, and a result is obtained. And finally, defect category probability output and causal traceability graph visual display are realized. According to the scheme, the accuracy, traceability and result interpretability of welding defect recognition are effectively improved, and data support is provided for intelligent diagnosis and continuous model optimization in the welding process.

The invention discloses a welding defect classification method and system based on deep learning, and the method comprises the steps: collecting a welding image, taking the welding image as a training sample in a training set, and carrying out the preprocessing of the welding image in the training set; constructing a feature fusion CNN image recognition and classification model; training a CNN image recognition and classification model by using the training sample to obtain a trained CNN image recognition and classification model; and inputting a welding image to be classified and recognized into the trained CNN image recognition and classification model, and obtaining a defect category to which the welding image belongs through model reasoning. According to the method, the defect identification precision is effectively improved, the feature extraction process is optimized, and the real-time detection capability is ensured.

The invention provides a steel structure welding quality detection method and a storage medium, and the method comprises the steps: generating a composite image # imgabs1 # marked with the type and position of a welding defect based on an unmarked real welding region image # imgabs0 # of a steel structure; the real steel structure welding image # imgabs2 # and the composite image # imgabs3 # marked with the welding defect type and position are combined into a steel structure welding image set # imgabs4 #; performing datamation processing on the steel structure welding image set # imgabs5 # to generate a deformable grid # imgabs6 # matched with the type of the welding defect and a preprocessing image set # imgabs7 #; and inputting the preprocessed image set # imgabs8 # into a pre-trained building engineering steel structure welding defect detection model so as to output welding defect characteristics of a welding seam area. The steel structure welding quality detection method is small in data dependence, high in welding defect recognition robustness and high in intelligent degree, and the intelligent level of constructional engineering steel structure welding quality detection can be improved.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention relates to the technical field of profile welding, and discloses a positioning welding device for a steel structure profile machining production line, which comprises a supporting bottom plate, a supporting frame is mounted at the top of the supporting bottom plate, and a driving screw rod is movably connected to one side of the bottom of the supporting frame through a bearing; and a motor used for driving the screw rod to rotate is installed on the outer side of the supporting frame, a threaded block is in threaded connection with the outer side of the driving screw rod, a connecting frame is fixedly connected to the bottom of the threaded block, and an annular driving guide rail is installed on the outer surface of the connecting frame. And through the multi-angle welding function, the problem that precise positioning is difficult to achieve when traditional welding equipment treats complex steel structure profiles is solved, and the welding quality and efficiency are improved. The angle of the laser welding gun can be rapidly adjusted through the unique positioning device, it is guaranteed that all faces of a profile can be evenly welded in the welding process, welding defects caused by improper welding positions are avoided, and therefore the stability and the bearing capacity of the whole structure are improved.

The invention discloses a welding spot defect detection method, electronic equipment and a storage medium. The mode relates to the field of image detection, and the method comprises the following steps: obtaining a to-be-detected image of a to-be-detected welding spot; edge detection is carried out on the to-be-detected image, a welding spot edge image of the to-be-detected welding spot is obtained, and the welding spot edge image is used for representing contour features of the to-be-detected welding spot in the image space; parameter extraction is conducted on the welding spot edge image, welding spot shape information of the to-be-detected welding spot is obtained, and the welding spot shape information is used for representing shape features of the to-be-detected welding spot in a parameter space; welding spot defect detection is conducted on the to-be-detected welding spot based on the welding spot shape information, a defect detection result is obtained, and the defect detection result is used for representing whether the to-be-detected welding spot has welding defects or not. According to the invention, the technical problem of low efficiency of detecting the welding spot defect in the prior art is solved.

The invention discloses a visual inspection method and device for reflow soldering quality and computer equipment. The method comprises the steps of obtaining a to-be-detected product image entering a detection area; preprocessing the to-be-detected product image and extracting a welding spot ROI region; the welding spot ROI is put into a defect classification model for welding defect identification and classification, and a classification result is obtained; and determining an equipment parameter adjustment scheme according to the classification result in combination with a preset adjustment rule, and adjusting the reflow soldering equipment according to the equipment parameter adjustment scheme. By implementing the method, the welding spot quality can be automatically recognized in the welding process, the production parameters are automatically adjusted according to the detection result, the welding quality is effectively improved, potential quality problems are avoided, the production efficiency can be improved, manual intervention is reduced, and the labor cost is saved.

The invention provides a welding seam defect identification and area quantitative calculation method and system, and relates to the technical field of defect identification, and the method comprises the steps: collecting an array eddy current probe scanning signal to generate a welding seam eddy current C scanning original image, enhancing the image quality through adaptive filtering and sharpening processing, constructing a nonlinear mapping function for normalization, and calculating the welding seam defect. And optimizing the enhancement parameters by using a genetic algorithm. The method can effectively identify the weld defect and realize quantitative area calculation, improves the accuracy and efficiency of weld defect detection, and can be widely applied to the field of industrial welding quality control.

The invention discloses a steel structure weld defect detection device and method.The steel structure weld defect detection device comprises a U-shaped base, a transmission roller, an L-shaped frame, an ultrasonic probe, a liquid spraying mechanism and a display device, the transmission roller is arranged on the U-shaped base and used for supporting and rotating a steel pipe, and the L-shaped frame fixes the ultrasonic probe so that the ultrasonic probe can make contact with the surface of a steel pipe weld for real-time detection; a liquid spraying mechanism uniformly sprays water-based foam liquid on the surface of the welding seam, an air spraying device blows air into air holes in the welding seam, so that air enters the foam liquid through the air holes to form stable air bubbles, and a cleaning mechanism wipes the residual foam liquid on the surface of the welding seam to ensure that the surface is clean and tidy; according to the method, high-precision detection of the welding seam of the steel pipe is achieved, the automation degree of operation is high, manual intervention is reduced, the detection efficiency and accuracy are improved, and the continuity of detection and follow-up evaluation work can be improved.