112 results about "Abnormal positions" patented technology

The invention discloses a fault analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first burying point information corresponding to an abnormal event if the vehicle detects that the abnormal event of a burying point is triggered, the first burying point information comprises at least one of a subsystem type corresponding to the abnormal event, an application module corresponding to the abnormal event, an abnormal type corresponding to the abnormal event, an information domain corresponding to the abnormal event, a fault identifier corresponding to the abnormal event, an abnormal name corresponding to the abnormal event and an abnormal position corresponding to the abnormal event; and sending the first burying point information to a server, so that the server determines the fault information corresponding to the abnormal event according to the first burying point information, thereby improving the vehicle fault problem determination rate.

The invention discloses an anomaly detection method, system and device based on a multi-mode visual large model and a medium, and the method comprises the steps: carrying out the shooting of an image at a designated point through an inspection device, comparing a template image in a normal state with a detection image collected in real time, whether abnormity exists or not and the position of the abnormity in an image are judged through the following steps that S1, the image is registered; s2, identifying a target in the image by using an open set target detection large model so as to detect various abnormal parts; s3, performing image segmentation by using the large segmentation model; s4, performing IOU comparison on contours generated by segmentation of the template image and the to-be-detected image, removing part of differences, performing area-by-area analysis, and setting the contours with IOU smaller than a specified threshold value as possible abnormal positions; and S5, carrying out similarity comparison on the different contours, removing contours with the similarity greater than a preset threshold value, and taking the rest contours as abnormal areas. The method does not need to perceive a specific abnormal type in advance, and has the capability of identifying and segmenting multiple types of targets.

The invention discloses a monitoring and early warning method based on intelligent wearing, and aims to improve the safety of a user. The method comprises the following steps: continuously collecting original data streams from various built-in sensors through intelligent wearable equipment, and reflecting the state, posture, position and environment information of a user in real time; and carrying out real-time preprocessing, time-space synchronization and multi-modal fusion on the data to construct unified data representation. Based on this, analysis and feature extraction are performed, key indicators are calculated and compared with threshold values, and preset safety or auxiliary events are identified and confirmed in real time, the events at least including user tumble, location abnormality, external threats or active help seeking of the user. And according to the identified event type, severity and context, automatically selecting and triggering a corresponding execution module built in the equipment to execute a preset response action. According to the invention, various danger or help-seeking scenes can be accurately identified, and timely and effective automatic response is provided.

The invention discloses a test link abnormity positioning device, system, method, equipment, medium and program, and relates to the technical field of computers, and the method comprises the steps that when a test link is abnormal, a processing component writes a first enabling bit into a first register of each node device, each node device responds to the first enabling bit to enter a low-power-consumption mode, and the first enabling bit enters a low-power-consumption mode; according to the embodiment of the invention, each node device triggers link retraining in the low-power-consumption mode to expose the unstable position of the test link in the low-power-consumption conversion process, and determines the abnormal position of the link according to the flag bit state of the second register, so that the troubleshooting efficiency and the abnormality positioning precision are improved, and the time and the labor cost are saved. Therefore, the problems of low working efficiency and high time and labor cost caused by the fact that the abnormal problem cannot be effectively solved in time when the abnormal position of the link is checked manually in the prior art are solved.

The invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and discloses a performance detection and analysis method of a guide rail glass lifting system. According to the method, the noise data, the output current and the vibration time domain signal are subjected to data analysis to construct the smoothness comprehensive evaluation matrix, the system state is comprehensively evaluated from multiple aspects of frequency band energy, current fluctuation, vibration frequency and the like, the operation anomaly characteristics are comprehensively covered, the anomaly recognition accuracy is improved, and the system reliability is improved. A scientific basis can be provided for performance quantitative evaluation, and the one-sidedness of a single parameter is avoided. When the smoothness of the guide rail glass lifting system is judged to be abnormal, the abnormal part can be accurately recognized based on the noise spectrum mapping relation, meanwhile, the specific abnormal position is positioned in combination with current, vibration signals and position-time mapping, double positioning of the part type and the physical position is achieved, manual blind troubleshooting is avoided, and the working efficiency is improved. And the fault diagnosis efficiency and the maintenance pertinence are improved.

The invention discloses a ship maintenance inspection robot and a use method, and relates to the technical field of ship maintenance inspection robots. Through mutual cooperation of the telescopic springs and the anti-collision plates, it is conveniently guaranteed that in the collision process of the robot, impact on a robot body is reduced, the use safety of the robot is improved, and then the function of protecting the robot and ship equipment is achieved; a linear relation model of obstacle length, width, walkway width and passing time is constructed to quantify the influence of the obstacle on the passing efficiency, and the limitation that only the route length is used as a single optimization index is avoided; the actual passing time of each route is calculated based on historical inspection data, the consumed time of the optimal route and the consumed time of the to-be-selected route are dynamically compared, real-time adjustment of the route priority is achieved, and the inspection efficiency in a complex environment is improved; and the distribution rule of abnormal positions is judged by using a linear correlation coefficient, and if abnormal points are linearly arranged, the corrosion possibility is eliminated, so that the misjudgment rate is reduced.

The invention discloses an AI substitute inspection method and system based on multi-modal perception, and relates to the technical field of intelligent detection. According to the method, images, audios, environmental parameters and equipment state data of an inspection area are synchronously collected through a multi-modal sensor array, and after space-time alignment preprocessing, multi-modal features are extracted and input into a pre-trained fusion model to generate unified feature representation. The fusion model adopts a Transform architecture to realize cross-modal feature interaction, an anomaly detection algorithm is used to identify and classify anomalies, a confidence coefficient is calculated in combination with an evidence theory, and finally an inspection report containing anomaly positions, types and processing suggestions is generated. According to the invention, the information limitation of single-mode inspection is solved, the detection accuracy and robustness in a complex environment are improved, unmanned inspection in a high-risk scene is realized, the labor cost is reduced, the safety is guaranteed, and the method is suitable for automatic inspection in the fields of industrial equipment, public facilities and the like.

The invention relates to the technical field of speed reducers, and particularly discloses a speed reducer fault monitoring method and system, and the method comprises the steps: obtaining multi-source data and working condition parameters; preprocessing the multi-source data; extracting a multi-dimensional feature vector from the preprocessed multi-source data according to the working condition parameters; calculating the deviation degree between the collaborative change condition of the multi-dimensional feature vector and the reference baseline; judging whether early abnormality occurs according to the deviation degree; when the early abnormality occurs, analyzing the type of an abnormal feature inducing deviation in the multi-dimensional feature vector, and determining an abnormal type and / or an abnormal part according to the type of the abnormal feature; according to the method, multi-source heterogeneous data acquisition and multi-dimensional feature extraction based on working condition parameters are combined, and a deviation degree calculation method based on a multi-dimensional feature collaborative change reference baseline is introduced to detect early abnormality, so that the problem that weak early fault signals are difficult to recognize in a traditional method is solved.

The invention discloses a sound barrier anomaly recognition method and device based on an anomaly detection model, and relates to the field of anomaly detection. In the method, a spatio-temporal data matrix is constructed according to a timestamp, spatial coordinates of a sensor and multi-dimensional data, and a preset state feature vector is constructed according to the timestamp, environmental data and current traffic flow data; constructing a sensor topological graph according to the spatio-temporal data matrix, obtaining a spatio-temporal feature vector of each time step according to the sensor topological graph, and determining an abnormal candidate event according to the target spatio-temporal feature vector; determining the confidence of the abnormal candidate event; and determining the sensor corresponding to the abnormal candidate event of which the confidence coefficient is greater than a second threshold value as a target sensor, determining an abnormal position in the sound barrier according to a space coordinate of the target sensor, and determining an abnormal type according to a target spatial-temporal feature vector corresponding to the target sensor. By implementing the technical scheme provided by the invention, the effects of accurately identifying the abnormal condition of the sound barrier and improving the detection efficiency and accuracy are achieved.

The application discloses a chip packaging defect detection and positioning method and device, and the method comprises the following steps: performing PCA preprocessing on a test set and a training set of a chip packaging image; training a deep tensor dictionary learning method based on the features extracted from the training set, so as to obtain a trained deep tensor dictionary; determining a sparse coding third-order matrix based on the features extracted from the test set and the trained deep tensor dictionary; and judging whether the chip packaging image to be detected has defects and positioning the defects based on the sparse coding third-order matrix. The application describes the main features of the image through a three-dimensional dictionary, strengthens the feature expression ability of the deep tensor dictionary by combining deep learning, and approximately represents the image by combining sparse coding, so that whether the image is abnormal can be judged according to the error between the actual image and the approximate representation, and the abnormal position of the image can be positioned according to the sparse coding, thereby improving the detection speed and the detection accuracy of the chip packaging defect detection.

The application relates to the technical field of data monitoring, in particular to a pipeline abnormality monitoring method and device, electronic equipment and a medium. The method comprises the following steps: comparing pressure values of each substation with corresponding preset standard pressure values of each station to determine whether an initial abnormal substation exists; if yes, determining a monitoring range corresponding to the initial abnormal substation according to an initial abnormal position; determining corresponding noise frequency and noise characteristics based on sound monitoring data corresponding to the monitoring range, and determining a first weight value based on the same; determining a predicted influence value according to the noise frequency and the noise characteristics; determining a pressure difference value by comparing the pressure value corresponding to the initial abnormal substation with the corresponding preset standard pressure value, and determining a second weight value according to the predicted influence value and the pressure difference value; determining whether the monitoring range is abnormal according to the first weight value and the second weight value, and if yes, generating warning information based on the monitoring range. The application can discover the existing abnormality of a transportation pipeline in time.

The present application belongs to the technical field of data labeling, and particularly relates to a 3D point cloud data labeling-based acceptance method and device and a storage medium. The method comprises the following steps: S1, standard presetting, in which, based on an acceptance object and an acceptance standard, a size of an element box and a fitting error of the element box are preset in advance; S2, system judgment, in which, according to a comparison between the standard presetting and actual labeling, it is judged whether the size of the element box is correct, whether the fitting of the element box is within the error range, whether the element boxes overlap, and an abnormal position and an abnormal reason are identified; and S3, inspection, in which, according to the abnormal position and the abnormal reason judged by the system, a form that can be viewed visually is used to display, and the abnormal position and the abnormal reason are clicked to inspect. The purpose is to solve the problem of misjudgment and low efficiency in the background art.

A data shaping apparatus includes: an abnormality detection unit configured to detect, as abnormal data, positioning data indicating an abnormal position from a positioning data group based on a movement constraint condition for a moving object, the positioning data group being a collection of positioning data at least indicating a position and a positioning time of the moving object; a first generation unit configured to generate first data from the positioning data group by replacing a position indicated by the abnormal data with a position assumed under a predetermined movement condition; and an output unit configured to output information based on the first data.

The invention discloses a flushing system anomaly detection method, device and system for medical equipment, and belongs to the technical field of medical equipment. The medical equipment is provided with a flushing channel, the method comprises the steps that first detection information and second detection information corresponding to the flushing channel are obtained, the first detection information represents the flushing pressure change condition of the flushing channel within the target duration, and the second detection information comprises at least one of the second detection information and fluid temperature information; the second detection information represents the bubble generation condition in the flushing channel, and the fluid temperature information represents the temperature change condition of fluid in the flushing channel; and according to the first detection information and the second detection information, abnormal alarm information corresponding to the flushing channel is output, and the abnormal alarm information indicates at least one of an abnormal position in the flushing channel and an abnormal state corresponding to the abnormal position. By means of the method, the abnormity in the flushing channel can be accurately detected.

The invention discloses a position processing model training method and device, a position processing method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: acquiring a position track sequence generated by interaction between a plurality of users and a position interaction node within a set time period; determining a target position interaction node based on all the sequences; aiming at each target position interaction node, respectively clustering all position track sequences containing the position of the node to obtain a plurality of class clusters; then, constructing a sample containing annotation information according to the centroid of each class cluster; according to the method, model training is carried out on the basis of the samples, the abnormal position processing model is generated, the training data size is reduced, noise in original data is reduced, and therefore the training accuracy and the training speed of the abnormal position processing model are effectively improved.

The application relates to the technical field of target detection, and provides an optimization method and device for target detection, which comprises the following steps: inputting a to-be-detected image into a target detection model, so that the target detection model outputs a target category, a target position and a first confidence value for determining the category; inputting the to-be-detected image into an abnormality detection model, so that the abnormality detection model outputs an abnormality position and a second confidence value for determining the abnormality; calculating the IoU of the target position and the abnormality position; if the IoU is greater than or equal to a set value, optimizing the confidence value corresponding to the target category according to the first confidence value and the second confidence value; and outputting a target detection result. The abnormality detection model and the target detection model are combined, the optimization of the confidence value corresponding to the target category in the target detection result is realized, and the simultaneous optimization of the missing and overkill of the target detection result can be realized without changing the confidence value threshold as much as possible.

The invention discloses an abnormity identification method and device for a vehicle test tool, and relates to the technical field of test tool abnormity identification. The method comprises the following steps: in the process of testing a whole vehicle or a vehicle part through a test tool, detecting real-time relative displacement between two corresponding connecting pieces with a relative relationship by using each connecting displacement sensor; for each connection displacement sensor, calculating a reference data fluctuation threshold value corresponding to a specific position according to the historical relative displacement, detected by the connection displacement sensor in a first historical period, of the specific position of the test tool in a normal state; according to the real-time relative displacement detected by each connection displacement sensor and a reference data fluctuation threshold value, whether abnormal fluctuation exists in the real-time relative displacement or not is determined; and under the condition that any real-time relative displacement has abnormal fluctuation, a structure abnormal position is determined, and a structure abnormal alarm is given. According to the embodiment, the position with the abnormal structure in the test tool can be quickly identified and positioned.

The utility model relates to the technical field of testing fixtures, in particular to a testing fixture for an electronic control unit positioning tray, which comprises a bottom plate, a tray positioning pin, clamps, a floating plate and guide pins, the tray positioning pin, the guide pins and the clamps are mounted at the top of the bottom plate, the clamps are positioned on two sides of the top of the bottom plate, and guide holes and mounting holes are arranged on the surface of the floating plate. A slidable floating head is mounted in each mounting hole, a spring is sleeved outside each floating head, and two ends of each spring respectively abut against the lower surface of the corresponding mounting hole and a boss at the bottom of the corresponding floating head. Compared with the prior art, whether the positioning tray is deformed or not is judged through the tray positioning pin, whether the product positioning pin of the positioning tray is deformed or loosened or not can be quickly judged through whether the floating head in the floating plate mounting hole is jacked up or not, and the abnormal position can be quickly positioned; the inspection efficiency of the positioning tray is improved, and the product risk caused by the unqualified positioning tray is reduced.

The invention relates to the technical field of test evaluation, in particular to a semi-physical simulation system test confidence coefficient online evaluation and guarantee method, which comprises the following steps of: recording asynchronous abnormal events in real time, and generating a local asynchronous event sequence which serves as basic data of subsequent confidence coefficient weakening; calculating a confidence weakening factor of each local confidence unit, reflecting the real influence degree of the asynchronous abnormal event in the local region, and outputting a local confidence evaluation result; according to the method, the overall test credibility is subjected to decreasing type recombination, an overall credibility structure capable of indicating a local abnormal position is formed, the situation that the test is misjudged to pass when asynchronous abnormity exists is avoided, and abnormal positioning information for backtracking and an independent local confidence record are generated. According to the method, the structural decline mode not only improves the response capability of the overall assessment to the chain propagation risk, but also realizes the conversion of credibility calculation from static weighting to dynamic conduction.

The invention provides an abnormal detection positioning method and system for an electromagnetic test, and the method comprises the steps: carrying out the electromagnetic interference test of a target device, and obtaining abnormal position information and abnormal test parameters when a detection abnormality occurs; obtaining an equipment abnormal area and a plurality of implicit abnormal elements in the equipment abnormal area according to the abnormal position information and an index, and collecting an infrared image of the equipment abnormal area; according to the plurality of implicit abnormal elements, carrying out integration level analysis to obtain an integration level, and configuring a detection positioning coefficient; and according to the infrared image, identifying a plurality of temperature anomalies of the plurality of implicit abnormal elements, respectively correcting the detection positioning coefficients, and respectively carrying out array anomaly identification according to the anomaly test parameters to obtain a plurality of element anomaly probabilities as anomaly detection positioning results. The technical problem that in the prior art, when electronic equipment is subjected to electromagnetic interference testing, abnormal element detection and positioning are not accurate is solved.

The present invention relates to the field of cable attitude detection technology, and in particular to a long-distance cable attitude detection method for underwater robots. Acceleration time series data of all monitoring positions on the long-distance cable of the underwater robot are obtained, and cable attitude changes are analyzed. Considering the acceleration differences and position distribution of different monitoring positions on the cable, the position attitude change degree of each monitoring position is determined. When performing a specific task, the attitude changes of some monitoring positions on the cable are consistent, and the monitoring positions are divided based on the attitude change degree. The more consistent the attitude changes of the monitoring positions close to the underwater robot, the greater the impact of the motion and the higher the probability of abnormality, so the degree of influence value of each monitoring position is calculated. Since the torsional change of the cable is a process of gradual accumulation of stress, the degree of influence value of each monitoring position at all times is comprehensively analyzed to obtain the attitude distortion degree value, and finally the abnormal position of the attitude is determined.

The invention belongs to the technical field of substation equipment intelligent inspection and anomaly recognition, and particularly relates to a substation intelligent inspection robot anomaly recognition method and system based on multi-modal fusion and deep learning. The objective of the invention is to solve the problems of single mode limitation, poor working condition adaptability and the like in substation equipment abnormity identification. The method comprises the following steps: synchronously acquiring multi-modal time sequence data through an inspection robot, and embedding a GPS position and a unique ID of equipment to complete scene marking; after preprocessing and time alignment, extracting depth features of each modal; in combination with the working condition parameters, outputting an adaptive weight by a dynamic weight fusion network, and generating a combined multi-modal feature; and through a space-time double-flow deep learning network, synchronously capturing space static characteristics and time dynamic characteristics, identifying an abnormal state, and positioning an abnormal position. The system comprises a data acquisition module, a preprocessing module, a feature extraction module and other core modules, supports online updating of the model, adapts to complex dynamic working conditions of the transformer substation, and improves the inspection efficiency and the anomaly recognition accuracy.

The invention relates to the technical field of experiment monitoring, and provides an experiment monitoring alarm method and device based on a scheduling plan, a computer readable medium and electronic equipment. Experimental information and equipment information are acquired, operation steps and operation duration of each operation step are determined, and a dynamic scheduling scheme of the equipment components is generated; performing an experiment based on the dynamic scheduling scheme, and monitoring the actual time corresponding to each operation step and the actual operation state of each equipment component; when it is detected that the actual time or the actual operation state is abnormal, an alarm identifier is displayed at the associated position of the control interface; and performing indexing based on the alarm identifier to determine an abnormal target component and an abnormal reason from the equipment components. Equipment resource allocation is optimized through a dynamic scheduling scheme, visual positioning of an abnormal position is realized in combination with a control interface association alarm mechanism, experimental monitoring is converted into active early warning from passive response, and the efficiency and accuracy of an experiment and a monitoring process thereof are guaranteed.

The application discloses a device, method and equipment for checking abnormalities of a laid mineral insulated cable, and belongs to the technical field of power facilities. The device comprises: a laying track acquisition module, which is used to acquire a track line of the laid mineral insulated cable; a position of interest determination module, which is used to determine a position of interest according to the track line; and a flaw detection module, which is used to perform nondestructive flaw detection on the position of interest based on a nondestructive flaw detection means to determine whether the position of interest has an abnormality. According to the technical scheme, the position of the mineral insulated cable where damage defects are prone to occur can be checked for abnormalities through the laying track of the mineral insulated cable, maintenance personnel can be helped to timely repair the abnormal position, the insulation of the mineral insulated cable is prevented from being too low and electric breakdown from occurring, the safe operation of a power system is maintained, the service life is prolonged, and the maintenance cost of the mineral insulated cable is reduced.

The invention discloses a reset control method and system of a mushroom mechanical arm based on path planning, relates to the technical field of path planning, and determines the clamping action of the mushroom mechanical arm according to the detection of a clamping path. And according to the clamping action and the corresponding clamping posture parameters, the clamping grade of the mushroom frame by the mushroom mechanical arm is determined, the clamping grade of the mushroom frame by the mushroom mechanical arm is guaranteed, and accurate clamping of the mushroom frame by the mushroom mechanical arm is achieved. Therefore, according to the position variable quantity of the first obstacle and the moving path of the mushroom mechanical arm, reverse reset of the mushroom mechanical arm is triggered; in the reverse reset process of the mushroom mechanical arm, the abnormal posture of the mushroom frame is determined based on the multiple posture parameters of the mushroom frame, and the mushroom mechanical arm is triggered to dynamically and stably control the mushroom frame according to the abnormal posture of the mushroom frame and the corresponding abnormal position, so that the reverse reset accuracy of the mushroom mechanical arm is guaranteed, and the mushroom frame is prevented from being damaged. And the dynamic stability of the mushroom mechanical arm to the mushroom frame is further improved.

The invention provides a valve opening and closing state real-time tracking and abnormality diagnosis method, which comprises the following steps: comparing the impedance growth rate of each branch with increased roughness with a preset flow distribution rated value to obtain the flow contribution variation of each branch, and determining the interference level of each branch according to the flow contribution variation; the abnormal type of the abnormal valve and the flow of the branch to which the abnormal valve belongs are analyzed, the influence weight between the abnormal type and the flow distribution amount is evaluated by combining the interference level, and the accurate coordinate of the abnormal position is determined; the actual flow at the accurate coordinates of the abnormal position is obtained, the floating range between the expected flow and the actual flow is determined, and the tracking judgment boundary value of the opening and closing state of the valve is determined according to the floating range; the roughness change rate and the opening reading which are collected in real time are processed by tracking and judging boundary values of the opening and closing state of the valve, and whether the opening and closing state of the valve is normal or not is evaluated.

The invention discloses a hot rolling plate shape abnormity automatic identification method, and belongs to the field of AI computer vision, and the method comprises the steps: video collection and transmission: arranging a camera between hot rolling and finish rolling field racks, and collecting a strip steel plate shape video stream in real time, the video stream is transmitted to the AI intelligent identification system through the streaming media server or the switch or is directly transmitted to the AI intelligent identification system through the switch; the AI intelligent recognition system carries out frame extraction on the video stream, and point position coordinates of four corner points of the strip steel plate shape are recognized through a key point detection model; voice early warning is automatically recognized through the AI computer vision technology, the fluctuation value of a specific certain point of the plate shape is pointed out in time, accurate plate shape position control key points are provided, and rolling tends to be normal; and meanwhile, a plate shape abnormal position overrun signal can be triggered to send an L1 control signal, field equipment is triggered to act, field rolling is stopped, and serious equipment safety accidents caused by waste steel are avoided.

The application discloses an abnormality detection method, system, device and medium based on a multimode visual large model, which comprises the following steps: a patrol device captures images at a specified point, and compares a template image in a normal state with a real-time collected detection image; whether there is an abnormality and the position of the abnormality in the image are determined through the following steps: S1. image registration; S2. using an open set target detection large model to identify targets in the image to detect various abnormal components; S3. using a segmentation large model to perform image segmentation; S4. comparing the IOU of the contours generated by the template image and the to-be-detected image segmentation, eliminating part of the differences and analyzing region by region, and setting the contour with an IOU less than a specified threshold as a possible abnormal position; and S5. comparing the similarity of the difference contours, eliminating the contours with a similarity greater than a preset threshold, and the remaining contours are abnormal regions. The application does not need to perceive specific abnormal types in advance, and has the ability to identify and segment multiple types of targets.

The present invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and discloses a method for detecting and analyzing the performance of guide rail glass lifting systems. The present invention constructs a comprehensive evaluation matrix for smoothness by performing data analysis on the noise data, output current, and vibration time domain signals, comprehensively evaluating the system status from multiple aspects such as frequency band energy, current fluctuation, and vibration frequency, comprehensively covering operational abnormality characteristics, improving the accuracy of abnormality identification, and providing a scientific basis for performance quantitative evaluation to avoid the one-sidedness of a single parameter. The present invention can accurately identify abnormal components based on the noise spectrum mapping relationship when it is determined that there is an abnormality in the smoothness of the guide rail glass lifting system, and at the same time locate the specific abnormal position by combining the current, vibration signal, and position-time mapping, thereby achieving dual positioning of component type and physical position, avoiding manual blind investigation, and improving fault diagnosis efficiency and maintenance targeting.

This invention discloses a visual positioning method for double-sided printing on cartons, relating to the field of carton printing technology. It addresses the problem that existing methods for verifying the post-printing position of cartons typically rely on manual inspection, which suffers from issues with accuracy and efficiency. This invention includes the following steps: S1: First, determine the inner and outer printing positions of the folded carton and mark the determined printing positions with feature points; S2: Then, verify and correct the marked printing positions. This invention prints customized patterns on the inner and outer sides of the carton before folding, meeting the needs of different customers. Simultaneously, it utilizes a visual positioning method to double-verify the printed patterns on the carton, correcting any abnormal positions, greatly improving the accuracy of the printed patterns. Furthermore, by analyzing the position and quality of the printed patterns, it can determine whether there are any abnormalities in the printing press or the carton clamping and positioning device and provide timely warnings.