464results about How to "Avoid missing detection" patented technology

The invention relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention relates to the technical field of engine cylinder surface defect detection, and discloses an engine cylinder surface defect detection method and system. The method comprises the following steps: acquiring three-dimensional height information of cylinder surface defect points based on a laser displacement sensor, identifying surface defects by combining an edge detection algorithm with a standard template library, calculating geometrical characteristics of the defects by fitting a polynomial and a least square method, and finally generating a defect report and feeding back the defect report to a central control system. Compared with a detection method in the prior art that fine defects on a complex curved surface are difficult to accurately identify, and especially in a data environment with large noise, accurate detection of micron-sized defects cannot be realized, the detection method has the advantages that noise is filtered through a high-precision laser displacement sensor and Fourier transform, so that the detection accuracy is improved, and the detection efficiency is improved. Precise detection and identification of surface fine defects are realized, so that the problems of missing detection and false detection in the prior art are avoided, and the detection precision and the production efficiency are improved.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses an OQC shipment inspection intelligent monitoring system based on AI, which belongs to the technical field of shipment inspection and is mainly composed of an intelligent image acquisition system, an AI defect detection system, a size measurement system, a data management and analysis system and a human-computer interaction interface. The intelligent image acquisition system is provided with a high-resolution high-definition camera and an intelligent light source controller, so that high-quality images are obtained, and complex component structures and fine surface features are clearly presented. The AI defect detection system uses a deep learning technology, especially a convolutional neural network model trained by mass data, to accurately and efficiently identify various defects. The dimension measurement system accurately measures the dimension of the component by means of a precision algorithm and a calculation module, and quickly judges whether the component is qualified according to the standard, thereby improving the detection reliability. And the data management and analysis system mines a quality rule through statistical analysis and data mining technologies, and supports decision making. The man-machine interaction interface is simple and visual in design, convenient to operate and clear in result display.

The invention relates to a composition for identifying bladder cancer based on urine sample detection and application thereof, and belongs to the technical field of molecular biology. According to the composition disclosed by the invention, the bladder cancer is identified by detecting the expression of one or more of hsa-miR-423-5p, hsa-miR-155-5p and hsa-miR-590-5p in a urine sample; the composition comprises a specific reverse transcription stem-loop primer, a specific detection forward primer, a specific detection probe and a universal reverse primer. According to the composition disclosed by the invention, the expression of hsa-miR-423-5p, hsa-miR-155-5p and hsa-miR-590-5p in a urine sample is detected, and a binomial logistic regression model is combined, so that auxiliary judgment is performed on the bladder cancer, the sensitivity and the specificity are relatively high, and the accuracy of identifying the bladder cancer can be improved. The invention further provides a kit containing the composition, the detection object of the kit is a urine sample, and the kit has the advantages of being small in sample dosage, noninvasive in sampling, convenient, rapid, small in risk and the like.

The invention discloses a water conservancy project dam detection method and device based on an unmanned aerial vehicle and a medium, belongs to the technical field of water conservancy project dams, and aims to solve the problems that an existing water conservancy project dam often depends on manual inspection, the detection efficiency is low, the labor cost is high, missing detection and misjudgment are likely to occur, and the detection efficiency is high. And intelligent monitoring and early warning of dam risks are not facilitated. The method comprises the steps of collecting a real-time detection image of a dam based on a preset unmanned aerial vehicle flight plan; performing risk identification processing related to dam main body characteristics on the real-time detection image to obtain dam main body characteristic information; through preset dam laser radar data, carrying out calculation processing related to dam appearance characteristics on the real-time detection image to obtain dam appearance characteristic information; and according to the reflectivity data of the preset multispectral image, carrying out index judgment processing on dam environment characteristics on the real-time detection image to obtain dam environment characteristic information.

The invention discloses a media file query and retrieval method and system based on an AI dialogue scene, the method combines a mixed mode of vector retrieval and keyword retrieval, realizes accurate retrieval of cross-modal data, and can process various media files including texts, pictures, videos, PPT and the like. According to the method, a double-round scoring mechanism of a large language model (LLM) is combined, firstly, the relevancy of file content is scored, then secondary scoring is carried out according to the matching degree of a file title and user query, and it is ensured that the file most relevant to query is called. In addition, the system can further utilize a user feedback mechanism to continuously optimize the retrieval precision, and the user experience is improved. Compared with the prior art, the method has the advantages that the accuracy and the efficiency of multi-modal data retrieval are remarkably improved, the limitation of a traditional AI dialogue system during multimedia file processing is solved, and the method is suitable for intelligent dialogue application scenes needing to quickly and accurately obtain various types of media files.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention relates to the technical field of surface defect detection, and discloses a gearbox gear defect detection method. The method comprises the steps of collecting a full-surface image of a gear, performing multi-layer feature extraction through image preprocessing in combination with a convolutional neural network, performing gridding segmentation and defect probability calculation through a Bayesian discrimination model, performing defect detection and classification on a defect-prone area and a defect-non-prone area by adopting a single-category model and a multi-category model respectively, and obtaining a defect-prone area and a defect-non-prone area. Generating a defect report; compared with the problems that in the prior art, manual detection is relied on, the speed is low, precision is insufficient, and particularly under the condition of a high-speed production line or uneven illumination, a traditional detection technology is difficult to effectively recognize small defects, real-time and high-precision gear surface defect detection is achieved by combining automatic detection based on deep learning, and the detection efficiency is improved. Therefore, the problems of missing detection and false detection in manual detection are avoided, the detection efficiency and accuracy are remarkably improved, and the method is suitable for a modern automatic production line.

The invention provides a cable insulation performance detecting and repairing device and method. The cable insulation performance detecting and repairing device comprises a cable laying device, a detecting mechanism, a repairing mechanism and a cable collecting device. The cable laying device is used for stably releasing a to-be-detected cable. The detection mechanism is used for detecting cable insulation performance and comprises a detection ring, a first driving device and an electric leakage detection device, and insulation defects are positioned by rotating the detection ring and acquiring electric leakage current signals; the repairing mechanism comprises a gluing mechanism and a drying box, the gluing mechanism is used for coating glue to the cable through the glue injection device and the gluing device after the damage point is detected, and the drying box accelerates repairing and solidifies the glue; and the cable take-up device is used for taking up the repaired cable. The automatic and cooperative work of all mechanisms in the whole device greatly improves the speed and quality of cable insulation performance detection and repair, avoids the problem of uneven repair or leak detection caused by misoperation or low efficiency in traditional manual operation, and remarkably improves the safety, reliability and construction efficiency before cable construction.

The invention discloses an intelligent low-altitude unmanned aerial vehicle cluster air-ground cooperative detection fire extinguishing system and method, a patrol plan is made by determining the patrol time interval of a patrol area of an unmanned aerial vehicle and the density of patrol routes, the system can dynamically adapt to the change of seasons and human activities, and the system and method are more accurate and more efficient. Fire missing detection can be effectively avoided; identifying a fire source by adopting a multi-source data fusion technology, determining whether the fire source exists in the forest region through the infrared image and the actual image of the forest region, and determining the intensity and the position of the fire source according to the smoke concentration when determining that the fire source exists in the forest region. The problem that the accuracy of infrared thermal imaging is affected and the detection precision is low due to the temperature difference of different vegetation, the change of the sunlight irradiation angle, water vapor in the atmosphere and the like can be solved. The fire trend prediction module and the fire extinguishing scheme generation module are adopted to automatically realize fire extinguishing of the forest area, the prediction result is efficient and accurate, and missing of the optimal fire extinguishing opportunity can be avoided.

The invention discloses a 110KV power transmission line unmanned aerial vehicle cluster inspection control method and system, and relates to the technical field of power systems and automatic control, and the method comprises the steps: generating an inspection region three-dimensional map through first data, and carrying out the distributed task distribution through a first algorithm; performing path search based on three-dimensional expansion of a second algorithm; and a third control algorithm is adopted to realize unmanned aerial vehicle cluster coordinated flight, and a dynamic window method is combined to realize real-time obstacle avoidance. According to the method, the flight path of the unmanned aerial vehicle can be more accurately planned through three-dimensional expansion of an existing path planning algorithm in combination with topographic data, and unnecessary flight time and energy consumption are reduced; through cluster collaborative operation, multiple sections of power transmission lines can be inspected at the same time, the inspection time is greatly shortened, the operation efficiency is improved, all corners of the data lines are fully covered, and the blind area or missing detection problem is avoided.

The invention discloses a dry-type transformer testing device, and relates to the technical field of testing devices, the dry-type transformer testing device comprises a workbench and a transformer main body, the transformer main body is installed on the workbench, the transformer main body is provided with a wiring terminal, and one side of the workbench is provided with a testing assembly for testing the transformer main body. According to the dry-type transformer testing device, when the brush is in contact with the outer wall of the wiring terminal and rotates, dirt, dust or other impurities on the outer wall of the wiring terminal can be effectively cleared away, through the clearing work, the insulation resistance tester body can check and measure the wiring terminal more accurately, and the testing efficiency of the dry-type transformer is improved. Therefore, potential connection problems, corrosion or abrasion and the like can be found easily, misjudgment or missing detection is avoided, and after interference factors of dirt and impurities are eliminated, the accuracy and reliability of a detection result can be improved.

The invention relates to the field of bridge detection, in particular to a bridge pavement layer damage detection method and system based on a ground detection radar. The method comprises the following steps: scanning a bridge pavement layer by using a ground detection radar, and collecting radar echo data; performing denoising and filtering processing to obtain a clear radar signal; judging whether a damage sign exists or not, if so, determining the radar signal as the damage sign, and entering the next step, otherwise, returning to the first step; waveform coding feature extraction is carried out on the detected damage signs, and damage features are identified; carrying out comparative analysis on the extracted damage features and a database in a normal state, and determining existence and types of the damage; according to an analysis result, utilizing an adaptive frequency modulation technology to accurately detect the damage depth and range; predicting a damage development trend; and obtaining a detection report according to the analysis result and the prediction result. The method improves the damage detection precision and detection efficiency, and achieves the accurate prediction of the damage development trend.

The invention relates to the technical field of corrugated pipe air tightness detection, and discloses an air tightness detection device and a detection method for corrugated pipe quality inspection.The air tightness detection device comprises a detection machine table, fixed installation assemblies are arranged at the two ends of the upper portion of the detection machine table, a movable supporting frame is arranged between the fixed installation assemblies, and a corrugated pipe is located above the movable supporting frame; the fixed installation assembly comprises a fixed installation mechanism and a sealed installation mechanism, the two ends of the corrugated pipe are connected with the fixed installation mechanism and the sealed installation mechanism correspondingly, and the pressurization assembly is arranged on one side of the sealed installation mechanism. An air supply mechanism is introduced into the sealed installation end of the corrugated pipe through an air inlet mechanism in the pressurization assembly, at the moment, one end of the corrugated pipe is compressed and fixed through a movable supporting frame, the end, close to the sealed installation end, of the corrugated pipe is in an extending state, and therefore all corrugations of the end, close to the sealed end, of the corrugated pipe can extend; airtightness detection is difficult to carry out on the area between the corrugations in the compressed state, and missing detection is avoided.

The utility model discloses a rapid visual defect detector which comprises a conveying frame, the side surface of the conveying frame is fixedly connected with a motor, the output end of the motor is fixedly connected with a driving shaft, the side surface of the driving shaft is fixedly connected with a driving tooth, the side surface of the driving tooth is rotatably connected with a chain, and the chain is connected with a motor. A driving roller is fixedly connected to the side surface of the driving shaft, a conveying belt is rotationally connected to the side surface of the driving roller, and a driven shaft is rotationally connected to the side inner wall of the conveying frame. According to the structure, through cooperation of the conveying belt, the containing frame, the gear table and the rack plate, cylindrical products are placed in the containing frame in order, toppling is avoided, meanwhile, blockage caused by product accumulation is avoided, missing detection can be effectively avoided, the gear table is matched with the rack, the cylindrical products rotate, all directions of the cylindrical products can be detected, and the detection efficiency is improved. And the lamp panel supplements light for the camera in the detection process, so that the situation that the detection result is affected due to the fact that a shot picture is black is avoided.

The invention discloses a real-time auricle identification and auricular point positioning method and system for multi-task feature sharing. An auricle identification and auricular point positioning combined model used in the method comprises an encoder and a decoder. The encoder includes a backbone network and a neck network. The decoder comprises a plurality of detection heads. A plurality of detection heads receive outputs of different layers of the neck network. The detection head comprises a target regression branch, a target classification branch and a feature point regression branch which are parallel. A plurality of detection heads share output features of the same encoder, and parallel target regression branches, target classification branches and feature point regression branches in the detection heads are respectively used for calculating loss functions of different tasks; therefore, auricle identification, frame selection and auricular point feature point marking tasks can be efficiently completed; on the basis, high-frame-rate real-time prediction is realized in the field of auricular point positioning for the first time, and the limitation of the prior art is broken through.

The invention provides a resource query method and device, equipment and a medium, relates to the technical field of artificial intelligence, can be applied to scenes such as cloud technology, artificial intelligence, smart traffic and aided driving, and comprises the following steps: acquiring an image text of a to-be-queried image, image features of the to-be-queried image and text semantic features of the image text; based on the image text and the text semantic features, performing resource recall in a preset resource library to obtain a recalled data set; based on the image text, the text semantic feature, the image feature and the resource feature data, generating a multi-dimensional association index between each pre-stored resource corresponding to the recall data set and the to-be-queried image; screening candidate resources from pre-stored resources corresponding to the recall resource set according to the multi-dimensional association index; and based on the multi-dimensional association index of the candidate resource, the resource feature data of the candidate resource and the image text, carrying out classification identification on the candidate resource to obtain a resource classification result. According to the invention, the accuracy of resource recall can be effectively improved.

The invention belongs to the technical field of electric performance testing of cookers, and particularly discloses an electric ceramic cooker electric performance testing system based on artificial intelligence, which comprises the steps of simulating and detecting electric operation data of an electric ceramic cooker under working conditions of different electric power steady-state loads and different transient loads; the performance of each component under different conditions can be known more comprehensively, so that the component with the abnormal electrical function can be identified; by simulating the electric power of the electric ceramic cooker under various cooking functions, the transverse heat conduction efficiency and the longitudinal heat conduction efficiency of the electric ceramic cooker under various electric loads are analyzed, heat conduction test indexes of the electric ceramic cooker are constructed, the transmission path and efficiency of heat in the electric ceramic cooker are reflected, and research and development personnel can be helped to carry out targeted optimization on the structure of the electric ceramic cooker; the function integrity index of the electric ceramic cooker is analyzed by detecting the cooling response condition of the electric ceramic cooker under the excessive temperature condition, it is ensured that the electric ceramic cooker can be cooled in time when the temperature is too high, and therefore internal elements are protected.

The invention discloses a porosity uniformity evaluation method and a detection device, and belongs to the technical field of material detection, the method comprises the following steps: collecting any one of the following ultrasonic detection data of a sample: first scanning based on an ultrasonic reflection contact method, third scanning based on an ultrasonic penetration method, and the porosity of the sample; calculating any one of the following evaluation indexes or a combination of the following evaluation indexes through the ultrasonic detection data: a first scan, a third scan and a uniformity coefficient of the porosity of the sample; and evaluating the uniformity of the sample through the evaluation index and the corresponding threshold value. The evaluation index is calculated for one or more ultrasonic detection data, and the uniformity of the sample or the reference block is quantitatively evaluated, so that the correctness and accuracy of the ultrasonic detection result of the porosity of the composite material workpiece are improved, missing detection and misjudgment are avoided, and the quality of the workpiece is ensured.

The invention relates to the technical field of remote maintenance of a power distribution cabinet, in particular to a remote maintenance system of an intelligent power distribution cabinet, which constructs a component operation parameter matrix by collecting current, voltage, component temperature, starting time and internal and external environment data of the power distribution cabinet in real time, and combines environment parameters of the power distribution cabinet to maintain the power distribution cabinet. And carrying out deep learning training by using a residual neural network algorithm. The method not only can detect common fault modes, but also can identify abnormal conditions caused by component aging, so that the problems of false alarm and missing alarm in a traditional fault detection method are solved. Through the residual neural network, the system can automatically learn normal and abnormal operation modes from historical data, and the accuracy of fault detection is improved. And the exception type is further pushed to the operation and maintenance personnel and is visually displayed, so that the judgment time and the round-trip times of the operation and maintenance personnel on the maintenance component are saved, and the maintenance efficiency is improved.

The invention provides a poor-perception-quality root cause analysis method, a poor-perception-quality root cause analysis device, a computer storage medium and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-modal data of a target object; a hypergraph structure for the target object is constructed according to the multi-modal data, the hypergraph structure comprises a plurality of initial nodes and a plurality of hyperedges used for connecting the initial nodes, and the initial nodes are used for representing a plurality of feature vectors obtained after feature extraction is carried out on the multi-modal data; the plurality of hyperedges are used for representing association relationships among the plurality of feature vectors respectively; and inputting the hypergraph structure of the target object into a pre-trained hypergraph neural network model to obtain a root cause analysis result of poor perceptual quality for the target object. The application range of root cause analysis on various types of environments can be widened, and the accuracy of root cause analysis results can be improved.

The invention discloses a wind shear and shear line identification method and system based on a laser radar in the technical field of radar detection, and the method comprises the steps: identifying all wind shear slopes from single radial wind speed data, and screening a stronger wind shear slope according to a wind shear intensity factor; and calculating a total wind shear value of each wind shear slope, comparing the total wind shear value with a wind shear grade criterion, and identifying a wind shear slope with a strong wind shear grade. Extracting all radial wind speed data from radar echo signals of a target area to obtain a significant wind shear slope in the whole target area, and performing clustering analysis on all wind shear slope shear points according to position coordinates of the wind shear slope shear points to form a plurality of clusters; and screening clusters conforming to shear line conditions, and performing curve fitting to obtain all shear lines. According to the invention, the position and intensity of wind shear in the target area can be effectively identified, real-time identification and early warning of the shear line are realized, and the timeliness and accuracy of weather detection and early warning are improved.

The invention discloses a magnetic bead for eliminating an anti-CD47 monoclonal antibody drug, a preparation method of a reagent and a detection method, the immunomagnetic bead comprises a carboxyl magnetic bead located in the immunomagnetic bead and a human CD47 recombinant protein wrapping the periphery of the carboxyl magnetic bead, and the particle size of the immunomagnetic bead is 100 nanometers; according to the method, only one round of adsorption is needed, the serum magnetic bead mixed solution can be placed on the magnetic frame for magnetic separation after full vortex mixing, interference can be completely removed, the time is very short, and detection can be completed within 1 min.

The invention belongs to the technical field of oil pipe monitoring, and discloses a cruise ship oil pipe oil spill monitoring system and method based on machine learning, and the system comprises a data obtaining module which is used for obtaining cruise ship oil pipe state monitoring data; the data processing module is used for preprocessing the acquired cruise ship oil pipe state monitoring data to obtain an oil pipe state comprehensive feature data set; the oil spill risk prediction module is used for training according to the oil pipe state comprehensive feature data set to obtain an oil spill risk assessment model, and predicting an oil pipe oil spill risk rate based on the oil spill risk assessment model; judging whether the cruise ship oil pipe has an oil spilling risk or not according to the oil pipe oil spilling risk rate; if the cruise ship oil pipe has the oil spilling risk, the oil spilling early warning module carries out graded early warning on the oil spilling risk and triggers a corresponding alarm instruction; the oil spilling source tracking module is used for acquiring oil pipe space position data and positioning an oil spilling position in combination with oil pipe video monitoring data; and comprehensive monitoring and efficient management of the cruise ship oil pipe state are realized.

The invention provides an e-commerce product compliance detection method and system based on platform policy global matching, and the method comprises the steps: receiving product graphic and text information of a target product uploaded by a user, detecting a setting range, and receiving a triggering operation of a compliance detection control of a product policy compliance detection interface; sending a policy compliance detection request message to the first cloud agent; receiving a product sales compliance category from the first cloud agent, detecting that the product sales compliance category is a forbidden product or a limited product, creating a root cause detection request message, and sending the root cause detection request message to the first cloud agent; and receiving policy root cause description information from the first cloud agent, and displaying the policy root cause description information on a product policy compliance detection interface. Therefore, it can be ensured that the target product passes detection of each policy related to the product, the corresponding violation behaviors and policy reasons are output, and comprehensiveness and accuracy of compliance detection are improved.

The invention provides a capsule endoscopy path prediction method and device based on multi-modal data fusion, and belongs to the technical field of medical equipment.The method comprises the steps that body position time sequence data of a patient are collected in the current time period through a plurality of inertial measurement units (IMUs), collecting time sequence image data of the digestive tract of the patient in the current time period through a capsule endoscopy; preprocessing the body position time sequence data and the time sequence image data to obtain preprocessed body position time sequence data and preprocessed time sequence image data; and inputting the preprocessed body position time sequence data and the preprocessed time sequence image data into a pre-trained prediction model to obtain a capsule endoscopy path prediction result output by the prediction model and a body position adjustment prompt of the patient. According to the method, the accuracy of capsule endoscopy path prediction is improved, and missing detection can be effectively avoided.

The invention relates to the technical field of aero-engine detection, in particular to an aero-engine blade tracking and counting method, which comprises the steps of aero-engine blade tracking and aero-engine blade counting: step 1, adopting a blade tracking algorithm based on a TBD framework to realize aero-engine blade tracking, comprising: step 1, target detection; 2, motion modeling and state estimation; 3, association matching; step 2, counting the aero-engine blades based on the trajectory information calculated in the step 1: step 1, identifying newly appearing aero-engine blades; 2, judging the rotating direction of the newly-appearing aero-engine blade and recording the starting direction of the newly-appearing aero-engine blade; and 3, updating the current count value according to the movement direction of the aero-engine blade. According to the invention, the problem of missing detection in the manual blade counting process can be solved, the problem of repeated detection of aero-engine blades by workers due to missing detection is solved, and the working efficiency of the workers is improved.

The invention discloses a large-size ceramic sheet wall surface paving quality detection device, and relates to the technical field of building detection, the large-size ceramic sheet wall surface paving quality detection device comprises a shell, a plurality of rollers are rotatably mounted at the bottom edge of the shell, and a knocking assembly for knocking a ceramic plate is arranged at one end of the inner wall of the shell; a sound wave analysis assembly used for analyzing knocking sound waves is arranged above the knocking assembly, and a track identification assembly is arranged on one side of the knocking assembly; according to the device, manpower consumption in the process of manually pushing the device to move is reduced in a roller moving mode, when the device moves on the surface of a ceramic tile, marking rollers rotate synchronously, then ink absorption gaskets can make contact with the surface of the ceramic tile at intervals, marks are left, marking of the pushing movement track of the device is achieved, and the efficiency is improved. An operator can visually know and distinguish a detected area and an undetected area, so that missing detection and repeated detection are avoided, the detection efficiency is improved, and manpower is saved.