30results about How to "Efficient and accurate detection" patented technology

The utility model relates to the technical field of image acquisition, in particular to a chip glass substrate image data acquisition device. Comprising a longitudinal movement guide rail, a sample bearing base plate is arranged above the longitudinal movement guide rail in a sliding mode, a transverse movement guide rail is arranged above the sample bearing base plate, and a first detection moving rail and a second detection moving rail are fixed to the front side of the transverse movement guide rail; a first sensor is fixed to the front side of the first detection moving rail, and a second sensor, a third sensor and a fourth sensor are fixed to the front side of the second detection moving rail. According to the device, the chip glass substrate can be automatically detected, and the situation that the through hole of the glass substrate is abnormal and blocked can be identified.

The application discloses an aggregation-induced emission metal organic framework, a preparation method thereof and fluorescent / colorimetric dual-mode intelligent detection for trace HCl. 2+ The aggregation-induced emission metal organic framework is prepared by providing Zn(NO3)3.6H2O as Zn 2+ , imidazole-2-formaldehyde and an AIE molecule HDBB as organic ligands, and through a solvothermal method to obtain ZIF-90-HDBB, which can change the aggregation state fluorescence from yellow green to orange red after being contacted with HCl gas; the ZIF-90-HDBB is uniformly loaded on cellulose filter paper to obtain a paper-based sensor for portable detection of HCl, and through combination with a color recognition APP of a smart phone, the HCl can be rapidly and sensitively detected on site in a fluorescent / colorimetric dual mode. Since the HDBB molecule is prone to protonation under acidic conditions, the fluorescence color changes, and therefore the material can be accurately and efficiently used for detection of acidic gas, and the material can be recycled.

The invention provides a clothes defect detection method and system based on multi-spectrum characteristic harmonic complex, and relates to the technical field of image detection. The method comprises the following steps: establishing a multi-spectrum characteristic harmonic aggregation complex defect detection model and carrying out pre-training, wherein the multi-spectrum characteristic harmonic aggregation complex defect detection model comprises a spectral domain frequency division network, a spectrum harmonic aggregation network, a boundary spectrum complex network and a target detection output layer; inputting a to-be-detected image in the original clothing image data into the multi-spectrum feature harmonic congelation complex defect detection model, and processing the to-be-detected image by using a spectral domain frequency division network, a spectrum harmonic congelation network and a boundary spectrum complex network in sequence to obtain feature representation with enhanced edge perception capability, and performing target detection and classification on the feature representation through a target detection layer, and outputting a defect detection result of the to-be-detected image. The problems that in the actual production process, the garment flaws are difficult to recognize, and the manual detection efficiency is low are solved, and efficient and accurate detection of the garment flaws under the actual engineering application scene is achieved.

The invention discloses a fence structure-oriented small target double-stage detection and defect assessment method, which comprises the following steps of: inputting preprocessed RGB image data of a fence into a trunk convolution module of a defect risk assessment model to obtain first feature maps with different scales, fusing the first feature maps with polarization physical feature maps of the fence respectively, and obtaining a defect risk assessment result through a plurality of FPN modules; performing feature fusion on the fused feature map by adopting a cross-level feature fusion strategy and a cascade connection strategy, and sequentially outputting a fused second feature map to a channel attention module, a space attention module, a texture attention module and a region candidate module to obtain a candidate box set; when a candidate frame meeting the image reconstruction requirement exists, candidate frame reconstruction is carried out through an image super-resolution reconstruction module, RGB image data and a polarization physical feature map, then a bounding box, a defect category and a pixel mask are determined through an RoI feature processing module and a defect detection and segmentation module, and defect risk assessment information is obtained in combination with a geometric model of a fence.

The utility model provides an environment monitoring device for tunnel safety, which comprises a shell, an air inlet flow channel, a semiconductor detection unit, a control panel and a power supply module, the outer wall of the air inlet flow channel is in a circular truncated cone shape and can gather and accelerate air flow, so that the semiconductor detection unit can obtain gas data more efficiently and accurately, and the detection efficiency and sensitivity are improved; in the filter piece connected with the air inlet flow channel, the filter screen can intercept impurities, pollution to the detection unit, influence on result accuracy and reduction of element loss are avoided, and the threaded connection of the connecting cylinder facilitates maintenance; the anti-vibration pad at the bottom of the shell supports the air inlet channel, external vibration influence is reduced, looseness of internal components is reduced, and stable work of the device is guaranteed; the warning lamp is electrically connected with the controller and the like, and warning signals can be sent out in time when abnormity is detected, so that rapid processing is facilitated, and the early warning capacity is improved. According to the device, efficient and accurate detection, stable operation and timely early warning are realized, and the tunnel safety monitoring level is improved.

The application belongs to the technical field of transistor detection, and specifically relates to a field effect transistor quality detection system and equipment thereof, wherein the system comprises a transistor scanning module, a structure reliability analysis module, a signal excitation generation module, an intelligent feature analysis module, a transistor quality evaluation decision module and an interactive alarm end; the structure reliability analysis module is used for analyzing a transistor image to evaluate the structure reliability of the field effect transistor, a plurality of excitation signals suitable for field effect transistor detection are generated when a structure reliable signal is generated, the intelligent feature analysis module collects a plurality of electrical parameters and non-electrical parameters of the field effect transistor under signal excitation and performs in-depth analysis, the field effect transistor is comprehensively and objectively evaluated in quality according to an analysis report and in combination with a preset quality standard and an evaluation model, comprehensive, efficient and accurate detection of the field effect transistor is realized, and the quality detection level and production efficiency are improved.

This application discloses a method, system, and computer program product for detecting the length of back-drilled residual piles. The method involves injecting a test signal into the metallized through-hole of the back-drilled structure under test, obtaining the first time-domain difference between the test signal and the first reflected signal, and then measuring the length of the residual pile in the back-drilled hole under test. This achieves efficient and accurate measurement of the length of back-drilled residual piles in high-frequency, high-speed printed circuit boards, effectively improving the accuracy and repeatability of back-drilled residual pile measurement. It also facilitates automated online detection, effectively improving detection efficiency, reducing human error, and meeting the requirements of large-scale production of high-frequency, high-speed printed circuit boards.

The utility model discloses a machine vision detection device for welding seam quality, which relates to the field of welding seam detection and comprises a supporting bottom plate, bottom cushion blocks are symmetrically and fixedly connected to the bottom surface of the supporting bottom plate in a threaded manner, a horizontal top groove is horizontally formed in the top surface of the supporting bottom plate, and supporting cushion blocks are butted to the top surface of the supporting bottom plate. A clamping top groove is horizontally formed in the top face of the supporting bottom plate, a conveying motor is fixedly arranged on one side edge of the supporting bottom plate, a telescopic vertical rod is in butt joint with the top face of the supporting bottom plate in the vertical direction, a bottom transverse rod is horizontally and fixedly connected to one side edge of the telescopic vertical rod, and an end fixing box is fixedly connected to one end of the bottom transverse rod. A horizontal connecting rod is horizontally arranged on one side edge of the telescopic vertical rod in a butt joint mode, a vertical connecting rod is fixedly connected to the bottom face of the horizontal connecting rod in the vertical direction, a multi-direction adjustable structure is adopted so that the multi-point welding seam detection device can be used for multi-point welding seam detection, and detection of the multi-point welding seam detection device can be more accurate and efficient under mutual cooperation visual detection of the upper face and the lower face.

The embodiment of the invention provides a pot detection method of a cooking utensil, electronic equipment, a storage medium and a program product. A pot detection pulse signal is sent to the electromagnetic heating assembly, and whether a magnetic conduction material pot is placed on the electromagnetic heating assembly or not is judged according to the number of oscillation pulse signals generated by a resonance circuit of the electromagnetic heating assembly; or the resistance wire heating assembly is started, and whether the cookware is placed or not is determined according to the detected temperature; if the cookware made of the magnetic conductive material is not placed on the electromagnetic heating assembly and the cookware exists, it is determined that the cookware made of the non-magnetic conductive material is placed on the electromagnetic heating assembly. According to the method, through self-adaptive identification of the type of the cookware, the problem that the user needs to manually select the cookware after placing the cookware is solved, convenient, efficient and accurate cookware detection is realized, and the cooking experience is improved.

The present application relates to a kind of equipment appearance and environment anomaly detection method for offshore wind farm booster station intelligent inspection, first by reversible column main network RCN processing complex feature, then by Neck Multi-scale feature fusion is carried out, finally using SA Head generates final detection result: including the class of target, boundary box coordinate and confidence degree.And in order to better solve the influence of low-quality example to model, a kind of dynamic non-monotonic focusing method Wise-IoU is used in network training process.The present application can realize the accurate and efficient detection of equipment ontology and surrounding environment anomaly in offshore wind farm booster station, find the abnormal change of equipment ontology and surrounding environment, can provide direct and important basis for preventive maintenance of offshore wind farm booster station.

This invention discloses an automatic detection device and method for longitudinal axial flow grain loss, comprising a material conveyor belt, a separating axial flow mechanism, a seed receiving tray, a double-layer vibrating screen, a cleaning fan, a grain conveyor belt, a weighing device, a sieve box, an axial flow stepless speed regulator, a power and transmission unit, a work area, a driver's cab, and an operating system. The material conveyor belt is located at the front end of the device and is connected to the interior of the separating axial flow mechanism. Below the separating axial flow mechanism, from front to back, are arranged the seed receiving tray, the double-layer vibrating screen, and the sieve box. The cleaning fan is located below the seed receiving tray, the grain conveyor belt is located behind the cleaning fan, the weighing device is located below the grain conveyor belt, and the axial flow stepless speed regulator is located at the rear end of the separating axial flow mechanism and is connected to the separating axial flow mechanism via a spline. This automatic detection device and method for longitudinal axial flow grain loss employs an intelligent driving and operating system to achieve accurate and efficient detection of grain loss across multiple crops and working conditions, with full mechanization and automation.

The utility model discloses deep groove ball bearing producing and machining equipment which comprises a cleaning mechanism, a detecting mechanism and an assembling mechanism. The cleaning mechanism is matched with a spraying device through the cavitation effect of an ultrasonic generator, deep cleaning can be conducted on the inner ring, the outer ring and ball gaps of the deep groove ball bearing, oil stains and chippings are effectively removed, and sewage is discharged through a liquid outlet after cleaning. The detection mechanism synchronously detects multiple parameters such as the size precision, the surface roughness and the roundness of the bearing by means of a multi-parameter detection probe, detection data are transmitted to the data processing system to be analyzed, and whether the bearing is qualified or not is rapidly judged. An automatic feeding device of the assembling mechanism conveys parts, and an assembling mechanical arm is matched with a positioning clamp to accurately complete assembling of all parts of the bearing. All the mechanisms of the equipment work cooperatively, and the production and machining efficiency, quality and assembly stability of the deep groove ball bearing are improved.

This invention discloses a SNP molecular marker associated with mung bean resistance to the bean weevil and its application in genetic breeding. The problem this invention aims to solve is how to identify mung bean resistance to the bean weevil. The SNP molecular marker is a locus (SNP site) on mung bean chromosome 4, with a nucleotide type of T or C, located at nucleotide position 164 of sequence 3 in the sequence listing. This invention provides the application of substances for detecting polymorphisms or genotypes of SNP sites in the mung bean genome in identifying or assisting in the identification of mung bean resistance to the bean weevil. This invention can be used to assist in the identification of mung bean resistance to the bean weevil, for early screening of mung beans, and for marker-assisted breeding of mung beans.

The utility model discloses a research experiment device used for oil field fracturing technology, relates to the oil field fracturing field, and comprises a support bottom plate, the bottom surface of the support bottom plate is in threaded fixed connection with a bottom cushion block, the top surface of the support bottom plate is vertically and fixedly provided with a fixed top plate, one side edge of the fixed top plate is fixedly clamped with a touch control panel, and the touch control panel is fixedly clamped with the bottom cushion block. A fixed box body is horizontally and fixedly connected to the top surface of the supporting bottom plate, pressure sensors are horizontally, uniformly and fixedly connected to one side edge of the fixed box body, top through holes are uniformly formed in the top surface of the fixed box body, and sealing rubber rings are fixedly clamped to the inner side edges of the top through holes; the top surface of the supporting bottom plate is vertically and fixedly connected with a telescopic vertical rod, one side edge of the telescopic vertical rod is horizontally and fixedly connected with a fixed top rod, an integrated structure is adopted, so that control and use are more convenient and efficient, and meanwhile, detection is more accurate and efficient under the condition of a partitioned independent control detection structure.

This invention relates to the fields of molecular biology and genetic breeding, and particularly to the dCAPS marker associated with the length of adventitious roots in wheat under waterlogging stress and its application. This dCAPS molecular marker is based on the 234,677,747th base of wheat chromosome 6B; the SNP base difference is either C or T. This molecular marker is accurate and efficient to detect, convenient and stable to amplify, and can be used for marker-assisted selection to improve the efficiency and accuracy of identifying the waterlogging stress resistance of wheat plants.

This utility model discloses a copper bar screw fastening device, including a copper bar positioning and fastening detection mechanism, a copper bar material handling mechanism, a screw fastening mechanism, and a defective product recycling mechanism. The copper bar positioning and fastening detection mechanism includes a fixture seat, a screw height detection unit, and an appearance detection unit. The fixture seat achieves precise positioning of the copper bar through a lifting cylinder, clamping blocks, and springs. The two detection units respectively detect screw height and appearance abnormalities. The copper bar material handling mechanism consists of a horizontal movement module, a lifting module one, and a clamping claw module, realizing automatic copper bar transfer. The screw fastening mechanism includes a lifting module two and a screw fastening unit, driving the fastening unit to lift and lower to complete the fastening. The defective product recycling mechanism drives a recycling box to collect defective products through a transfer module. This device integrates precise positioning, automatic detection, material handling, and defective product recycling functions, improving fastening quality and efficiency, and solving problems such as inaccurate positioning and inefficient detection in traditional technologies.

This utility model discloses a portable rapid eutrophication detector, including a detection box. A detection port is opened on the top surface of the detection box, and a movable platform is fitted into the detection port. A detection probe is installed on the bottom surface of the movable platform. Movable discs are installed inside the detection box, and a base plate is fixed between two movable discs. A sample container and a calibration container are respectively fixed on the top surface of the base plate. A cleaning hole penetrates through the center of the base plate, and the detection probe is movably connected to the cleaning hole. A storage cavity is opened at the bottom of the base plate. A cleaning box is fixed to the bottom surface inside the detection box, and a spray head is arrayed on the top surface of the cleaning box. A display panel is installed on the front of the movable platform. The automatic detection method achieves efficient and accurate detection, greatly improving the efficiency and reliability of eutrophication monitoring. After detection, the container is automatically rotated 180 degrees to automatically discharge waste liquid, while the high-pressure spray head rinses the inner wall of the container to prevent cross-contamination.

This invention provides a method, apparatus, and device for detecting the physicochemical parameters of oil, relating to the fields of spectral analysis and industrial equipment condition monitoring. The method includes: acquiring hyperspectral data of the oil to be tested; inputting the hyperspectral data into a pre-trained spectral decoupling network to output a base spectral component related to the oil brand, and a common component feature vector unrelated to the oil brand but related to the oil's health status; matching the common component feature vector with a pre-built general reference library to determine the physicochemical parameter values ​​of the oil to be tested. The method of this application achieves efficient and accurate detection of physicochemical parameters of oils from multiple brands, breaking the limitation of "one model per brand," effectively improving the accuracy of the detection results and the generalization ability across brands, significantly reducing the deployment and maintenance costs of the detection system, and significantly improving the practicality of multi-brand oil condition monitoring.

Systems and methods are provided that include obtaining an image providing information of at least a portion of at least one structural element of a semiconductor sample, generating one or more filters using the image, performing a comparison between each given region of a plurality of regions of the image and the one or more filters, and determining a set of points of the image based on the comparison, the set providing information of an estimated position of at least one edge of at least one structural element in the image, the set of points for each given row of a plurality of rows of the image or for each given column of a plurality of columns of the image, at least one given point of the given row or the given column of the image is included.

The utility model discloses a cold rolling die detection device which comprises a line drawing support and a detection support which are located at a discharging end, an installation cylinder is arranged on the line drawing support and located above a finished steel pipe extending out of the discharging end, a marking pen is arranged in the installation cylinder, and a pen point of the marking pen penetrates through the bottom of the installation cylinder. The finished steel pipe rotatably extends out of the discharging end to enable the marking pen to draw a continuous spiral line on the surface of the finished steel pipe, the detection support is provided with a rotating head capable of rotating to adjust the angle, the rotating head is provided with a camera, and the camera faces the finished steel pipe to detect the spiral line on the surface of the finished steel pipe. Whether the spiral line is abnormal or not is detected through the visual detection function of the camera, if the spiral line is abnormal, an abnormal signal is directly transmitted to the controller of the cold pressing equipment, shutdown is achieved, workers do not need to stare at all the time, the labor intensity is greatly reduced, and detection is more efficient and accurate in a visual detection mode.

This disclosure provides a method, model training method, and system for detecting energy consumption anomalies in industrial scenarios. The method includes: acquiring several sets of sample energy consumption training data for target energy-consuming objects in a target industrial scenario; the sample energy consumption training data includes sample energy consumption feature data and corresponding sample energy consumption anomaly annotation data; training a preset random projection model based on the sample energy consumption feature data in each set of sample energy consumption training data as input and the corresponding sample energy consumption anomaly annotation data as output to obtain an energy consumption anomaly detection model; wherein, the preset random projection model uses a random hyperplane for recursive segmentation. In this disclosure, by comprehensively considering multi-dimensional energy consumption correlation data in the scenario, fused features are obtained through a stacked autoencoder network, and then combined with the preset random projection model for joint modeling, to achieve efficient and accurate detection of energy efficiency anomalies in the target industrial scenario, enabling timely intervention and processing of energy consumption anomalies.

The present application belongs to the technical field of pest area detection specific computer model, more specifically, it relates to a corn leaf pest area detection method based on YOLOv8. The method comprises the following steps: constructing a data set; preprocessing the corn pest area pictures of the data set; using a lightweight annotation tool "labelme" for polygon annotation, and dividing the pest area into three categories; inputting the preprocessed corn pest area pictures into the backbone network of the Poly-YOLOv8 pest area detection model for feature extraction, then inputting the feature fusion through the neck network, and finally inputting the improved decoupling detection head to generate the detection result; and performing real-time monitoring on the corn pest area through the trained and tested Poly-YOLOv8 pest area detection model. The present application solves the problem that the prior art ignores the detection of the pest area on the leaf, the pest area detection based on instance segmentation cannot effectively locate the leaf boundary, and thus the accuracy is insufficient.

This invention discloses a steel structure component torsion testing device, including a support frame, a movable mounting plate on top of the support frame, a rotatable vertical rod at the bottom of the mounting plate, a clamping rod rotatably connected to the bottom of the connecting rod, a connecting block on the outside of the vertical rod, a piston cylinder communicating with a circular groove fixed on the connecting block, a movable piston slidably connected inside the piston cylinder, a horizontal rod fixed on the movable piston, and a laser rangefinder fixed to the end of the horizontal rod. This invention provides two testing methods: horizontal linear movement and rotation, which can be used individually or in combination. The former is suitable for torsion testing of a single horizontal cross-section of the component, while the latter is suitable for overall torsion testing of the component. Simultaneously, the travel distance, rotation range, and testing position of the laser rangefinder can be flexibly adjusted to adapt to steel structure components of different sizes and testing locations, offering strong testing coverage and applicability.

The utility model relates to fire detection equipment technical field and disclose a kind of comprehensive pipe gallery wisdom management fire detection device, including fire alarm control host, the inner surface of both sides vertical plate of fire alarm control host is fixedly installed with air inlet grille, wherein, protection mechanism is provided in fire alarm control host, the comprehensive pipe gallery wisdom management fire detection device, when abnormal situation such as fire occurs, plc electric control board controls motor to start, motor drives fixed rod to rotate, make collar slide along sliding roller, and then drive vertical plate to move, finally make baffle rotate, realize the shielding or opening of air inlet grille, this mechanism can adjust ventilation according to actual situation, both can prevent fire spread when smoke or fire through air inlet grille into device internal and cause harm to its internal electrical components, also can protect equipment safety, can also ensure ventilation when necessary, maintain normal operation of device.

The present application provides a mental state detection method based on cross-frequency coupling and band attention mechanism, relates to the technical field of electroencephalogram signal processing, and comprises the following steps: preprocessing electroencephalogram data and decomposing the electroencephalogram data into a high-frequency band range and a low-frequency band range, and respectively performing sub-band selection; calculating a phase-amplitude coupling matrix for the multi-channel decomposition result of each sub-band; calculating the attention weight of each pair of sub-bands, and aggregating and convolving features to obtain a unique representation of each band coupling feature; performing multi-scale feature extraction, capturing coupling features with high contribution in different receptive fields and performing feature enhancement processing; a mental state classification network initial model compresses the coupling features after the feature enhancement processing, reduces the dimension of the coupling features, obtains new feature representations, and performs classification training based on the new feature representations through a plurality of full connection layers to obtain an optimized mental state classification network model. The present application can effectively improve the accuracy of the classification model in mental state detection.

The application discloses a feeding device for bamboo product processing and relates to the technical field of bamboo product processing. The feeding device comprises a feeding fixing frame, an adjustable guide plate is fixedly arranged in the feeding fixing frame, burr detection films are arranged on the two sides of the adjustable guide plate, and a contact image sensor is arranged on one side of the burr detection film. The feeding device has the following advantages: the bamboo product is controlled to produce displacement in four directions in a plane during feeding, the contact image sensor detects the surface of the burr detection film during displacement of the bamboo product, burrs of the bamboo product are analyzed and verified according to the detection result, the bamboo product that does not meet the standard is selected according to the analysis result, and the burrs of the bamboo product are prevented from causing harm to the operator during subsequent processing. The burrs of the bamboo product are detected during feeding of the bamboo product, and thus the preceding process of production and processing can be inspected, and the operation safety of the subsequent process is ensured.

The utility model provides a kind of laminated member misplacement detection device, laminated member production system, it is related to photovoltaic technical field to solve laminated member misplacement phenomenon difficult to be efficiently detected technical problem.The laminated member misplacement detection device, including pedestal, conducting part and the first stop piece and the second stop piece of relative arrangement, along preset direction, first stop piece, second stop piece are slidably arranged on pedestal;Conducting part is arranged on first stop piece;In preset direction, second stop piece has insulating section and at least one conducting section, and switch circuit is formed between conducting part and second stop piece;When the laminated member of detection is in misplacement state, the end of conducting part away from first stop piece and conducting section abut;When the laminated member of detection is in alignment state, the end of conducting part away from first stop piece and insulating section abut.The laminated member misplacement detection device can be efficiently detected, reduce the probability of misplacement laminated member flowing into normal component process.

The application discloses a piece cigarette appearance detection method and system based on machine vision, and relates to the technical field of logistics and warehousing. The method adopts technical means such as image preprocessing, target detection network, morphological processing, D-S evidence theory fusion, convolutional neural network classification and evaluation, and structured report sorting, and realizes efficient and accurate detection of the appearance quality of piece cigarettes. Specifically, a set of preprocessed images is obtained through multi-view image acquisition and preprocessing; surface defect areas and packaging defect areas are identified through a YOLOv8 lightweight backbone network and channel attention weighting; a barcode recognition result is obtained through morphological processing and multi-direction decoding; an abnormal piece cigarette mark is output through D-S evidence theory algorithm execution uncertainty fusion; a defect category and a barcode quality score are obtained through a convolutional neural network and a sequence recognition network; and a detection report sorted according to quality risks is obtained through structured report generation and sorting.

The invention discloses a GIS isolation switch dislocation defect live-line detection method and system based on ultrasonic detection, and belongs to the technical field of electrical equipment state detection, and the method comprises the steps: S10, obtaining related data of a target GIS isolation switch, the related data comprising normal ultrasonic signals of each key point of the GIS target isolation switch; the key point location comprises a mechanical connection and transmission part, an electrical connection and conductive part and an insulating and supporting part; s20, a signal library about normal ultrasonic signals is constructed, at least two key point positions are selected from the signal library to serve as reference objects, and the reference objects comprise a connection and transmission part and an electrical connection and conduction part. According to the method, efficient and accurate detection of the displacement defect of the GIS disconnecting switch is realized by constructing the normal ultrasonic signal library, marking and collecting the signal features of the key point locations, analyzing the change of the feature rule and setting a dual-stage verification mechanism, and the method is beneficial to improving the operation safety and the operation and maintenance efficiency of equipment.