6results about How to "Realize full coverage detection" patented technology

This invention belongs to the field of biological sample contaminant screening, specifically involving a pretreatment method for liquid chromatography-tandem mass spectrometry analysis of chemical contaminants in serum. The pretreatment method includes the following steps: (1) Preparation of Ti / Zr MOF: Zirconium source, 1,3,5-tris(4-carboxyphenyl)benzene, titanium source, and organic acid are placed in a container, dissolved in an aprotic polar solvent, heated to react, cooled, and the white powder product is collected by centrifugation and washing, which is Ti / Zr MOF, and dried for later use; (2) Extraction: The serum sample is mixed with acetonitrile, ultrasonically vortexed, centrifuged, and a portion of the supernatant is taken as the purification solution; (3) Purification: The purification solution in step (2) is mixed with the Ti / Zr MOF in step (1), ultrasonically vortexed, centrifuged, and the supernatant is taken to obtain the purified sample.

The application provides a tobacco data cleaning method and a storage medium. The tobacco data cleaning method comprises the following steps: S1, acquiring a to-be-cleaned tobacco data set; S2, performing visual analysis and detection processing on the to-be-cleaned tobacco data set to obtain a first detection result; S3, according to the attribute type of the to-be-cleaned tobacco data set, selecting a corresponding algorithm in a preset abnormal value detection algorithm set to detect abnormal data in the to-be-cleaned tobacco data set, and obtaining a second detection result; and S4, cleaning the to-be-cleaned tobacco data set according to the first detection result and the second detection result to obtain a target tobacco data set. Through the parallel joint detection of multiple algorithms, the accuracy and robustness of abnormal identification are significantly improved, global abnormality, skew distribution abnormality and relative deviation abnormality are fully covered, and the technical defects of high false judgment rate and high missed detection rate of traditional single detection algorithm are effectively solved.

This invention proposes an integrated pointer-type pendulum tunnel lining maintenance and inspection trolley, comprising a frame assembly; a load-bearing mechanism mounted on the frame assembly, on which a detection mechanism is installed for detecting defects in the tunnel lining; a maintenance mechanism also mounted on the load-bearing mechanism, configured to perform maintenance operations on the tunnel lining; and an electronic control system mounted on the frame assembly, controlling the mechanical movement, inspection, maintenance operations, and data transmission of the entire trolley; the load-bearing mechanism is equipped with an arc-shaped circumferential guide rail coaxial with the tunnel lining, on which a circumferential guide rail crawling system and a pointer-type pendulum system are installed. This invention enables automated control of maintenance and inspection, allowing rapid switching between inspection and maintenance modes, facilitating simultaneous self-inspection by construction units during the maintenance process.

The invention relates to the field of physical testing technologies and systems, and discloses a bridge steel structure defect nondestructive testing system based on infrared thermal imaging, which comprises an unmanned aerial vehicle integrated with an infrared thermal imaging detection device and a ground computing station. The unmanned aerial vehicle flies along a planned path, transient thermal excitation is applied through a thermal excitation device, and dynamic thermal response data of the surface of a pre-coated infrared coating is collected by synchronously utilizing an infrared thermal imager. And the ground computing station performs space-time fusion preprocessing on the returned data to obtain a standardized panoramic temperature field image, and inputs the standardized panoramic temperature field image into a convolutional neural network model which fuses physical mechanism constraints and has an online self-optimization function for intelligent analysis. According to the method, signals are enhanced through the coating, efficient inspection is achieved through the unmanned aerial vehicle, the recognition precision and generalization ability are guaranteed through the physical information fusion model, continuous evolution is achieved in combination with online self-optimization, and a high-sensitivity, full-automatic and quantitative bridge steel structure defect intelligent detection solution is formed.

This invention relates to a damage inspection device for double-layer fishing net aquaculture cages, comprising an inner cage frame, an outer cage frame, an inner net layer, and an outer net layer, with the inner and outer net layers enclosing multiple rectangular areas. A damage inspection device is installed within each rectangular area. Each damage inspection device includes a displacement drive system and a positioning detection device. The displacement drive system drives the positioning detection device to move along a planned route within the rectangular area. The positioning detection device includes a detection box, a detection roller assembly, a pressure sensor assembly, and a GPS positioning system. The detection roller assembly includes inner and outer detection rollers, with a distance L1 between the inner and outer detection rollers greater than the width L0 of the rectangular area, allowing the double-layer net layer to be stretched. This invention can detect the integrity of the fishing net based on the pressure signal between the net layer and the detection rollers, detect damage to the double-layer net layer in real time, promptly report the location and size of the damage, and facilitate repairs to effectively avoid economic losses.

The application relates to a target global domain recognition method based on ring domain convolution and belongs to the target recognition field in computer vision. A fisheye camera is used to collect a global target distortion image, and through data cleaning and labeling, a distortion image dataset is formed; a target three-dimensional field distribution is converted into an image two-dimensional pixel array, and a nonlinear mapping relationship between three-dimensional space points and distortion two-dimensional pixels is established; a distortion target recognition model based on a deep learning network is constructed, a deep learning network framework is optimized, a contradiction between translation invariance of a traditional deep learning network and radial symmetry adaptation of a fisheye image is relieved, and a precise mapping relationship between a two-dimensional pixel domain and target semantic features is established; the distortion image dataset is input into the target recognition model, model training and parameter optimization are carried out, and target global domain recognition is realized. While ensuring detection accuracy, the application reduces the calculation complexity and improves the real-time performance of target recognition.