46results about How to "Accurate identification and detection" patented technology

The invention provides a flash malicious file detection method, which includes the steps: analyzing a flash file, and extracting virtual machine byte codes from the flash file; and matching the virtual machine byte codes with characteristic byte codes, making statistics on occurrence times of the virtual machine byte codes if the virtual machine byte codes can be matched with characteristic byte codes, and determining the flash file as a malicious file if the times exceed a threshold value. The invention further provides a flash malicious file detection device for implementing the method. By the aid of the flash malicious file detection method and the flash malicious file detection device, high detection efficiency and high detection precision are achieved.

The invention discloses a control system of a multifunctional man-machine interaction humanoid teaching robot. The system comprises a remote monitoring module, real-time protective modules, a speech module, a mobile phone APP module, a robot movement module, a wireless communication module, a steering engine integration module and a control module, the modules are connected with an embedded controller through wires for real-time data exchange, the remote monitoring module is located on the head of the robot, the real-time protective modules are located on the shoulders of the robot, the wireless communication module is located on the chest of the robot and composed of three communication module bodies, robot palms are arranged at the tail ends of robot arms, the chest of the robot is supported on robot feet through robot legs, and the robot movement module and the steering engine integration module receive control instructions sent by the control module. The biological humanoid teaching robot is multifunctional, richer in man-machine interaction and high in environment adaptability, and the adaptability range to people of different ages is wide.

The invention discloses a control method suitable for an SMD extensible intelligent warehousing system. The control method can be used for achieving a function of simultaneous storage and takeout of multiple SMD trays and adopts an RFID reader-writer and bar code scanning dual management recognition system to achieve recognition, scanning and information input of the trays. Full-automated management and material storage and takeout are achieved, inventory information is automatically collected, and the inventory management cost is effectively reduced. A storage space with large storage volume, good leakproofness and excellent storage environment is provided. The expansibility is higher, and each unit consists of multiple tray frames and is provided with an expansion interface for facilitating warehouse scale enlargement. By means of the control method, intelligent storage and management of the SMD trays can be efficiently achieved, and manpower and material costs are greatly saved.

The present invention relates to an information entropy-based snowflake noise detection method. The overall distribution state of the gray values of pixels is obtained; a previous image frame and a subsequent image frame are converted into a gray space; the gray values of all the pixels are clustered into n intervals, and the distribution state of the gray values of the pixels is displayed in the form of a grey level histogram; the information entropy of the previous image frame and the subsequent image frame are calculated; and whether snowflake noises exist in a video stream is judged according to the change amplitude of the information entropy of the previous image frame and the subsequent image frame. According to the method of the invention, when an actual calculated value is not smaller than a set threshold value, and the value of the subsequent image frame is larger than value of the previous frame, or when the actual calculated value is smaller than the set threshold value, while, the previous frame is marked as a noise frame, it can be finally determined that snowflake noises actually exist, otherwise, it is considered that the images are normal images. The method of the invention has high accuracy in video snowflake noise detection and also lays a good foundation for snowflake noise elimination for obtaining a better visual effect.

The invention discloses an intelligent garbage classification device, comprising a garbage classification unit and a garbage identification unit mounted on the garbage classification unit. The garbage identification unit comprises an image preprocessing module, an image extreme-point detection module, an image feature-point positioning module, a principal direction determination module and a feature extraction module. From all extreme points, the image feature-point positioning module determines extreme points as feature points by rejecting noise-sensitive low-contrast points and unstable edge points. The principal direction determination module is used to connect any two adjacent peak values in a gradient direction histogram about the feature points so as to form many sub line segments, to merge, in the length direction, the adjacent sub line segments with similar slopes to form a line segment, and to take the direction of the optimal line segment among the many line segments as the principal direction of the feature points. The invention has the advantages of accurate classification and high speed.

The invention relates to the technical field of biology, in particular to a reagent which can simultaneously distinguish and detect four animal insect-borne diseases as well as a preparation method and application of the reagent. The animal insect-borne disease multi-RT-PCR distinguishing and detecting reagent comprises four pairs of specific primers, and the respective amplification target fragment lengths of Bluetongue virus (BTV), epizootic haemorrhagic disease virus of deer (EHDV), Vesicular stomatitis virus (VSV) and Akabane virus (AKV) are 351bp, 536bp, 300bp and 250bp. The VP7 of the BTV, the EHDV, the VSV and the AKV and a conservative fragment of an N gene are respectively selected as targets, and the primer Express software and the primer prere 5.0 software are applied to deign and combine a primer. An optimal matching and screening test and a multi-RT-PCR test are carried out on a plurality of pairs of designed primers, and four pairs of primers which can carry out the distinguishing and detection on the four animal insect-borne diseases and have high amplification efficiency and good specificity can be obtained by a plurality of reaction condition optimization and comparison tests and verification tests. A kit formed by the reagent can obtain a qualitative distinguishing and detecting result in six hours after a sample is received. The multi-RT-PCR distinguishing and detecting reagent is a sensitive and reliable method for detecting the BTV, the EHDV, the VSV and the AKV in the clinical sample.

The invention discloses an intelligent air purifier. The intelligent air purifier comprises an air purifier and a scene recognition device mounted on the air purifier, wherein the scene recognition device comprises an image preprocessing module, an image extreme point detecting module, an image feature point positioning module, a main direction determining module, a feature extracting module and a scene determining module; the image feature point positioning module is used for determining extreme points of feature points by removing low-contrast points sensitive to noise and unstable edge points among the extreme points; the main direction determining module is used for connecting any two adjacent peak values in a gradient direction histogram about the feature points to form a plurality of sub line segments, merging the adjacent sub line segments with similar slope in a length direction to form a line segment and taking a direction of an optimal line segment among a plurality of line segments as a main direction of the feature points. The intelligent air purifier has the advantages of high scene recognition accuracy and fast scene recognition speed.

The invention discloses a metal oxide gas sensor array and a method for identifying the type and concentration of methanol or ethanol or isopropanol by using the gas sensor array. The sensor array comprises sensors coated with n different nickel oxide sensitive films, the nickel oxide sensitive films are prepared through a solution method, and the types and recognition performance of the metal oxide sensitive films are expanded through doping of different metal ions; Furthermore, thermal modulation resistance response signals of the sensor array to methanol, ethanol or isopropanol with different concentrations and types are tested through variable-temperature thermal modulation, and then normalization processing of thermal modulation sensitivity response signals is carried out; mapping (calibration) between methanol or ethanol or isopropanol with different concentrations and a normalized sensitivity signal is established through a supervised learning algorithm, then types and concentrations of the three alcohols are identified, and the method can be widely applied to quantitative identification and detection of types and concentrations of other volatile organic compounds very easily.

The invention relates to an intelligent repair system of a connecting rod mold visual detection machine. The intelligent repair system comprises a supplementary welding robot, a welding power source, adjusting slide rails, a cooling system, a laser tracking system, a 3D intelligent scanning camera, a supplementary welding workbench and a control system. The supplementary welding workbench comprises a workbench body, workpiece locating frames, ejector pins and adjusting drive devices. At least one adjusting slide rail surrounds the supplementary welding workbench to form a closed annular structure. The laser tracking system and the 3D intelligent scanning camera are arranged on the supplementary welding robot. The cooling system is arranged on a tackle and connected with the supplementary welding robot and the supplementary welding workbench. The welding power source and the control system are arranged on the side surface of the supplementary welding workbench. On one hand, connecting rod surface quality can be precisely recognized and detected effectively, and the efficiency for finding surface defects is improved; and on the other hand, the surface defects are precisely analyzed and calculated, then welding equipment is driven for welding operation, and therefore the precision quality of surface supplementary welding operation is improved.

The invention discloses a P-type metal oxide gas sensor and a preparation and application method thereof. The sensor consists of a ceramic base which is attached with test and heating electrodes and coated with P-type metal oxide nano particles. The method is characterized in that bacterial cellulose hydrogel is first immersed in the metal salt solution, is then subjected to standing, rinsed and then lyophilized, the obtained cellulose xerogel attached with metal ions is first calcined, mixture of the obtained flocculent metal oxide particles and volatile reagent is further ground, and lastly, the obtained metal oxide nanoparticle slurry is coated on a ceramic substrate and aged, and a desired product is obtained; The method is advantaged in that calibration processing is performed in advance, according to what kind of volatile organic compound (VOC) envelope the measured signal belong to in the calibration result, or from what kind of volatile organic compound (VOC) envelope the distance is the shortest, what kind of volatile organic compound the gas measured at the time is obtained. The method is advantaged in that the method is extremely easy to be widely commercialized for accurate identification detection of a variety of volatile organic compounds.

The invention provides a process step correctness automatic detection method and system which are applied to a server, and the method comprises the steps: carrying out the semantic segmentation of a to-be-detected image based on a deep learning method, and obtaining the target type of each pixel in the to-be-detected image, wherein the to-be-detected image is an image frame in a video containing aprocess step execution process; determining a human body region and a working region in the to-be-detected image based on the target category; recognizing the human body area and the working area based on a deep learning method to obtain human body posture information and working task information respectively; determining process step information based on the human body posture information and the work task information; extracting a target feature vector of the process step information based on a deep learning method, and judging whether the process step is correctly executed or not based onthe target feature vector. The technical problems that in the prior art, the cost is high, and the process steps cannot be detected in real time are solved.

The invention generally relates to the field of manufacture of semiconductor integrated circuits, particularly to a bearing device and a method adopting the device to transfer wafers. In the bearing device and the method adopting the device to transfer wafers, a movable sensor is arranged on a bearing platform, the position of the sensor can be set as per wafers of different specifications and sizes, so that the movable sensor can be used for recognizing and detecting wafers of different sizes and accurately detecting and recognizing wafers with special shapes, and overlapped wafers in a bearing platform can be effectively avoided, the danger of crushing wafers as wafers cannot be recognized is reduced, the yield of products is improved, the process cost is reduced, little modification is carried out to the conventional equipment, and the compatibility of the device is stronger.

The invention relates to a living body identification and living body identification model construction method and device. The method and device are also applied to a scene that a model-based computer system or electronic equipment performs true prosthesis identification detection on biological characteristics. The living body recognition method comprises the following steps: acquiring a group of image focus stack sequences focused at different depths of an object to be recognized obtained by shooting the same observation point position; based on a feature extraction model, performing feature extraction on each image in the image focus stack sequence to obtain a feature vector of each image in the same feature space; analyzing differences among the feature vectors of the images to obtain a feature difference distribution sequence of the images related to the image focus stack sequence; and inputting the feature difference distribution sequence into a pre-trained living body identification model, and identifying whether the to-be-identified object is a living body biological feature or a non-living body biological feature. The method has the advantages of high identification accuracy, simplicity in operation and convenience in application.

The invention discloses an image scene mode generation device. The device comprises an image preprocessing module, an image extreme point detection module, an image characteristic point positioning module, a main direction determining module, a feature extraction module and a scene determination module, wherein the image characteristic point positioning module determines extreme points as characteristic points by rejecting noise sensitive low-contract points and instable edge points from different extreme points; and the main direction determining module connects two random adjacent peak values, in a gradient direction histogram related to the characteristic points, into a line to form multiple sub line segments, merges the adjacent sub line segments with similar gradients into line segments, and uses the direction of an optimal line segment among the multiple line segments as the main direction of the characteristic points. The generation device has the advantages that scene identification is high in precision and speed.

The invention discloses a method for detecting ovine babesiasis indeterminate species, Babesia motasi LZ subspecies and Babesia motasi NB subspecies as well as a detection kit thereof. The detection method takes ovine babesiasis thrombospondin-related anonymous protein TRAP gene as a detection identifier. The kit comprises six primers shown in SEQ ID No.1, SEQ ID No.2, SEQ ID No.3, SEQ ID No.4, SEQ ID No.5, and SEQ ID No.6, The method takes TRAP as the detection identifier, the difference between species and subspecies is large, the detection sensitivity is better than that of the current technologies. The kit can simultaneously amplify several target DNA molecules in a PCR reaction, and realizes a purpose of simultaneously discriminating and detecting a plurality of microbes in a same sample. Compared with the other molecule detection technologies, the method has the advantages of simpleness, rapidity, high efficiency and saved cost, is suitable for rapid detection of cause of disease, and can be a hotspot technology for scientific research and clinical diagnosis.

The invention relates to the technical field of image processing, in particular to a water body shaking detection and analysis method and system based on image processing. The method comprises the following steps of acquiring multiple frames of water body initial images, removing a mirror image in the initial water body image to obtain an actual water body image, graying the actual water body image, and endowing the gray value of each pixel point in the actual water body image with the gray value corresponding to the gray level interval according to a preset number of gray level intervals to obtain a water body target image, calculating a difference value between two adjacent frames of water body target images to obtain a bubble image, acquiring the number of bubbles and the dispersion degree of the bubbles, and inputting the bubble number and the bubble dispersion degree into a preset water body shaking degree detection model to obtain the water body shaking degree. According to the method, the water body shaking phenomenon in the equipment air tightness detection process can be simply and quickly identified, so that subsequent workers can accurately detect the air tightness of the equipment.

The invention discloses a method for identifying and detecting porphyra red rot pythium pathogens, and belongs to the technical field of algae cultivation disease detection, identification and control. According to the method, specific primers are designed on the basis of genomic difference sequences of the porphyra red rot pathogens P.porphyrae and P.chondricola, and a single PCR detection method and a double PCR detection method for the two pythium pathogens are established. By the method, the P.porphyrae and the P.chondricola as well as the possibility of coexistence of the P.porphyrae and the P.chondricola can be identified and detected. The lower limit of single PCR primer P-for/P-rev detection is 103 copies, and the lower limit of C-for/P-rev primer detection is 102 copies. The lower limit of double PCR detection is 104 copies. According to the method, a specific and sensitive porphyra red rot pythium pathogen identification method is established, the P.porphyrae and the P.chondricola can be identified without sequencing and comparison, and a technical support is provided for prevention and control of red rot in porphyra cultivation in China.