588results about How to "Avoid slow recognition" patented technology

The invention discloses a system and method for processing an unmanned aerial vehicle polling image. The system comprises an unmanned aerial vehicle polling image processing system body which comprises a human-computer interaction module. After the human-computer interaction module obtains a user instruction, a main control panel analyzes the user instruction and transmits the analyzed instruction to a data receiving and transmitting controller, the data receiving and transmitting controller sends a parameter configuration instruction to corresponding peripheral equipment according to the instruction and transmits an instruction execution result back to the main control panel, and the main control panel processes the execution result of the parameter configuration instruction and then sends processed information to the human-computer interaction module so that the information can be displayed to a user. The main control panel is respectively communicated with a secondary controller, a power supply module, a wireless communication module, a data storage module, a locating module, a distance measuring meter and a height gauge. The system and the method improve the automation degree and the informationization degree of polling image processing, improve the efficiency of searching for and processing the polling result, and lower the cost of processing the polling result.

The invention discloses a face detection and recognition method based on skin color segmentation and template matching. A face is segmented with the use of a face skin mixing Gauss model according to the clustering characteristics of face skin color in an YCbCr color space and a region of which the color is close to face skin color is separated from an image so as to achieve the purpose of quickly detecting the outer face of a face. A light compensation technology is used to overcome the influence of brightness on face detection and recognition. Adaptive template matching is used to overcome the influence of a skin-color-alike background on face detection and recognition. A secondary matching algorithm is adopted to reduce the amount of computation during the matching process, and singular value features of a face image are extracted to recognize a face and achieve the purpose of reducing the number of dimensions of a characteristic value matrix in the process of face feature extraction. The face detection and recognition method can quickly detect a face, improves the accuracy of face detection and recognition, is strong in practicability and has strong popularization and application values.

The invention discloses a breakdown intelligent classification and positioning method of an electric transmission line. The technical scheme of the breakdown intelligent classification and positioning method of the electric transmission line breakdown is that the advantages of three kinds of technologies of a support vector machine (SVM), self-adaptation nerve fuzzy inferences (SVM) and radial based function (RBF) neural networks are concentrated. The breakdown classifiers and positioners of the SVM, the SVM and the RBF neural networks are designed. Positioning errors, classification accuracy and model operation time are used as evaluation indicators. According to the standard that accuracy is preferred and efficiency is taken into account, intelligent selection of an optimal classifier and an optimal positioner is achieved under different breakdown conditions, and optimal breakdown classification and positioning effect is achieved. Meanwhile breakdown serious extent and repair indicators are designed to evaluate breakdown injury extent and breakdown repair difficulty. The breakdown intelligent classification and positioning method of the electric transmission line effectively improves power supply reliability, reduces outage cost, and meanwhile greatly reduces workload of maintenance personnel and improves working efficiency.

The invention relates to the technical field of conducting on-line detection through the machine vision and image processing technology, mainly relates to a method for conducting on-line detection on the non-continuity lithium battery thin film defects through a machine vision system and provides a non-continuity lithium thin film defect on-line detection method based on the machine vision. A grey level mutational point is obtained through adjacent grey level point difference method of three horizontal scanning lines, and therefore a continuity thin film section is determined; an optimal threshold value algorithm is used for achieving the binarization segmentation of a grey level image, a reversing large area defecting method is used for the binarization image to position a defective target, the geometrical and projection characteristics of the defects are extracted to be used as identification parameters, and finally the minimum Euclidean distance is used for achieving the rapid identification and classification of the defect target.

The invention discloses an intelligent fruit picking robot based on machine vision. The robot comprises a control system and a walking mechanism, a picking mechanical arm, a visual system and a powersystem which are connected with the control system respectively, the control system, the power system and the walking mechanism are fixedly connected, and the picking mechanical arm is a four degreesof freedom joint mechanical arm and is fixedly connected with the walking mechanism through a base; a visual sensor of the visual system is fixedly arranged above the wrist of the picking mechanical arm, a visual control module of the visual system is integrated with the control system and is connected with the visual sensor through signals, and the visual control module is used for conducting target positioning and identifying on images of the visual sensor based on a full-convolution network image processing technology of image partition; the visual control module transmits target positioning information to the control system, and the control system controls the walking mechanism to move and / or the mechanical arm to pick fruits. The intelligent fruit picking robot based on the machine vision has the advantages of compact structure, good stability, high intelligent degree, quick positioning, high positioning precision, and high environmental adaptability.

The invention provides a method for quickly detecting melamine in a milk sample based on nanogold, which aims to solve the problems of the high operation requirement, complex detection process, high cost, long time, incapacity of meeting a lot of food test requirements and the like on the detection of the melamine. The method comprises the following steps: performing pretreatment on the milk sample and adding the pretreated milk sample into a nanogold solution; and adopting visual colorimetry to compare the color of the nanogold added with the sample solution with the color in a color standard series and then qualitatively judging whether the milk contains the melamine or not. The color standard series is prepared through the following steps: adding the pretreated milk samples in an identical volume and containing the melamine of different concentrations into the nanogold solution, wherein the content of the nanogold in each sample is consistent; and after color development reaction, obtaining the color standard series. The method has the advantages of low cost, simple and convenient operation and capacity of visually and quickly detecting the melamine in the milk.

The method includes steps: carrying out Gaussian convolution operation for gray scale value (GSV) of pixels; determining whether GSV of pixels after operation is larger than prearranged threshold value; if yes, marking current pixels, and recognizing relevant light spot, and calculating product of GSV of current pixel and coordinate value and accumulated value of products of GSV and coordinate values of all pixels in same light spot; saving the accumulated value; otherwise, marking current pixel as pixel in background. When accomplishing whole output image, the method calculates out quotient between accumulated value of product of GSV by coordinate value and accumulated value of gray scale value. The calculated result is as coordinate value of mass center of facula. The invention also discloses mass center positioning device. Advantages are: increasing speed for processing data and capability for anti noise, and capable of processing multiple facula images.

The invention discloses a biochip analysis method based on an active contour model and a cell neural network. The method comprises the following steps that improved Hough transformation is adopted to perform slant correction on a rectangular sampling point, and improved Radon transformation is adopted for a circular sampling point; initial positioning is performed on the sampling points by using a projection method, and an optimized network is generated; then the network is adaptively adjusted on the basis of neighborhood search, and secondary precise positioning is performed on the sampling points; the active contour model is optimized by using a greedy algorithm, and a CNN (Cable News Network) is utilized to classify the sampling points in accordance with signal strength; Multiple snakes are combined with the CNN, the CNN first learns about the convergence behavior of the sampling point snake with a strong signal and then guides the convergence of the sampling point snake with a weak signal, and finally, reasonable partition of the sampling points is realized; and signal data of microarray sampling points is extracted and output. By using the method, the problems of slant correction of a biochip image, difficulty in partition of sampling points with irregular shapes and sampling points with weak signals and the like are solved, automatic identification of biochip sampling points is realized, and the method is suitable for quick analysis of large-scale biochip sampling points.

The present invention discloses a method and a device for managing information of a lost person. The method comprises: acquiring a first to-be-searched image containing a human face of a first to-be-searched object; performing human face recognition on the first to-be-searched image to obtain human facial feature information of the first to-be-searched object; searching in a known lostness image collection stored in a known lostness database for a particular known lostness image, wherein a similarity between human facial feature information of a particular known lost person corresponding to the particular known lostness image and the human facial feature information of the first to-be-searched object is larger than a similarity threshold; determining whether the particular known lostness image exists according to a search result, and if the particular known lostness image exists, determining the first to-be-searched object is the known lost person, and if the particular known lostness image does not exist, determining that the first to-be-searched object is not the known lost person. According to the method and the device provided by the present invention, fast search and accurate identification of the lost people can be realized.

The invention discloses a kind of array sensor for gas and the preparing method. The sensor uses the aurum, platinum or argentum as electrode; the RuO2, MoSi2, W or Pt is as heating membrane material; the random one or several kinds of the ZnO, SnO2 or Fe2O, or ZnO, SnO2 or Fe2O3 and Pt0.1wt%-3wt%, Pd0.1wt%-3wt%, Ni1wt%-30wt%, TiO21wt%-30wt%, WO31wt%-40wt%, Co2O3 1wt%-15wt%, V2O51wt%-30wt%, Al2O31wt%-20wt%, MnO21wt%-30wt% is as the sensing layer material. It gets the gas sensing array on the alumina, aluminium nitride or silicon base sheet with anti high temperature, electric insulating and heat conducting by the screen printing technology. The gas sensing array is fixed on the basal seat by the axis to form the gas sensing array sensor by ultrasonic hot press welding electrode leg wire. The sensor has the low production cost, low power cost, good mechanism stability. Each unit has the good gas sensing character and the process is proper for the big batch production.

The invention discloses an ultrasound image processing method. In the ultrasound real-time scanning state, a scanning instruction of an ultrasound image of a target object is received and acquired; according to the scanning instruction, a standard section of the target object is acquired and regarded as the ultrasound image of the target object; the ultrasound image is stored. The invention further discloses an ultrasound image processing device and ultrasound diagnosis equipment. According to the ultrasound image processing method and system and the ultrasound diagnosis equipment, in the ultrasound real-time scanning state, the standard section of the target object is acquired and regarded as the ultrasound image of the target object according to the scanning instruction, direct fast recognition and acquirement of the standard section are achieved, and manual freezing or thawing operation is not needed, so that the accuracy and efficiency of scanning are improved.

The invention relates to a preparing method of an amino carbon quantum dot fluorescence silicon substrate imprint sensor and belongs to the technical field of preparation of an environment function material. The preparing method comprises the following steps: dissolving citric acid and a polymine solution to obtain a transparent solution, then transferring to a hydrothermal reaction kettle for reaction, and then cooling to room temperature to obtain dispersion liquid of amino carbon quantum dots; centrifuging the dispersion liquid to obtain an amino carbon quantum dot solution; then taking the amino carbon quantum dots as fluorescence carriers, p-nitrophenol as template molecules, (3-aminopropyl) triethoxy silane as a function monomer, and tetraethoxysilane as a cross-linking agent so as to obtain a fluorescence molecular imprinted polymer to be used for optical detection of p-nitrophenol. The amino carbon quantum dot fluorescence molecular imprinted polymer has very good optical and pH stabilities and has te capacity of selectively recognizing p-nitrophenol.

The invention discloses a colorimetric detection method of hydrogen peroxide. The colorimetric detection method comprises the following steps: mixing a sample solution containing hydrogen peroxide with a molybdenum disulfide solution, a color developing agent and a buffer solution, and measuring the content of the hydrogen peroxide in the sample by adopting a visual colorimetry or an ultraviolet-visible spectrophotometer after reaction. According to the colorimetric detection method, the chromogenic reaction of the hydrogen peroxide and the color developing agent is catalyzed by using molybdenum disulfide as a catalyst to detect the hydrogen peroxide, and the concentration of the hydrogen peroxide is semi-quantitatively determined by adopting a visual colorimetry, or the concentration of the hydrogen peroxide is quantitatively determined by adopting an ultraviolet spectrophotometer. According to the colorimetric detection method, the problems of high operation requirement for detection of the hydrogen peroxide, complicated detection process, high detection cost, long detection time, large background interference and the like in the prior art can be solved. The colorimetric detection method disclosed by the invention is low in cost, simple and convenient to operate, and capable of realizing visible quick detection of the hydrogen peroxide.

The invention discloses a deep learning-based haze visibility detection method. The method comprises the following steps of: firstly creating a road traffic haze image library which comprises a training sample set and a cross validation sample set; preprocessing all the haze images in the traffic haze image library; extracting farthest visibility edge features of the preprocessed haze images I the training sample set by adoption of a convolutional neural network so as to obtain a plurality of feature maps; forwardly propagating the feature maps to a configured convolutional neural network to be trained, adjusting weights between layers in the convolutional neural network through a counter-propagation algorithm, carrying out repeated iteration to solve a convolutional neural network model for haze image classification, and optimizing the convolutional neural network model through the preprocessed cross validation sample set so as to finally obtain a visibility detection model for haze image classification; and carrying out classification judgement on pictures shot by a pavement by utilizing the obtained visibility detection model so as to detect the haze condition in real time.

The invention discloses a vehicle-mounted terminal multi-target recognition tracking prediction method, which is a vehicle-mounted terminal multi-target recognition tracking prediction method based onYOLOv5s (You Only Look Once v5s) and FairMOT (FairMOT) fusion, and detects vehicles in front of a road quickly and accurately in real time by using a YOLOv5s deep learning object detection technology. The YOLOv5s model is fused into the FairMOT architecture detection module to perform target detection and re-identification tracking in a single network so as to realize position detection, type identification and multi-target motion trajectory tracking of the traffic target in front of the vehicle on the road, thereby achieving prediction of driving behaviors of lane change, following, deceleration and the like of the traffic target in front of the vehicle.

The invention discloses a user identity verification and permission identification method. The method includes: establishing a user database including face images of users, terminal IDs and one-to-onecorrespondence relationships of the face images and the terminal IDs; acquiring, through mobile-communication-terminal sensing equipment, a user terminal ID of a mobile communication terminal entering a communication coverage range thereof; finding the acquired terminal ID in the user database, and establishing a temporary database including the terminal ID, a face image corresponding thereto anda correspondence relationship between the two parts; and capturing, through shooting equipment, an image of a face entering a shooting area in real time, comparing the captured face image and user data in the temporary database in real time to verify legality of holding an identity of the user, and issuing corresponding service signals when the legality is verified. Through the method, fast, accurate and secure user identity verification and permission identification can be realized to complete signal output of payment information exchange, control on hardware equipment or the like.

The invention discloses an in-situ measurement method of adherent mushroom vision based on RGB-D camera; The RGB-D camera continuously collects the depth video stream; dynamic threshold segmentation is performed on each acquired depth image to remove the soil background, extract the mushroom connected domain and smooth the edge; and use the eight-neighbor tracking method for the extracted mushroomconnected domain Sequence traversal of its boundary contour, preliminary detection of its center and radius based on circular fitting, sequential extraction of boundary points within 1.3 times radiusaround the center of the circle and conversion to polar coordinates, denoising between the found adhesion points, Interpolation, in order to obtain the two-dimensional coordinates of the boundary contour of the single mushroom; calibrate the camera coordinate system, verify the accuracy of the in-situ measurement method based on the ceramic circular plate, and calculate the position, diameter, and deviation angle of the single mushroom in the camera world coordinate system . The method of the invention can accurately and quickly identify adhered round-like mushrooms, and the picking robot hashigh coincidence degree, short running time and high real-time performance.

The invention provides an examination room monitoring data processing method and an automatic monitoring system thereof. The system comprises a monitoring terminal module, a server module, and a video data preprocessing module used for sampling video monitoring pictures obtained by the monitoring terminal module, preprocessing sampled images and dividing seat areas; an identification and alarm module used for identifying violation motion in the seat areas, marking the level of violation motion when motion exceeding a preset threshold is identified, sending the level and a data packet associated with the violation motion to a server, and meanwhile sending seat area information involved by the violation motion to a monitoring platform; and the monitoring platform used for displaying monitoring frames, highlighting a violation seat area according to the information sent by the identification and alarm module. The method can identify violation motion with a fast speed and high recognition rate; the automatic monitoring system facilitates observation by monitoring platform personnel, and on-site personnel rapid communication.

The invention discloses a shale gas reservoir staged comprehensive evaluating method. Shale gas well core analysis and test data and logging information are collected, the corresponding relation of the development characteristics and logging parameters of a shale gas reservoir in a target area is determined, and the shale gas reservoir is identified qualitatively; and a key parameter system for evaluating of the shale gas reservoir is selected and determined, shale gas reservoir staged comprehensive evaluating standards are built according to the characteristics and correlation of key parameters, and the quality of the shale gas reservoir in the target area is divided and evaluated quantitatively. The multiple key parameters of the shale gas reservoir are composited, the logging response characteristics of the single-well shale gas reservoir section are combined, thus staged comprehensive evaluating of the shale gas reservoir is formed, and the shale gas reservoir can be evaluated quantitatively in the two aspects of geology and geophysical logging.

The invention relates to an agricultural drug applying device, in particular to an accurately targeted drug applying device for fruit tree rootstock. The device comprises a system carrying platform, a locating and identifying subsystem, a targeted drug applying subsystem and a distribution map generating subsystem. According to growing characteristics of the fruit tree rootstock, a portal frame type moving platform is adopted; a scanning type laser range finder, machine vision and GPS (global positioning system) are combined together to quickly and effectively locate and identify the fruit tree rootstock, therefore a multi-nozzle drug applying system is controlled to finish the targeted drug applying operation, and meanwhile the distribution map of the fruit, the rootstock and the drug applying amount is generated. The accurately targeted drug application to the fruit tree rootstock can be implemented; the pesticide usage amount is effectively reduced; the drug liquid utilization ratio is improved; the environment pollution is reduced; and scientific skills can be provided for the orchard digital management in the future.

The invention relates to an abandoned land remote sensing recognition method based on texture and vegetation indexes. The characteristics of multiple sensors, multiple resolutions, multiple spectra and multidate remote sensing data are utilized, the spectra and the textural features of remote sensing images, and the vegetation index features are combined, and the abandoned land remote sensing recognition method based on the texture and the vegetation indexes is established by structuring a remote sensing image registration and fusion framework, a spectrum and texture feature space and a vegetation index time sequence. Compared with an existing abandoned land remote sensing recognition method, the abandoned land remote sensing recognition method based on the texture and the vegetation indexes solves the precision problem brought by broken cultivated land, reduces artificial involvement subjectivity and improves abandoned land recognition efficiency. The abandoned land remote sensing recognition method based on the texture and the vegetation indexes is characterized in that a remote image registration and fusion unit, a typical spectrum and texture feature unit, a vegetation index time sequence feature unit and an abandoned land recognition and information extraction unit are included. Through the abandoned land remote sensing recognition method with the four units, the distribution information and data of abandoned land in a large range area can be recognized fast and precisely, and auxiliary decision-making data support can be provided for land utilization, grain guarantee and the like.

The invention discloses a method and a device for caching webpage content and loading a webpage and a terminal device. The method for caching the webpage content comprises the steps of obtaining an identification mark of a current browsed webpage when a trigger of the browsed webpage is received; judging whether the identification mark of the browsed webpage exists in a preset identification mark set or not; storing the webpage content of the browsed webpage corresponding to the identification mark in a first caching area corresponding to the browsed webpage in caching areas if the identification mark corresponding to the browsed webpage exists in the preset identification mark set; storing the webpage content of the browsed webpage in a second caching area if the identification mark of the browsed webpage does not exist in the preset identification mark set. According to the method and the device for caching the webpage content and loading the webpage and the terminal device, which are disclosed by the embodiment of the invention, loading can be carried out without networking again when a preset website needs to be loaded, the processing efficiency is increased, the energy consumption of the device and the consumption of network data are saved, and the user experience is increased.

The invention discloses quick solution system and method for a shield attitude, and relates to the technical field of shield attitude solution. Two automatic measurement total stations are adopted; one automatic measurement total station is still arranged in a hanging basket; the other automatic measurement total station is arranged on a top bracket of a shield locomotive cabin which synchronously moves along with a shield tunneling machine; the static total station is used for carrying out real-time tracking measurement on the dynamic total station to obtain an instantaneous coordinate of a measurement station; the dynamic total station is used for monitoring three prisms on a shield tunneling machine body in real time to obtain the coordinate of the shield tunneling machine body under an engineering coordinate system; the data are fed back to a solution center through a wireless communication module; and the solution center is used for calculating conversion parameters between a shield coordinate system and the engineering coordinate system by combining the dip angle data of the shield tunneling machine returned by a tilt meter with the positions of the three prisms in the shield coordinate system according to a joint data solution model for space coordinate conversion, so as to determine the space positions of the head and tail centers of the shield tunneling machine, and comparing the space positions with design positions, so as to obtain the horizontal and vertical deviations of head and tail center points and the attitude angle of a machine body.

The present invention relates to a method for preparing a porous magnetic imprinting absorption agent through emulsion polymerization, and belongs to the technical field of environment function material preparation. The method comprises: preparing magnetic halloysite, carrying out vinyl modification on the halloysite, adopting a dispersion solution of the modified magnetic halloysite and 2,2'-azobis(2-methylpropionamidine) dihydrochloride as a water phase, adopting a mixture of a template molecule cyhalothrin, a function monomer methacrylic acid, a cross-linking agent ethylene glycol dimethacrylate, a pore-forming agent and an organic solvent chloroform as an oil phase, mixing the water phase and the oil phase, carrying out an ultrasonic treatment to prepare stable Pickering emulsion, carrying out polymerization to prepare porous magnetic imprinting, adopting a methanol and acetic acid mixing solution to wash to remove the template molecule, and carrying out vacuum drying on the obtained porous magnetic imprinting at a temperature of 50 DEG C, wherein the obtained product is used for selective recognition and separation of cyhalothrin in an aqueous solution. The prepared porous magnetic imprinting absorption agent has characteristics of significant thermal stability, significant magnetic stability, high absorption capacity, and significant cyhalothrin molecule recognition property.

The invention discloses a bamboo forest pest recognition method based on a convolutional neural network model, and belongs to the technical field of agricultural and forestry pest recognition and classification. The method comprises the steps of collecting the bamboo forest pest sample image data; processing the pest sample image data of the bamboo forest to obtain a processed pest sample image data set of the bamboo forest; adopting a VGG convolutional neural network model, and optimizing the VGG convolutional neural network model; carrying out target pest identification and classification byutilizing the optimized VGG convolutional neural network model; developing a set of pest identifying and classifying method suitable for bamboo forests based on a VGGNet network structure, so that rapid identification and classification of common bamboo forest pests can be achieved, the automatic identification and classification can be achieved, and the identification accuracy is high.

The invention provides a machine vision-based tobacco leaf portion identification method, an electronic device and a storage medium. The method includes the following steps that: the standard gradingsamples of tobacco leaves are acquired, tobacco leaf sample images are collected, and all qualified tobacco leaf sample images are obtained; tobacco leaf regions where tobacco leaves are located are segmented from the qualified tobacco leaf sample images on the basis of differences between tobacco leaf colors and background colors; the mean values and standard deviations of the RGB and HSV of thetobacco leaf regions, as well as the area, length and width of the tobacco leaves are obtained; and the mean values and standard deviations of the RGB and HSV as well as the area, length and width ofthe tobacco leaves are marked as feature vectors for tobacco leaf grade identification, and a feature matrix is formed according to the feature vectors of different tobacco leaves; the feature matrixis decomposed, so that an identification model for tobacco leaf portion identification is established; and tobacco leaf portions are identified according to the identification model. With the method of the invention adopted, portion confusion in a large number of tobacco leaf portions and errors in grade identification can be avoided. The method has accurate and rapid identification capacity.

The invention relates to a method and a system for automatic positioning and identifying a target based on high definition video images. The system comprises a high definition video image enhancement processing module, a target detecting and positioning module, a grayscale image binarization module, a target contour image inclination correction module, a target contour image dividing and feature extracting module, and a target contour image identifying and post-processing module. The system has the advantages that automatic target detecting and fast target positioning and identifying effect of high definition (above 2 million pixels) video images (moving) under complicated conditions is fine, high definition video image enhancement processing effect under uneven illumination conditions is fine, dynamic range is large, automatic target positioning and identifying precision are high and the like. In the meantime, reliability and stability of software is improved greatly by the design of an embedded software function, and construction mounting requirement of a front end embedded system (intelligent camera) is lowered.

An embodiment of the invention discloses a method for establishing tin soldering process libraries and a system applied to the same. The method includes acquiring solder joint types of first circuit boards, selecting tin soldering environmental parameters according to the solder joint types and determining tin soldering parameters according to the tin soldering environmental parameters and the solder joint types; counting and storing the solder joint types of the first circuit boards, the tin soldering environmental parameters and tin soldering parameters. The solder joint types include pin parameters and pad parameters of components on the first circuit boards, and the tin soldering environmental parameters correspond to the solder joint types. The tin soldering parameters correspond to the tin soldering environmental parameters and the solder joint types and include tin feeding speeds, tin feeding lengths, preheating time and tin-soldered stagnation time. The method and the system have the advantages that tin soldering can be carried out by the aid of the tin soldering parameters in the tin soldering process libraries when the tin soldering process libraries contain the solder joint types of the circuit boards to be subjected to tin soldering and the tin soldering environmental parameters, accordingly, the tin soldering parameters do not need to be manually set one by one, and the tin soldering efficiency can be improved.

The invention relates to the vision identification method of a chip, especially to a solder ball positioning and parameter identification method for a ball grid array pin chip. With the method, problems of poor robustness and low chip solder ball identification precision of the existing method can be solved. An original chip image is processed to obtain a binary solder ball image and connected domain marking is carried out on each binary solder ball; grayscale connected domain extraction is carried out on each binary solder ball after the connected domain marking in a corresponding neighborhood range on the original image and a grayscale information list is established; a background image is established and a local analysis is carried out; boundary straight-line fitting is carried out to solve a deflection angle and a center position of the chip; a linear equation of equivalent solder balls at each row and each column is determined, equivalent solder balls searching is carried out to obtain an equivalent solder ball set of each row and an equivalent solder ball of each column, and then correlated dimensions of the solder balls are solved; and linear fitting is carried out, and all fitting straight line spaces at adjacent rows are used as solder ball standard row intervals and fitting straight line spaces of adjacent columns are used as solder ball standard column intervals. For identification and positioning of a chip with a 500-solder-ball-pin scale, the time is less than 200ms.

The invention discloses a preparation method for a silver nano sheet array by induced growth based on a primary cell mechanism and an application of the SERS (surface-enhanced Raman scattering) effect of the silver nano sheet array. The silver nano sheet array is prepared on conductive glass subjected to gold steaming by an assembled copper-silver primary cell; and the prepared silver nano sheet array has stronger uniform surface-enhanced Raman scattering effect, namely SERS effect. The nano sheet array is used as an SERS substrate, thus rapid detection and identification of trace tetrachlorobiphenyl, namely PCB-77 are achieved; and the substrate has potential application prospects in the aspect of realizing rapid trace detection on persistent organic pollutants by using the SERS effect.