64 results about "Region analysis" patented technology

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention relates to the technical field of twin models, in particular to a subway station flood prevention simulation method and system based on a three-dimensional model. According to the method, local important evaluation indexes are obtained through placename approximate analysis and concavity-convexity analysis of structures of point cloud data in a neighborhood space, and structural important evaluation indexes of single point cloud data are determined by combining and analyzing density distribution complexity in the neighborhood space; clustering the point cloud data and dividing the point cloud data into different regions; analyzing depth distribution of all the point cloud data in the regions; combining structural importance evaluation, determining an attention rate of each region, performing differential modeling and semantic processing to obtain a semantic three-dimensional model; and combining a hydrodynamic model to obtain a digital twinborn model. According to the method, differentiated three-dimensional modeling is carried out by evaluating the importance of each point cloud data in subway station flood prevention simulation, and flood prevention analysis is carried out more comprehensively and accurately by combining a digital twin model with a hydrodynamic model, so that the protection capability of subway flood prevention is improved.

The invention relates to the technical field of vehicle damage detection and identification based on computer vision, and discloses a vehicle damage detection and identification method based on computer vision. The method comprises the steps of image acquisition, polarization image acquisition, Stokes parameter calculation, polarization feature extraction, anomaly evaluation, connected region analysis and the like. A polarization statistical model is established by setting an image coordinate system, collecting a multi-angle polarization diagram and calculating the polarization degree and the polarization angle of each pixel and taking an artificially selected nondestructive area as a reference, so that anomaly measurement and adaptive threshold segmentation are performed on the pixels, and a structured vehicle damage detection result is output in combination with corrosion, expansion and connected domain analysis. Physical interpretability and detection precision are enhanced by using polarization characteristics, recesses or scratches can be accurately identified even in a vehicle body area with a smooth surface or complex reflection, a standardized report containing position information and abnormality is provided, and automation and accuracy of vehicle damage assessment are effectively improved.

The invention discloses a document proofreading system and method based on artificial intelligence, and relates to the technical field of natural language processing, and the method comprises the steps: analyzing a document, dividing semantic units, and obtaining a document with a context label based on context type library labeling; performing initial error detection by using the corresponding rule set, and comparing the format of the document and the text quality characteristics with a standard sample vector space to obtain an initial error set; constructing a defect conduction chain network, and locating root cause error nodes causing a plurality of secondary errors by tracing the defect conduction chain network; generating an intelligent proofreading report; receiving a new-version document revised by the user, comparing the new-version document with the original-version document, positioning a change area, and analyzing a quality difference of the change area on a related quality dimension; based on the quality difference, an incremental proofreading report is output, and the incremental proofreading report comprises newly introduced errors and unsolved root cause errors.

The invention provides an image processing method, device and equipment based on hardware perception and dynamic rarefaction, and relates to the technical field of image processing, and the method comprises the steps: inputting hardware operation state data into a preset PPO strategy network, so as to obtain the sparse rate of each convolution layer; reserving a specified number of channels as effective calculation channels; performing saliency region analysis on an input to-be-processed image to generate a pixel-level saliency map corresponding to the to-be-processed image; dividing the to-be-processed image into a key target area and a background area; allocating a first preset precision to the key target area for image processing, and allocating a second preset precision to the background area for image processing; according to the sparse rate of each convolutional layer, the first preset precision allocated to the key target region and the second preset precision allocated to the background region, collaborative image processing is carried out on the to-be-processed image; according to the invention, the large-scale landing capability and continuous optimization performance of the edge intelligent vision system can be improved.

The invention relates to the technical field of heat exchangers, and discloses a control method and system for a spiral wound tube type heat exchanger based on the Internet of Things. The method comprises the following steps: acquiring flow velocity distribution data in a pipe in real time through a multi-point sensor to form a first data set, performing regional analysis to obtain a flow velocity deviation value, evaluating heat exchange abnormality, positioning an abnormal position, and generating an abnormal region distribution map; a dynamic parameter adjustment scheme is calculated in combination with a heat exchange efficiency target, a first adjustment instruction set is generated, a second flow rate data set is obtained after execution, and the matching degree is evaluated through a fuzzy control algorithm; if the preset standard is not met, a second adjusting instruction set is generated in combination with working condition requirements, and finally an operation state report is output. The method has the following effects that the flow velocity distribution in the spiral wound tube type heat exchanger is accurately controlled, the problems of local overheating and uneven heat exchange are effectively solved, the heat exchange efficiency is improved, the purpose of balancing energy consumption is achieved, and stable operation of the system is guaranteed.

The application relates to the technical field of product copying, in particular to a product copying software system for three-dimensional model import and conversion, and discloses the following scheme: a primary three-dimensional model is generated by acquiring a main view image and a sub-view image, extracting a first characteristic parameter and a second characteristic parameter; target parameter adjustment is carried out based on the bidirectional mapping relationship between the target parameter and an inferred parameter, and a parameter combination is generated; the response coefficient of the inferred parameter is corrected through reverse deduction, a bidirectional correlation matrix is optimized, and the consistency of the target parameter and the inferred parameter is ensured. Through shadow area analysis and surface fitting technology of multi-view images, the problem of bottom area data loss is solved, and accurate three-dimensional reconstruction of the object surface and the bottom area is realized.

The invention provides an end-to-end real-time analysis method for an endoscope image fluorescence brightness area based on double deep learning model joint training and a self-group iteration upgrading device. The end-to-end real-time analysis method comprises the following steps: acquiring an image of a biological tissue and processing the image by using a deep learning algorithm to obtain a training data set; sequentially constructing and training an image preprocessing deep learning model and an unsupervised image segmentation deep learning model based on the training data set; inputting image data acquired by the endoscope into an image preprocessing deep learning model to obtain a first output image; inputting the first output image into an unsupervised image segmentation deep learning model to obtain a second output fluorescence image; and carrying out brightness analysis on the second output fluorescence image, and visually outputting and storing the second output fluorescence image. The real-time performance and the accuracy of fluorescence region analysis can be improved. According to the self-group iteration upgrading device, model training iteration upgrading can be carried out in multiple modes, and the performance of the device is continuously improved.

The application discloses a kind of area energy storage configuration methods for improving power distribution network carrying capacity under source-load coupling scene.First, the source-load comprehensive carrying capacity coefficient calculation model of power distribution network area is constructed, for calculating the source-load comprehensive carrying capacity coefficient of each typical day of area;Then, the source-load comprehensive carrying capacity feasible region analysis model of power distribution network area is established, for calculating the source-load comprehensive carrying capacity feasible region area of area;Finally, based on the source-load comprehensive carrying capacity coefficient and source-load comprehensive carrying capacity feasible region area of each typical day of area, double-layer optimization model is constructed, and double-layer optimization model is solved using hierarchical solving method, and the optimal energy storage configuration scheme is obtained.The method considers source-load coupling phenomenon, the carrying capacity bottleneck of area is characterized by source-load comprehensive carrying capacity coefficient, the source-load comprehensive carrying capacity of area is quantitatively evaluated by source-load comprehensive carrying capacity feasible region area, and the problem that traditional method is not accurate for power distribution network carrying capacity evaluation is solved.

The invention relates to the technical field of low-frequency sonar systems, in particular to an active azimuth history graph suspected target prompting method based on image segmentation, which comprises the following steps: firstly, carrying out binarization processing on an active azimuth history graph through a maximum between-class variance method, then, carrying out morphological processing and connected region analysis on a binary image to realize 8 connected region marking, and finally, carrying out image segmentation on the image; secondly, calculating the area and the mass center of each connected region, filtering the region with the large area to obtain a discrete target region mask image, multiplying the discrete target region mask image with an original binary image to obtain a discrete target region image, and finally, carrying out BFS search on the mass center of each discrete target region to obtain a discrete target region image; and calculating the sum of the Euclidean distances between the active azimuth history graph and five nearest connected regions, marking the mass centers of five discrete target regions with the maximum Euclidean distance sum on the active azimuth history graph, and realizing isolated bright spot positioning, thereby realizing automatic prompting of suspected targets in the active azimuth history graph of the sonar system so as to reduce the difficulty of target identification of a worker.

The embodiment of the invention provides a pop-up window processing method and device and a storage medium. In the embodiment of the invention, based on a collaborative architecture of two AI models, pop-up window processing is divided into two stages of potential pop-up window area analysis and pop-up window feature recognition, a potential pop-up window area in a screen image is analyzed by using a first AI model, and a sub-image of the potential pop-up window area is cut out from the screen image; and performing multi-dimensional feature analysis and popup window feature identification based on the sub-images by using the second AI model, positioning position information of closing options according to the multi-dimensional feature analysis and the popup window feature identification, executing popup window closing operation based on the position information, cutting out the sub-images of potential popup window areas, converting global analysis into local analysis, reducing calculation overhead of irrelevant areas, and on the basis, improving the popup window closing efficiency. Model reasoning is used for analyzing the cut sub-images, the efficiency and accuracy of pop-up window detection and closing are effectively improved, then the probability that automatic testing is interrupted due to pop-up window interference is reduced, and the testing stability and continuity are improved.

This invention relates to the field of marine engineering safety early warning and intelligent prediction, and in particular to a point-to-area short-term wave height prediction method and system for offshore construction early warning. The invention takes historical pattern wave height, historical reference monitoring wave height, and future pattern wave height of a measurement point as inputs. It constructs a short-term prediction model based on a Transformer encoder and introduces a joint loss function including absolute error, large value underestimation penalty, trend asymmetry, residual, and peak time terms to automatically output the future correction results for each measurement point. Then, based on the mapping relationship between the measurement point and the corresponding coverage area, the correction results of the measurement point are extended to the entire target area to form a corrected wave height field. Combined with construction thresholds, it automatically determines the levels of normal, attention, alert, and work stoppage. Without requiring continuous manual interpretation, it forms a complete closed loop from data acquisition, regional analysis, prediction, regional correction to graded early warning and result storage and push.

The invention discloses a power distribution cabinet operation state intelligent monitoring method and system, and belongs to the technical field of power distribution cabinet state monitoring, and the method comprises the steps: carrying out the real-time analysis of each power distribution cabinet needing to be subjected to state monitoring, determining a state target, needing to be subjected to timestamp alignment monitoring, of each power distribution cabinet, and marking the state target as a time monitoring target; performing real-time simulation analysis on the time monitoring target, determining an alignment optimization mode of the time monitoring target, and adjusting a corresponding timestamp alignment mode in the corresponding power distribution cabinet according to the alignment optimization mode; monitoring the power distribution cabinet in real time to obtain monitoring data; analyzing the monitoring data to obtain state analysis data of the power distribution cabinet; carrying out regional analysis on the state analysis data of the power distribution cabinet, obtaining the state analysis data in real time, carrying out regional influence evaluation on the state analysis data, and determining whether the state analysis data has a regional risk; and when the regional risk is judged to exist, carrying out regional risk early warning.

The present application relates to a kind of password algorithm artificial intelligence side channel analysis method based on dimension reduction and clustering, belong to information security cryptography field.First, the energy trace obtained is analyzed, to determine the area of the energy trace in which the execution of the decryption process is related to secret information operation.Analyze the energy trace of the key operation part, use the method based on sliding window gradient value to divide each operation, obtain energy trace segment.Combined with the implementation of cryptographic algorithm, the energy trace segment is analyzed, and the operation type is identified.Use principal component analysis on energy trace segment for dimension reduction, take the component corresponding to the expected dimension as the principal component of each operation.Combined with the characteristics of energy trace segment, the cluster number obtained by clustering is estimated.Use clustering algorithm to cluster the energy trace segment after dimension reduction, analyze the clustering result to determine the operation type represented by each cluster, obtain the operation type corresponding to the energy trace segment, finally obtain the secret index sequence, realize the efficient analysis of the side channel of cryptographic algorithm.

The invention provides a slow action generation method based on motion focus area analysis. The method comprises the following steps: determining a video of a slow action to be generated; detecting a motion focus area in the video, and grading the motion focus area; and for different grades of motion focus areas, different strategies are adopted to carry out motion modeling and differential interpolation processing, and a slow motion frame sequence is generated. According to the invention, through identification and grading processing of the motion focus area, high-precision calculation of the core motion object, simplified calculation of the secondary area and simplest processing of the static background, compared with a traditional scheme of uniform and dense calculation of the whole frame, the method has the advantages of high calculation complexity and lower calculation amount, and guarantees the visual effect of the core area.

The embodiment of the invention relates to the technical field of intelligent robots, in particular to an intelligent robot control method and device, equipment and a medium, and the method comprises the steps: collecting environment data covering N regions, mapping an environment image into a preset grid map, and obtaining an environment grid map comprising a plurality of grid units; based on the environment data and the environment grid map, space-time pollution potential energy fields of the N areas are constructed, and an environment state distribution map is obtained based on the space-time pollution potential energy fields; performing connected region analysis and threshold segmentation based on the environmental state distribution diagram, and identifying a target connected region with the highest pollution score as a first region; based on historical operation data of a first area and pollution potential energy distribution of the first area in the space-time pollution potential energy field, determining at least one operation route according to a first path search function; and controlling the intelligent robot to operate according to the operation route. According to the scheme, precise management and control of the intelligent robot are realized, and the operation efficiency of the intelligent robot is improved.

The present application relates to the technical field of bearing texture optimization, and particularly relates to a sliding bearing surface texture optimization method and system. The technical scheme of the present application comprises the following steps: obtaining a load curve of a load of a rotating shaft in a sliding bearing changing with running time and a rotating speed curve of a rotating speed changing with running time; determining characteristic working conditions of the sliding bearing in a main running cycle according to the load curve and the rotating speed curve; performing feasible region analysis on each texture parameter of the sliding bearing according to the characteristic working conditions; taking a numerical interval of each texture parameter as a friction performance prediction input of a BP neural network model, and combining a genetic algorithm to perform friction performance optimization in the numerical interval, so as to determine a parameter combination corresponding to optimal friction performance as target texture parameters of the sliding bearing. The technical scheme improves optimization efficiency and accuracy when performing global optimization on local texture of the sliding bearing.

The application discloses a sea ice image recognition analysis method and system based on YOLOv8, the method comprises the following steps: acquiring sea ice image or video stream data and carrying out pretreatment, the pretreatment comprises geometric correction and image enhancement; based on the pre-trained YOLOv8 model, the sea ice region of the pretreated sea ice image data is preliminarily detected, and the boundary box containing the sea ice is output; the region of interest is extracted based on the boundary box, the fusion algorithm of watershed segmentation combined with random forest classification is applied, and the pixel-level sea ice mask is generated; the pixel-level sea ice mask is post-processed, and the post-processing includes morphological operation and connected region analysis; according to the post-processed pixel-level sea ice mask, the sea ice coverage ratio is calculated, and the calculation result of the sea ice coverage ratio is dynamically displayed at the predetermined position of the user interface. The application realizes the full-process automation from image input to risk output, without the need for personnel to continuously monitor and prevent, thereby reducing the labor intensity and human error rate.

The invention discloses a virtual power plant distributed optimization and feasible region analysis method and system. The method comprises the following steps: establishing an interactive physical model of a virtual power plant and a power distribution network; based on an interactive physical model, relaxation decoupling is carried out on wide-area and neighborhood time coupling constraints caused by the state of charge of the energy storage system through a Lyapunov optimization theory, and a multi-period optimization problem of the virtual power plant is converted into a single-period optimization sub-problem; aiming at the single-period optimization sub-problem, adopting a particle swarm optimization algorithm HSIPSO improved based on a hybrid strategy and fusing the particle swarm optimization algorithm HSIPSO into an ADMM distributed optimization framework to form an ADMM-HSIPSO algorithm, and carrying out distributed solution on the single-period optimization sub-problem; converting a multi-period virtual power plant feasible region representation problem into a calculation problem of each scheduling period feasible region; a mathematical model of the feasible region of the virtual power plant is constructed, and an ADMM-HSIPSO algorithm is adopted for solving; the coordination and unification of the optimal distribution of the dispatching power of the power distribution network among the virtual power plants and the privacy protection in the virtual power plants are realized.

The invention discloses a surrounding rock damage degree identification system based on machine vision, and the system comprises an image collection module which is used for collecting a surrounding rock surface image of a tunnel face; the image acquisition module is arranged and fixed on an acquisition site through a bracket; the intelligent processing module is connected with the image acquisition module and is used for receiving and processing the surrounding rock surface image, a built-in deep learning model, synchronously identifying surrounding rock cracks and a stratified rock stratum structure and performing regional analysis to output a rock mass crushing degree grading result; the display module is connected with the intelligent processing module and is used for displaying the surrounding rock surface image and the analysis result; and the power supply module supplies power to the image acquisition module, the intelligent processing module and the display module. According to the invention, on the basis of a dual-task collaborative learning mechanism of a unified deep learning model architecture, a multi-scale feature fusion and refined blindage information extraction technology and detection innovation device configuration, automatic, real-time and accurate recognition of surrounding rock information is realized, and support is provided for optimization of tunnel blasting parameters.

The invention relates to the technical field of networked vehicle transverse control system faults, and discloses a networked vehicle transverse control system fault estimation method based on an interval observer, which comprises the following steps: establishing a vehicle transverse dynamic model, and taking a transverse displacement error, a transverse displacement error change rate, a yaw angle error and a change rate as state variables; and constructing a continuous time state-space equation, discretizing to establish an information physical system model, and constructing a ZA-based multi-cell body region analysis method and a performance intermediate observer based on the intermediate variable. According to the method, the vehicle group formation system is abstracted into a typical information physical system model, deep fusion of a vehicle physical layer and a calculation network layer is realized, the core characteristics of a CPS system are reflected, the design freedom degree and the estimation precision are improved by introducing a novel interval observer with double auxiliary variables, and the estimation accuracy is improved. An interval fault estimation method is designed in combination with a set membership estimation thought, the robustness to unknown input is enhanced through intermediate variables, and the overall reliability of the system is improved.

This application proposes a document processing method, system, electronic device, and storage medium. The document processing method includes: acquiring a document to be processed; classifying the document to be processed into different file types and generating document classification results; based on the document classification results, calling the corresponding layout type region analysis model to analyze the document to be processed and generating region results for different layout types; parsing the region results for different layout types to obtain region-level structured data; obtaining resource data when there are regions in the region results for different layout types whose content cannot be fully expressed by text; extracting document metadata from the document to be processed and the region-level structured data to obtain document-level structured data; and generating a structured dataset based on the document to be processed, the region-level structured data, the resource data, and the document-level structured data.

The application discloses a kind of based on machine vision and artificial fish school optimization's magnetic core defect adaptive detection method, comprising: adopting OTSU adaptive threshold segmentation to realize workpiece and background separation, and the main body area of magnetic core is enhanced by morphological operation;Secondly, the geometric characteristics of magnetic core are determined by connected region analysis and minimum circumscribed circle fitting, and annular detection area focusing edge detection is constructed;Thirdly, based on CLAHE enhancement and dynamic adaptive threshold calculation, the preliminary identification of defect is realized;Then, through morphological optimization and contour screening, effective defect is accurately extracted;Finally, the key parameters are automatically optimized using artificial fish school optimization algorithm, and the detection result under the optimal parameters is output.The application can realize high-precision detection of magnetic core edge defects, and the detection efficiency is high, and the detection parameters can be adaptively adjusted, and can adapt to different specifications, different surface state of magnetic core product detection demand.

The application discloses a power grid-based distributed load early warning management method and system, relates to the field of power system automation, and solves the technical problem that the prior art ignores the priority state of each region during early warning, so that when a plurality of regions simultaneously perform data analysis, the region in urgent need of early warning cannot be timely and effectively analyzed, and the early warning efficiency is low; the region analysis priority is generated according to regional power grid data; a regional power grid topology graph is constructed; the regional predicted load is generated according to the region analysis priority and the regional power grid topology graph, and the equipment adjustment parameter is generated according to the regional predicted load; the alarm signal is generated according to the regional predicted load, and the power grid data of each region is analyzed from multiple angles, so that the important region can be preferentially paid attention to during load early warning, and the corresponding equipment parameter is dynamically adjusted according to the load prediction of each region, so that the accuracy of data is improved during high-load early warning. The efficiency and accuracy of early warning are improved.

The invention discloses a crack global optimization reconstruction method and system for structure detection. The method comprises the following steps: preprocessing a structure surface image, detecting cracks, and analyzing a connected region to obtain a discrete crack fragment set; extracting geometrical characteristics of each fragment, and calculating a fragment pair space adjacent distance to construct a candidate connection set and a crack connection graph; constructing a feature vector based on the connection probability features, and combining a likelihood model and a structure prior model to form a probability graph model; and after the problem is converted into an energy minimization problem, performing iterative optimization through a maximum posterior probability inference algorithm dynamically updated by a structural state, screening an optimal connection relation set, and combining crack segments to realize global optimization reconstruction. According to the invention, the accuracy and continuity of crack detection in a complex scene can be improved.

The application discloses a regional analysis method based on a space grid index, and relates to the field of geographic information system big data space analysis.The application comprises the following steps: creating a geographic feature point thematic data table, creating a grid index field for the geographic feature point thematic database table, creating a thematic data table, establishing a mapping relationship between each geographic feature surface record and the grid index table, calculating an index value when the query precision is n according to the created grid index value k, establishing a space relationship between each geographic feature point and the geographic feature surface, and screening out a statistical analysis result meeting the condition.The application has the advantages that the grid index field is established, the index creation cost is reduced, the maintenance cost is decreased, multi-size space indexes are supported, the precision requirements of different businesses are met, the retrieval performance is further improved when the precision requirement is not high, the operation efficiency is greatly improved by adopting hexadecimal character matching, and good scalability is maintained in the regional analysis direction.

The invention relates to the technical field of adhesive product detection devices, in particular to a non-setting adhesive product detection device and method.The device comprises a data acquisition module used for acquiring image frames and fitting state vectors through an industrial camera and an incremental encoder respectively; the image correction module is used for inputting the image frame into the extended affine model to generate a standard correction image; the reference image generation module is used for inputting the standard correction graph into the image encoder, the full connection network and the image decoder to output a defect-free reference image; the image analysis module is used for calculating a pixel-level difference image between the defect-free reference image and the standard correction image, performing adaptive threshold processing on the pixel-level difference image and generating a binary mask; and the decision execution module is used for performing connected region analysis on the binary mask, generating a defect center point coordinate, a defect bounding box and a defect region area, and mapping the defect center point coordinate back to the space coordinate system of the fitting system.

The application relates to a science and technology theme trajectory analysis system and method based on map visualization, wherein the system comprises: a map visualization module for collecting and processing geographical position information of global scientific research institutions to generate map tiles containing point position information, and rendering and interacting the geographical spatial distribution of the scientific research institutions; a multi-source data acquisition and processing module for acquiring paper data and / or patent data from at least one target database in a semantic manner; an AI analysis module for identifying institutions in a region and calculating scientific research density in the region through frame selection based on the paper data and / or the patent data, and generating a professional analysis report containing data insights according to a user-selected analysis object and target; and a user interaction interface module for dynamically adjusting display content according to user operations to perform point clustering, zooming adaptation and intelligent label display. Thus, the problems of weak geographical spatial visualization capability in the related art, inability to support large-scale real-time rendering and interactive regional analysis, data retrieval and analysis fragmentation and lack of AI automatic report generation function, and complicated user operation are solved.

The embodiment of the invention provides an image detection method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: dividing a panoramic image of a to-be-detected region into a plurality of image blocks, and extracting a region image containing a marker from the image blocks through an image detection model; according to the corresponding relation between the area image and the panoramic image, the marker is merged into the panoramic image to obtain a target panoramic image; obtaining a region of interest corresponding to the to-be-detected marker in the target panoramic image, and obtaining a central point of the region of interest; and performing connected region analysis on the region of interest according to the central point, determining a connected region closest to the central point in the region of interest, and determining the center of the to-be-detected marker according to the connected region. According to the invention, marker detection and marker center detection are combined, so that marker automatic detection is realized, and the detection precision and the detection speed are improved.