35 results about "Color normalization" patented technology

The invention discloses a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model, belongs to the field of cervical cell image recognition, and particularly relates to the cervical cell image intelligent diagnosis system based on the multi-modal visual language large model. In order to solve the problems of low diagnosis accuracy, strong subjectivity, insufficient efficiency and lack of interpretation in the prior art, the invention provides a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model. The system comprises an image block acquisition module, an image preprocessing and color normalization module, an effective tissue region screening module, a cell region extraction and post-processing module, an image end and text end processing module, a visual language large model acquisition and training module, a cell description text generation module in an inference stage, and a risk judgment and classification module. An interpretability verification and credibility evaluation module; and a structured diagnosis report output module.

The invention relates to the technical field of printed matter quality detection and computer vision and the technical field of artificial intelligence systems in the production field, in particular to a color box printed matter printed pattern visual detection algorithm. The invention discloses a visual detection algorithm for a printing pattern of a color box printed matter. The visual detection algorithm comprises the following steps of image acquisition, preprocessing, feature extraction, reference comparison, defect evaluation and defect classification. According to the scheme, the accuracy and the real-time performance of complex printing pattern defect detection are improved. A light-weight multi-scale attention convolutional neural network and a self-learning mechanism are combined, defects in various printing patterns such as block colors, fine characters and graph gradient are effectively detected, and human intervention and misjudgment are reduced. A defect saliency map is generated through color normalization, geometric correction preprocessing and reference map comparison, an online misjudgment cache and incremental learning strategy is introduced, self-adaptive optimization of a model along with time is kept, and real-time high-precision quality monitoring of an offset printing color box production line is achieved.

The invention relates to the technical field of image processing, and discloses a gynecological cell morphology intelligent identification method and system based on machine vision, and the method comprises the steps: collecting an original image of gynecological cells, carrying out the color normalization processing of the collected original image, carrying out the denoising processing through employing a wavelet threshold value, and carrying out the image enhancement through employing adaptive histogram equalization; according to the method, color consistency errors of different batches of dyed images are reduced, noise introduced in the image acquisition process is eliminated, cell structure details are reserved, and the accuracy of diagnosis is improved. And then image enhancement is carried out, the contrast ratio of cell nucleuses and cytoplasm is improved, so that the characteristics in the cells are more obvious, segmentation can be more accurately completed during subsequent image segmentation, the lesion level of the gynecological cells can be accurately identified, and the accuracy of an identification result is ensured.

The invention discloses a printing defect data generation method and device and a printing defect detection method and device, and relates to the technical field of printing defect detection.The method comprises the steps that processing is conducted on the basis of a first reference image and a to-be-detected image which are accurately aligned, and candidate defect areas are extracted from the to-be-detected image through a target detection algorithm or a target segmentation algorithm or an anomaly detection algorithm; and jointly inputting the candidate region and the corresponding region in the first reference image into a twin classification network for authenticity judgment. The method comprises the following steps: firstly, registering and aligning a second reference image and a defect-free to-be-detected image, implementing color standardization processing, estimating printing quantity distribution of each color channel in a CMY color space, and simulating printing defects in a single-channel printing quantity attenuation mode; and generating a training image containing a missing defect and a color cast defect through color space conversion. Through collaborative optimization of data generation and detection algorithms, the accuracy and generalization ability of printing defect detection are effectively improved.

The invention discloses a method based on polychromatic light DIP board preprocessing and defect classification, which comprises the following steps: S1, determining an expected performance index, evaluating the current situation of a current manual detection method and a defect detection method based on computer vision, analyzing the main problems of the methods, listing the technical problems needing to be solved of a project, and determining the performance index of the project; evaluating the feasibility of the technical scheme, and considering the cost, the technical difficulty and the required time, for example, improving the image quality, enhancing the contrast ratio of a defect area, improving the detection precision and the like; and S2, designing an overall architecture of the system, determining main modules including an image acquisition module, a color normalization module, an image preprocessing module, a feature extraction module and a defect classification module, defining specific functions and input and output of each module in detail, selecting suitable hardware, and selecting or developing adaptive software tools. The method is easy to implement and expand, can meet the detection requirements of DIP chip boards of different specifications and types, and has good universality and flexibility.

The invention discloses a fresh tea leaf sorting method based on a frequency domain tree topology network, computer equipment and a storage medium. The method covers the complete process of image acquisition, preprocessing, frequency domain decomposition, deep modeling, map construction and classification. Firstly, image quality is improved through color normalization and edge enhancement, and frequency domain tree decomposition is carried out through wavelet transform and discrete cosine transform to extract multi-scale features. And then, fusing long and short range dependent modeling and a residual convolution module to realize multi-level feature representation, and constructing a tree topology attention path and a structure map for simulating a bud-leaf-vein relationship to enhance semantic understanding. A tree structure is adopted to perceive a classification function, and fine-grained classification of single bud, one bud and one leaf, one bud and two leaves and one bud and multiple leaves is achieved. In training, the robustness of the model is improved by combining cross entropy loss, data enhancement and a regularization strategy. The method is high in classification accuracy and good in stability on a plurality of tea image data sets, and the practical level of automatic fresh tea leaf sorting is effectively improved.

The invention discloses a skin image abnormal region detection method based on a convolutional neural network, and relates to the technical field of medical image analysis, and the method comprises the following steps: S1, carrying out adaptive illumination and color normalization processing and two-dimensional fast Fourier transform; s2, multi-scale representation is fused in a cross-scale mode; s3, learning the dynamic weight of the multi-scale features and carrying out weighted summation; s4, through improving a DANet model, executing double-path processing of Fourier domain semantic modulation and morphological prior space attention, and gating bidirectional aggregation; s5, carrying out binarization and connected domain analysis, and extracting candidate focus areas; s6, extracting an instance-level feature vector, and estimating a corresponding cognitive uncertainty value; and S7, performing graph relation reasoning and multi-head decoding. According to the method, the limitations of neglect of association between lesions, single evaluation dimension and poor prediction generalization ability in a traditional method are effectively overcome, and an efficient and reliable solution is provided.

The invention belongs to the technical field of pathological diagnosis, and provides a colorectal cancer preoperative lymph node metastasis prediction method based on deep learning and a pathological HE slice, and the method comprises the steps: firstly obtaining an HE slice WS I image of preoperative intestinal endoscopic biopsy of a colorectal cancer patient; performing feature extraction on the WSI image by using a Transform network based on multi-instance learning; a dyeing normalization technology is introduced, and color standardization processing is carried out on the WSI image; according to the colorectal cancer lymph node metastasis prediction method, deep learning and pathological HE slices are combined, the digital level of colorectal cancer lymph node metastasis prediction is improved, by processing and analyzing the WSI images of the HE slices, the model can accurately extract pathological features and generate prediction results within seconds, and the timeliness of clinical decisions is remarkably improved; meanwhile, the model adopts a dyeing normalization technology, and image features are extracted in different channels; the model can keep stable performance in various clinical environments and data sources, and accurate issuing of treatment decisions is promoted.

The invention discloses a mulberry leaf picking and positioning method and device based on machine vision, and relates to the technical field of machine vision. The method comprises the following steps: acquiring calibrated and synchronized multi-modal image data of a mulberry target area, wherein the data can be a binocular sequence or a depth camera color-depth pair; performing preprocessing including brightness color normalization and image degradation compensation on the data to obtain an enhanced frame; inputting the enhanced frame into a target detection and maturity evaluation joint network, identifying each mulberry leaf instance, and outputting an instance segmentation mask defining the contour of the mulberry leaf instance and a probability vector representing the maturity; based on the instance segmentation mask and depth information, pixels in the mask are converted into a three-dimensional leaf surface point cloud; according to the point cloud, the six-degree-of-freedom pose of each mulberry leaf target is obtained, and at least one candidate picking site is determined; and finally, performing priority ranking on the candidate picking sites according to a preset comprehensive scoring function, and generating a scheduling queue containing timestamps, six-degree-of-freedom poses and priorities.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

This invention relates to the field of image recognition and detection technology for foodborne pathogens, and discloses a method for identifying Listeria monocytogenes based on image recognition. The identification method includes: acquiring images of chromogenic culture medium plates and converting them to Lab and HSV color spaces; performing adaptive color normalization based on the background region of the culture medium; performing colony instance segmentation on the normalized image and extracting extended regions of interest; extracting saturation distribution sequences along radial rays and detecting halo transition patterns using first-order difference; statistically analyzing halo angle coverage and average transition amplitude, and combining halo quantization feature vectors; inputting the feature vectors into a gradient boosting decision tree classifier to output colony identification results. This invention solves the technical problems of unstable classifier discrimination boundaries caused by color shifts between different batches and the difficulty in detecting halos caused by weak lecithinase reactions.

The application discloses a nasopharyngeal carcinoma prognosis feature determination method, system and device and a storage medium. The method comprises the following steps: pathological image preprocessing, color normalization based on dye separation is used to standardize the dyeing of the pathological image; a segmentation network is used to automatically segment the lesion area of the preprocessed pathological image to obtain a segmented image; the segmented image is cropped to obtain a target image block; principal component analysis is used to reduce the dimension of the target image block to obtain reduced dimension data; a clustering algorithm is used to perform unsupervised autonomous learning on the reduced dimension data to obtain pathological image features; and finally, the pathological image features are screened through feature inspection to determine a prognosis pathological feature set. The application can obtain and screen key image features of pathological images closely related to local area recurrence and distant metastasis of nasopharyngeal carcinoma from pathological images to assist in prognosis prediction of nasopharyngeal carcinoma, and can be widely applied to the technical field of image processing.

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The present application relates to the technical field of image processing, and discloses a light staining sperm microscopic image light-weight style consistency preprocessing method and system, comprising: acquiring a light staining sperm microscopic image, performing color normalization on the light staining sperm microscopic image according to a deep staining sperm microscopic image, performing local contrast enhancement on the normalized light staining sperm microscopic image according to local contrast of a sperm head, a tail and a boundary region, performing brightness correction on the enhanced light staining sperm microscopic image according to the definition of the sperm head, the middle section and the tail structure and the background stability, and obtaining a light staining sperm microscopic image after light-weight style consistency preprocessing. The present application can solve the problems of staining difference, uneven brightness and boundary blur through image preprocessing without increasing the number of samples and additional training burden, improve the overall quality of the light staining sperm microscopic image, and improve the detection performance of the subsequent model.

The invention discloses an immunotherapy curative effect prediction method based on pathological section deep learning scoring, and the method comprises the steps: pathological image acquisition, image segmentation, image quality evaluation and screening, image color normalization, feature extraction and model training, and patient-level score generation and curative effect prediction index establishment, using a digital slice scanner to scan the Hamp of the small cell lung cancer patient; e, digitizing the dyed tissue sections to obtain high-resolution WSI images, and segmenting the images, namely segmenting each WSI image into a plurality of image blocks with fixed sizes to form a standardized tile image set. According to the immunotherapy curative effect prediction method based on pathological section deep learning scoring, through data standardization processing, image segmentation, quality screening and color normalization, staining difference and invalid data interference are reduced, input data quality is improved, and through efficient feature extraction, deep features of pathological images are automatically learned by using a Resnet50 model.

The application discloses a kind of multi-scale feature extraction method, system, equipment and storage medium of reinforced image similarity matching, belong to machine learning field, solve the problem of low precision and efficiency of complex image similarity matching;Including: the color normalization of received image is normalized and using interpolation method or image edge is reduced to obtain preprocessed image;Preprocessed image and target image are simultaneously input into twin neural network, and after neck network is convolved, the multi-layer feature map corresponding to each image is obtained and residual connection is carried out;Fusion feature map of semantic information and spatial information fusion is obtained using PAN;SPP is introduced to generate multi-layer long width depth fixed standard feature map, the weight sharing of full connection layer is realized;According to the efficiency weight of task machine, the standard feature map is spliced and input into full connection layer, according to the task type, the corresponding output layer network is selected, and the prediction result is obtained.The application improves the precision and efficiency of complex image similarity matching by combining SPP and PAN.

The invention discloses a non-abandoned pattern rendering enhancement method based on generative image processing. The method comprises the steps of non-abandoned pattern collection, image preprocessing, enhancement generator model construction, discriminator model construction and non-abandoned pattern rendering enhancement. The method comprises the following steps: acquiring original image data through a non-missing pattern; image preprocessing methods of color normalization, random visual confusion, geometric space deformation, multi-scale image generation and data set segmentation are adopted; the enhancement generator model outputs a non-abandoned pattern rendering enhancement result, and the accuracy of an output image is ensured by simulating a cognitive process formed by layering patterns and integrating cultural implied meaning description and process physical characteristics at the same time; a four-level parallel heterogeneous model is adopted as a discriminator model, a multi-level identification system is constructed, and a generated image is evaluated from four dimensions of pixel authenticity, pattern structure rationality, image and text culture semantic consistency and process feature accuracy, so that high fidelity of an enhanced result is ensured.

The invention discloses an apple stem and calyx identification method based on binocular vision multi-mode fusion, which comprises the following steps: synchronously acquiring RGB images of a left view angle and a right view angle of an apple, calculating a disparity map according to the RGB images of the left view angle and the right view angle and generating a depth image, and carrying out space calibration and pixel-level registration on the RGB images and the depth image; performing color normalization, reflection suppression and brightness compensation processing on the registered RGB image, and performing hole filling and filtering smoothing processing on the registered depth image; performing multi-scale fusion on the processed RGB image and the depth image, and constructing multi-modal input data; and inputting the multi-modal input data into a semantic segmentation network based on an encoder-decoder architecture, and outputting a pixel-level semantic segmentation mask of the stem and the calyx based on the semantic segmentation network to obtain an identification result. According to the method, the problem of confusion of fruit stems / calyx and fruit defects is effectively avoided, so that the recognition precision is further improved.

The invention provides a tumor pathological image typing and grading method and device based on deep learning and a residual network technology, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a tumor pathological image, segmenting a tissue region, carrying out the color normalization, and dividing the tumor pathological image into image blocks; constructing a deep convolutional feature extraction network based on a residual network, and extracting shallow and deep feature maps; performing up-sampling on the deep feature map and performing weighted fusion on the deep feature map and the shallow feature map, and inputting the deep feature map and the shallow feature map into parallel convolution branches to obtain typing and grading feature maps; calculating a feature deviation degree based on the hierarchical feature map to generate a space attention map, and determining a region focusing degree; enhancing the typing feature map by using the attention weight; respectively carrying out global pooling and classification on the enhanced feature map and the graded feature map, and outputting a typing and grading result; screening high-focus points according to an area focusing degree set threshold value, and generating a key area thermodynamic diagram; according to the invention, typing and grading cooperative processing is realized, and the diagnosis efficiency and accuracy are improved.

The invention discloses a tongue picture normalization algorithm based on a checkerboard, and the algorithm comprises the steps: taking a white block and a black block in the checkerboard as the basis of normalization, placing a hard checkerboard at a position close to the oral cavity of a patient, and carrying out the shooting of a tongue picture, and obtaining a tongue picture image; a line of checkerboard, closest to a shooting window, in the tongue picture image is used as the basis of tongue picture normalization processing, normalization processing is carried out, and the process comprises tongue picture color normalization, tongue picture brightness normalization and tongue picture malformation correction. According to the invention, normalization processing is carried out on the tongue image, so that the method has the advantages that the image is more real and the judgment is relatively more accurate.

The invention discloses a road ecological landscape dynamic classification method and system based on landscape visual features, and relates to the field of artificial intelligence, and the method comprises the steps: constructing a data set containing image-level and pixel-level double-layer annotations; preprocessing the image through self-adaptive illumination-color normalization in combination with local statistics and global estimation; carrying out image block division and recombination by adopting semantic guidance; constructing an end-to-end classification model, deeply fusing visual and semantic features, modeling a spatial layout and semantic similarity relationship, and realizing landscape type discrimination; and utilizing the training model to automatically classify the new image. According to the method, the robustness to complex illumination can be effectively improved, structured image blocks rich in semantics are formed, multi-scale feature interaction and discrimination are enhanced, and feature representation consistent in semantics is learned.

The application discloses a color standardization method and system for digital pathological images, and the method comprises the following steps: obtaining a colored digital pathological image from a target domain; correcting the digital color difference of the digital pathological image to serve as a reference image; performing color amplification on the reference image to simulate different staining differences, and obtaining a plurality of color amplified images; establishing a similarity map of the reference image and the corresponding plurality of color amplified images; inputting the similarity map into a de-staining model to eliminate the color information caused by staining, and outputting a plurality of gray feature maps corresponding to the plurality of color amplified images and retaining the information related to human tissues, cell structures and morphological distribution; inputting the gray feature maps corresponding to the color amplified images into a virtual staining network model to virtually color the gray feature maps of the color amplified images, and outputting a color image which is color-standardized and has similarity in color between the coloring color and the color of the reference image. The application is a color standardization method without using source domain images.

The invention relates to the technical field of image processing, in particular to a purple sand ware identification method and system based on image processing, and the system comprises a data collection module which is used for collecting purple sand ware images and environment data; the preprocessing module is used for performing color normalization processing on the purple sand device image to obtain a corrected image; the region division module is used for performing region division on the corrected image according to the gradient of each pixel point of the corrected image in the horizontal direction and the gradient of each pixel point in the vertical direction so as to obtain a purple sand device region; the color feature analysis module is used for constructing a color coefficient of the purple sand ware area; the textural feature analysis module is used for analyzing textural features of the purple sand ware area; the gloss characteristic analysis module is used for analyzing the gloss characteristics of the purple sand ware area; and the judgment module is used for analyzing the pug type of the purple sand ware. According to the method, the accuracy of purple sand ware pug type analysis is effectively improved, and the identification efficiency of the purple sand ware is improved.

This invention relates to the field of image processing technology, and more particularly to a method and system for identifying Zisha (purple clay) teapots based on image processing. The system includes: a data acquisition module for acquiring images of the Zisha teapot and environmental data; a preprocessing module for color normalization of the Zisha teapot image to obtain a corrected image; a region division module for dividing the corrected image into regions based on the horizontal and vertical gradients of each pixel to obtain Zisha teapot regions; a color feature analysis module for constructing color coefficients for the Zisha teapot regions; a texture feature analysis module for analyzing the texture features of the Zisha teapot regions; a gloss feature analysis module for analyzing the gloss features of the Zisha teapot regions; and a judgment module for analyzing the type of clay used in the Zisha teapot. This invention effectively improves the accuracy of Zisha teapot clay type analysis and increases the efficiency of Zisha teapot identification.

The application discloses a drilling stratum AI logging method and system based on machine vision, collects multispectral drilling core image and multi-view ring scanning core surface image and depth identification information, generates a color consistent image group through light-color normalization registration; generates a lithology discrimination feature set through image partition splicing, lithology end member decomposition and grain size classification feature extraction; associates the lithology discrimination feature set with the depth identification information to establish a depth-lithology contrast index, identifies a stratum interface position through compaction compensation and cycle direction analysis, implements feature out-of-limit detection to generate a mutation trigger identification; extracts an interface transition zone feature slice according to the mutation trigger identification, constructs a depth-image analysis profile through multi-sampling point edge trend continuity evaluation and geometric distortion correction; finally, performs lithology partition boundary positioning and lithology classification comparison, and outputs a standardized image logging report, effectively improving the accuracy of lithology interface identification and the standardization degree of logging results.

The invention discloses a multi-scale feature extraction method, system and device for enhancing image similarity matching and a storage medium, belongs to the field of machine learning, and solves the problem that the similarity matching precision and efficiency of complex images are low. Comprising the following steps: normalizing the color of a received image and obtaining a pre-processed image by using an interpolation method or an image edge splitting method; inputting the pre-processed image and the target image into a twin neural network at the same time, performing convolution through a check network to obtain a multi-layer feature map corresponding to each image, and performing residual connection; using PAN to obtain a fusion feature map of semantic information and spatial information fusion; sPP is introduced to generate a plurality of layers of standard feature maps with fixed length, width and depth, and weight sharing of a full connection layer is realized; and according to the efficiency weight of a task machine, splicing the standard feature maps, inputting the spliced standard feature maps to a full connection layer, and selecting a corresponding output layer network according to a task type to obtain a prediction result. According to the method, the precision and efficiency of similarity matching of the complex images are improved through combination of the SPP and the PAN.

The invention discloses a heating radiator installation quality detection method based on image segmentation, belongs to the technical field of heating radiator installation quality detection, and aims to improve the accuracy of heating radiator installation quality detection and reduce defect false detection and missing detection. The method comprises the steps of performing standardization and quality verification processing on a heating radiator installation image to obtain a heating radiator installation standard image, and performing filtering denoising, illumination correction and color normalization processing to obtain a heating radiator installation to-be-segmented image; a heating radiator overall mask is obtained from the heating radiator installation to-be-segmented image, a segmentation area is limited by means of the heating radiator overall mask, and sub-component masks are extracted in combination with the heating radiator installation to-be-segmented image; screening and smoothing sub-component masks, extracting multi-dimensional features, and inputting the multi-dimensional features into a machine learning model after standardization to obtain a defect existence probability; and carrying out structure, connection and installation offset defect judgment on the defect existence probability according to the differentiated defect confidence threshold. Various installation defects of the heating radiator can be accurately identified, and the detection reliability is improved.

The invention discloses a multi-literature mixed character recognition method and system based on deep learning. The method comprises the steps of text image collection, primary data processing, text detection model construction, text recognition model construction and multi-literature mixed character recognition. According to the invention, original image data is obtained through text image acquisition; a data primary processing method of color normalization, random visual confusion, geometric space deformation, multi-scale image generation and data set segmentation is adopted; a multi-granularity deep learning model is adopted as a text detection model, features of different granularities are captured through an adaptive receptive field mechanism, and sub-tasks are mutually enhanced by utilizing the synergistic effect between the tasks; a two-way context network model is adopted as a text recognition model, feature expressions for different texts are fused through a dynamic routing mechanism, two-way time sequence modeling and visual semantic alignment are combined, context dependence of character sequences is considered, and the method is anchored to visual evidences.

The invention relates to a method for realizing high-precision identification of fine inclined character labels for an electrical cabinet pressing plate. The method comprises the following steps of executing operations of color normalization, brightness equalization and distortion correction; character area detection is carried out, and a small character label area is automatically positioned; performing multi-scale feature extraction; normal texts and inclined texts are automatically distinguished; performing rotation, affine or perspective transformation; performing digital full-angle to half-angle conversion, character misrecognition correction, fuzzy word normalization, semantic rule replacement and context consistency verification on the output recognition result; and carrying out structure optimization on the trained identification model. According to the method and the device for realizing high-precision identification of the fine inclined character label for the electrical cabinet pressing plate, the processor and the computer readable storage medium thereof, by combining text type classification, geometric correction and a model acceleration optimization strategy, the identification precision, the robustness and the deployment efficiency of a system in a complex industrial scene are remarkably improved.