50 results about "Graph cut algorithm" patented technology

The invention discloses a method and a system for establishing a comprehensive load model containing a distributed power supply, and relates to the technical field of load model establishment, after the establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.

The invention discloses a multi-source ship target identification method based on two-stage collaborative fusion, and relates to the technical field of signal processing, and the method comprises the steps: firstly collecting target sea area data, including original optical image and radar image data, by means of an optical camera and a radar sensor on an aircraft, and then carrying out the preprocessing of the collected data through Gaussian filtering; performing sea-land segmentation by using a deep learning algorithm, constructing a loss function optimization network parameter to obtain a segmentation mask, and removing a land background area according to a mask result to obtain processed optical and radar images; an improved SURF feature and graph cut algorithm combined registration method is adopted, optimal transformation parameters are obtained by screening feature point pairs and solving the minimum value of an energy function, and image registration is achieved; for the registered images, fusing the images based on an adaptive fusion rule; and finally, extracting corresponding features through manual feature extraction and feature extraction based on deep learning, and fusing the features to obtain a final target recognition result.

The invention discloses a distributed power supply load model establishment method and system, and relates to the technical field of load model establishment, after an establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The embodiment of the invention discloses a point cloud data vectorization method and device, and the method comprises the steps: carrying out the classification of a plane structure of a target indoor space based on a point cloud data image of the target indoor space, and obtaining a plurality of types of plane structures; determining a target plane structure from the plurality of types of plane structures; and performing graph cut algorithm processing on the target plane structure to obtain a vectorized data structure corresponding to the point cloud data image of the target indoor space so as to express a topological structure of the target indoor space through the vectorized data structure. Therefore, a full-automatic vectorization scheme for the point cloud data image of the target indoor space is realized, the topographic line drawing efficiency of the target indoor space can be improved, and the consistency of the measurement size is ensured, so that the topographic line drawing accuracy of the target indoor space is improved.

The invention discloses a method and equipment for segmenting large intestine effusion and a storage medium. The method comprises the following steps: extracting a target skeleton with the maximum skeleton volume from a three-dimensional intestinal image, and taking the target skeleton as an initial segmentation region; based on the initial segmentation region, performing contour extraction by using an active contour model to obtain an intestine segmentation result; calculating a minimum surrounding circle of the intestine segmentation result in the cross section area; fitting the probability distribution of the large intestine region and the probability distribution of the large intestine effusion region in the minimum surrounding circle by using a Gaussian mixture model; and inputting the fitting result into a graph cut algorithm model to carry out effusion segmentation so as to segment a large intestine effusion region from the large intestine region. By means of the scheme, the large intestine effusion area can be accurately extracted, and the complete large intestine area can be segmented by combining the large intestine effusion area with the large intestine area subsequently.

The invention relates to an artificial intelligence image segmentation processing method and device, equipment and a medium. The method comprises the following steps: acquiring pixel intensity distribution of an input image and extracting local features by using a convolutional neural network to generate a pixel intensity change feature map; on the basis of the feature map, boundary area probability distribution is calculated by using a conditional random field model, and a zigzag edge is smoothed by using a graph cut algorithm to generate a continuous segmentation boundary; splicing the continuous segmentation boundary and an input image, inputting the spliced continuous segmentation boundary and the input image into a Transform enhanced U-Net network, balancing edge alignment loss and region overlapping precision loss through a dynamic weighted loss function, and generating an optimized segmentation mask; and performing threshold binarization processing on the optimized segmentation mask, removing isolated noisy points in combination with morphological closed operation, and generating a segmentation image matched with the physical defect form. By adopting the method, the continuity of segmentation boundaries and the matching precision of defect forms can be improved, and the problems of edge sawteeth and noise interference in traditional segmentation are solved.

The invention discloses a complex slope runoff simulation method based on multi-source data fusion. Comprising the following steps: fusing an unmanned aerial vehicle LiDAR, a ground penetrating radar and a remote sensing image, and considering physical coupling relationships such as vegetation root systems to generate a high-precision and physically consistent initial earth surface parameter field; the method comprises the following steps: identifying micro-topographic depression through a graph cutting algorithm and the like, and constructing a dynamically evolved slope overflow network based on a depression filling-overflow mechanism; establishing an overflow-scour-infiltration dynamic feedback regulation and control mechanism, wherein the scour intensity calculated according to overflow data is used for correcting the saturated hydraulic conductivity of the soil in real time, and corrected parameters are immediately fed back to an improved Green-Ampt infiltration model; through coupling solution of a multi-time scale algorithm, spatial and temporal distribution of slope runoff is output, and early warning and inversion of critical rainfall conditions can be triggered. The underlying surface dynamic feedback process can be simulated, the simulation reliability is improved, and technical support is provided for mountain torrent early warning.

The invention relates to the field of digital modeling, and particularly discloses a mine modeling method and system based on point cloud data, a terminal and a medium, and the method comprises the steps: obtaining original point cloud data of a target mine area; preprocessing the original point cloud data, including voxel filtering, to obtain target point cloud data; calculating the curvature and the normal vector of each point in the target point cloud data; inputting the coordinate, the curvature and the normal vector of each point as input values into a pre-trained geological category classification model to obtain the probability that each point belongs to various geological categories; constructing an energy function of a graph cut algorithm based on the probability that each point belongs to various geological categories and the point cloud features of the target point cloud data, and obtaining the final geological category to which each point belongs through the graph cut algorithm; and constructing a digital model of the target mine area based on the geological category to which each point belongs. According to the method, geological category prediction is carried out based on the multi-scale features of the point cloud, optimization is carried out in combination with the graph cut algorithm, and the classification efficiency and robustness are improved.

The invention discloses a method and device for counting microorganisms by using a combined image, and relates to the technical field of microorganism counting, and the method comprises the steps: extracting core feature points of a preprocessed microscopic image by using a convolutional neural network model, calculating the similarity between the core feature points, and obtaining a microorganism microscopic image containing a splicing position. And splicing the microbiological microscopic images by using a self-adaptive splicing algorithm, identifying and correcting appearing errors, and obtaining a spliced panoramic image. And inputting the spliced panoramic image into a graph cut algorithm, constructing an energy function of the panoramic image, finding out a segmentation path with the lowest energy by using a minimum cut algorithm, and generating a panoramic image of the microbial region. According to the method, the matching precision between adjacent images and the stability of geometric transformation are greatly improved, the segmentation precision of image segmentation is improved by utilizing the breadth-first search algorithm and the minimum cut algorithm, and the conditions of image dislocation and inconsistent overlapping are avoided.

The invention provides a pipeline defect detection method, electronic equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: performing coordinate conversion on an initial fisheye video stream to obtain a multi-frame plane image; for any frame of plane image, determining a binarization mask of the frame of plane image based on a semantic segmentation network, and extracting feature points of a pipe wall region in the frame of plane image by taking the binarization mask as a constraint to form a feature point set; determining an optimal suture line between any two adjacent frames of feature point sets by adopting a graph cut algorithm based on energy minimization, and carrying out splicing to obtain a pipeline panorama; and identifying the pipeline panorama by using a deep neural network model to perform defect identification to obtain a target defect. According to the method, the binarization mask is used as a constraint, and non-pipe wall area interference is eliminated; an optimal suture line is determined and splicing is completed based on an energy minimization graph cut algorithm, so that inter-frame splicing gaps and artifacts are effectively eliminated, and rapid and accurate detection of target defects can be realized.

The present application provides a method and system for wide-area ocean panoramic fusion based on multi-source data, the method comprising: acquiring multiple images and extracting calibration points of the images, calculating the parameters of the camera, selecting a reference image from the image using the minimum spanning tree method, and sorting the remaining images; using a direct linear transformation algorithm to obtain the transformation matrix between two images with overlapping areas in all images, and obtaining the projection transformation relationship between all remaining images and the reference image; using a bundle adjustment method to optimize the parameters and obtain the calibrated camera parameters; and using an improved graph cut algorithm to perform splicing and fusion processing on multiple images to generate a panoramic fusion image of the sea surface. The calibration point residuals are collaboratively optimized using the light speed adjustment method to obtain the calibrated camera parameters, thereby constraining the overall geometric structure of the camera. The improved graph cut algorithm can be applied to the sea surface splicing application scenario to achieve the fusion of sea surface panoramic images.

The invention discloses a circuit board welding spot detection method based on surface structured light, and the method comprises the steps: installing an electric rotatable polarizing film in front of a lens of a binocular camera, and synchronously obtaining stripe images at four orthogonal polarization angles in a structured light collection stage. A Fresnel equation and a micro-surface model are utilized to establish an analytic model of polarization state and phase error, pixel-by-pixel reflectivity suppression is carried out on original stripes, and active physical compensation of high reflection error is realized. A micro-area self-adaptive phase unwrapping algorithm based on welding spot topology priori is provided, a Markov random field with process priori constraint is constructed, a Graph Cuts algorithm is used for solving, and edge period dislocation is eliminated fundamentally. And a dynamic graph convolutional neural network is adopted to extract depth features of the welding spot cloud, and few-sample intelligent classification of each type of defects only needing 5-15 samples is realized through contrast quantity learning and a dynamic memory bank.

The application relates to a CAD vector data intelligent preprocessing method based on a multiple recognition mechanism, which comprises the following steps: parsing a DXF file to construct a CAD heterogeneous information graph; extracting text features for a layer node and generating a layer embedding vector through adaptive fusion of a gate unit by aggregating high-order semantic structure features through a meta path; introducing a dynamic threshold decision mechanism to generate a dynamic threshold according to global statistical features of a drawing to determine a background layer; constructing a global dependency graph containing cleaned dependent edges and semantic constraint edges, dividing the global dependency graph into weakly coupled connected subgraphs as independent optimization transactions by using a graph cut algorithm, and performing parallel execution to complete cross-dimension collaborative optimization; setting a multi-level logical checkpoint and generating an atomic operation log, and realizing tracing and accurate recovery based on an incremental storage mechanism. The application has the effects of significantly improving background layer recognition accuracy and scene adaptability, improving collaborative optimization efficiency, and guaranteeing operation safety and traceability.

The application discloses a kind of method and device for microorganism counting using combined image, it is related to microorganism counting technical field, including, using convolutional neural network model to extract the core feature point of preprocessed microscopic image, and the similarity between core feature point is calculated, obtain the microorganism microscopic image containing splicing position.Utilize adaptive splicing algorithm to the splicing of microorganism microscopic image, identify and correct the error that appears, obtain the panoramic image after splicing.Cut the panoramic image after splicing into graph algorithm, construct the energy function of panoramic image, find out the lowest energy segmentation path using minimum cut algorithm, generate the panoramic image of microorganism region.The application greatly improves the matching precision between adjacent images and the stability of geometric transformation, and the use of breadth-first search algorithm and minimum cut algorithm improves the segmentation accuracy of image segmentation, avoids the situation that image is misaligned and overlaps inconsistently.

The invention relates to the technical field of image processing, in particular to a large-range water area time-space dynamic change monitoring method fusing remote sensing and GIS, and the method comprises the steps: obtaining the hyperspectral remote sensing image data of a to-be-monitored water area in real time; aiming at each moment, constructing a feature vector of each pixel point and a center vector of each area label in the water area to be monitored by combining a normalized water index and a normalized vegetation index of each pixel point through gray difference and texture feature difference between each pixel point and an adjacent pixel point in the hyperspectral remote sensing image data and by combining the normalized water index and the normalized vegetation index of each pixel point; and constructing an energy function of a graph cut algorithm for segmenting the to-be-monitored water area, segmenting the to-be-monitored water area at each moment through the energy function, and carrying out spatial-temporal dynamic change monitoring on the to-be-monitored water area through segmentation results at different moments. The invention aims to improve the accuracy of monitoring the spatial-temporal dynamic change of the water area by improving the accuracy of area segmentation.

The invention relates to a computer implemented method for segmenting an image, comprising S0: obtaining a 2D / 3D initial image (I) of a region of interest of an element including a structure (B); S1: processing the 2D / 3D initial image (I) for initially segmenting the structure (B) delimited by a main outline (MO); S2: defining on the 2D / 3D image (I), a first area (A1) delimited by a first outline (1) including the pixels / voxels of the structure (B) and a second area (A2) delimited by a second outline (2) around and outside the structure (B), the first and the second areas (A1, A2) being function of a margin error of the first segmentation of step S1; the main outline (MO) being located between the first outline (1) and the second outline (2), an uncertainty zone (UZ) being between the first outline (1) and the second outline (2), the uncertainty zone (UZ) comprising pixels / voxels to be classified as belonging to the structure (B) or not; S4: applying a graph cut algorithm on a graph where the pixels or voxels of the first area (A1) are foreground seeds and the pixels or voxels of the second area (A2) are the background seeds, for affecting each node of the graph to the foreground or the background, the foreground corresponding to the pixels / voxels of the structure thus resulting in a refined segmentation; S5: classifying the pixels / voxels of the 2D or 3D image according to their distance to the main outline and / or a refined outline (RO

A power transmission line equipment three-dimensional model segmentation method based on deep learning comprises the following steps: performing over-segmentation on a three-dimensional model of power transmission line equipment to obtain a plurality of sub-patches, performing feature extraction on each sub-patch, clustering the extracted features, and inputting a clustering result into a neural network prediction model to obtain a neural network prediction model; the neural network prediction model is a bidirectional long-short-term memory network improved based on a principal component analysis network and an ScSR sparse dictionary, and hyper-parameters of the bidirectional long-short-term memory network are optimized by adopting a whale optimization algorithm improved by a hybrid strategy; and carrying out segmentation prediction by using the obtained optimal hyper-parameter, and finally adjusting the segmentation labels of the sub-patches by using a graph cut algorithm. According to the invention, on the basis of the prediction result of the neural network prediction model, the segmentation labels of the sub-patches are adjusted by using the graph cut algorithm, and the segmentation is further optimized, so that the segmentation effect of the segmentation edge is ensured.

The invention discloses a joint image segmentation method, which comprises the following steps of: S1, acquiring an image containing a joint to be segmented, judging whether metal exists in the image or not, and removing a metal area in the image when the metal exists; s2, acquiring a skeleton region image in the image obtained in the S1; s3, performing threshold segmentation on the skeleton region image obtained in the S2 to obtain masks corresponding to skeletons in the skeleton region image, and performing segmentation by adopting a graph segmentation algorithm to obtain each skeleton in the skeleton region image to obtain a skeleton segmentation image; s4, calculating the distance between each point in the skeleton segmentation image obtained in the step S3 and the nearest boundary thereof to obtain a distance map; obtaining a seed region of each skeleton according to the characteristics of the skeleton region in the joint, and marking to obtain a seed region label map; and performing semantic segmentation on the skeleton segmentation image obtained in the step S3 through a watershed algorithm. According to the method, joint image segmentation can be realized under the adhesion condition, and defects such as cavities do not occur in the segmented image.

The invention discloses a finished pastry appearance analysis method and device based on AI image analysis and recognition, and relates to the technical field of food detection. Comprising the following steps: S1, acquiring a multispectral image, and preprocessing the multispectral image to obtain a preprocessed multispectral image; s2, recognizing the preprocessed multispectral image through a set rotatable anchor frame, obtaining a pastry position posture detection frame, and cutting the pastry position posture detection frame through a graph cutting algorithm to obtain a cutting result; and S3, obtaining feature data according to the separation boundary, and determining a confidence degree according to the feature data. The problems that traditional manual detection is low in efficiency and difficult to cope with random postures and background interference are solved, and automatic classification and grading treatment of pastry fermentation states, foreign matter mixing and appearance defects are achieved.

The application discloses a kind of multi-source ship target identification methods based on two-stage cooperative fusion, it is related to signal processing technical field, first with the optical camera on the plane and radar sensor acquisition target sea area data, including original optical image and radar image data, then using Gaussian filter to the data collected is preprocessed;Next, using deep learning algorithm is carried out sea-land segmentation, constructs loss function and optimizes network parameter to obtain segmentation mask, then according to the mask result removes land background area, obtains the optical and radar image after processing;Afterwards, using the registration method that improved SURF feature and graph cut algorithm are combined, by screening feature point pair and solving minimum value of energy function, optimal transformation parameter is obtained, image registration is realized;For the image after registration, based on adaptive fusion rule to fuse image;Finally, corresponding feature is extracted by hand feature extraction and based on deep learning feature extraction and is fused to obtain final target recognition result.

The invention discloses a solid state disk storage optimization method and system, and relates to the technical field of data storage.The method comprises the steps that based on behavior layer data and physical layer data of a solid state disk, a topological coloring algorithm and a graph cut algorithm are used for conducting regional division on a storage unit; performing abnormal behavior detection on the storage unit of each area by using an isolated forest algorithm to obtain an abnormal storage unit; acquiring change increment data of the abnormal storage unit; on the basis of a neural network model, taking the feature data and the change increment data of the abnormal storage unit as input, and generating an optimization strategy set, so that different regions are subjected to different optimization processing; and on the basis of the optimization strategy set and under the principle that the optimization strategy of the abnormal storage unit is balanced with the overall optimization target, the storage of the solid state disk is optimized after an optimal execution strategy is obtained through multi-target adjustment and optimization. Different optimization strategies can be applied to the internal area, the boundary area and the external area of the solid state disk.

The present invention relates to the technical field of data clustering and integration, and discloses a multi-view financial data clustering and integration method and device. The method comprises: processing financial multi-view data using a K-Means clustering method to generate an ensemble pool, and then using a fuzzy membership function to generate a binary partition matrix; learning the global structure of the label space through the binary partition matrix to obtain an initial co-correlation matrix, and optimizing it using an LSR model with a Frobenius norm to obtain a label co-correlation matrix; learning a subspace projection matrix, an affinity matrix, and a sample adjacency matrix of each view, optimizing the co-correlation matrix from a feature space, and obtaining a feature co-correlation matrix; combining the label co-correlation matrix and the feature co-correlation matrix to obtain an optimized co-correlation matrix; projecting the optimized co-correlation matrix into a constraint space for constructing an undirected graph; and partitioning the undirected graph using a graph cut algorithm to obtain a final clustering integration result of the financial multi-view data.

The present application relates to the technical field of plate identification, in particular to a plate identification method based on geometric range discrimination. Mainly including the following steps: using image acquisition equipment, laser scanner and BIM model to obtain the geometric data of the plate, including boundary contour, surface morphology, spatial position and size information; extracting the boundary and key geometric feature points of the plate through graph cut algorithm; generating a three-dimensional geometric model by using multi-scale shape analysis algorithm; using Markov random field algorithm for geometric range discrimination to identify abnormal areas; comparing the BIM model design data by using iterative closest point algorithm to generate an identification report. The present application realizes accurate identification and range discrimination of building structure plates, effectively improving the monitoring and management efficiency of construction quality.

The invention relates to a computer implemented method for segmenting an image, comprising S0: obtaining a 2D / 3D initial image of a region of interest of an element including a structure (B); S1: processing the 2D / 3D initial image for initially segmenting the structure (B), the structure being delimited by a main outline (MO); S2: defining on the 2D / 3D image, a first area (A1) delimited by a first outline (1) including only pixel(s) / voxel(s) belonging to the structure (B) and a second area (A2) delimited by a second outline (2) around and outside the structure (B), the first area (A1) is obtained by reducing the main outline (MO) and the second area (A2) is obtained by expanding the main outline (MO); the main outline (MO) being located between the first outline (1) and the second outline (2), an uncertainty zone (UZ) being defined between the first outline (1) and the second outline (2), the uncertainty zone (UZ) comprising pixels / voxels to be classified as belonging to the structure (B) or not; S4: applying a graph cut algorithm on a graph where the pixels or voxels of the first area (A1) are foreground seeds and the pixels or voxels of the second area (A2) are the background seeds, for affecting each node of the graph to the foreground or the background, the foreground corresponding to the pixels / voxels of the structure (B) a second outline (RO) being defined as the boundary between the foreground and the background; S5: classifying the pixels / voxels of the 2D or 3D image according to their distance to the main outline and the second outline (RO) obtained at step S4 resulting in a refined segmentation.

This invention discloses an intelligent analysis method for low-altitude remote sensing data from low-altitude unmanned aerial vehicle (UAV) hangars, belonging to the field of intelligent control technology. This scheme first collects multi-source heterogeneous data through timestamp alignment and hardware-triggered synchronization mechanisms; then, geometric correction is achieved through joint optimization of the RPC model and GCP, and image stitching is completed by combining SIFT feature matching and graph cut algorithms; subsequently, an improved YOLOv8 model is constructed to achieve multimodal fusion target detection, and semantically guided change detection is completed based on the Transformer algorithm; a high-precision 3D model is generated by fusing multi-source data using an enhanced NeRF algorithm, and vegetation analysis and trend prediction are completed using a dynamic weighting algorithm and an LSTM network; finally, a comprehensive report is generated based on a knowledge graph and interactive visualization output is provided. This invention effectively solves the problems of inefficient multi-source data processing and insufficient core detection accuracy in existing technologies, significantly improving data processing efficiency, detection accuracy, and analytical practicality, and is applicable to multiple scenarios such as ecological monitoring and resource exploration.

The invention relates to the technical field of data clustering integration, and discloses a multi-view financial data clustering integration method and device, and the method comprises the steps: processing financial multi-view data through employing a K-Means clustering method, generating an ensemble pool, and generating a binary division matrix through employing a fuzzy membership function; learning a global structure of a label space through a binary partition matrix to obtain an initial co-correlation matrix, and performing optimization by adopting an LSR model with a Frobenius norm to obtain a label co-correlation matrix; learning a subspace projection matrix, an affinity matrix and a sample adjacency matrix of each view, and optimizing a co-correlation matrix from a feature space to obtain a feature co-correlation matrix; combining the label co-correlation matrix and the feature co-correlation matrix to obtain an optimized co-correlation matrix; projecting the optimized co-correlation matrix into a constraint space for constructing an undirected graph; and dividing the undirected graph by using a graph cutting algorithm to obtain a final clustering integration result of the financial multi-view data.

The invention relates to the technical field of plate recognition, in particular to a plate recognition method based on geometric figure range discrimination. The method mainly comprises the following steps: acquiring geometric data of a plate by using an image acquisition device, a laser scanner and a BIM model, wherein the geometric data comprises boundary contour, surface morphology, spatial position and size information; extracting boundaries and key geometric feature points of the plates through a graph cut algorithm; generating a three-dimensional geometric model by adopting a multi-scale shape analysis algorithm; performing geometric range discrimination by using a Markov random field algorithm, and identifying an abnormal region; and comparing the BIM model design data through an iterative nearest point algorithm to generate an identification report. According to the invention, accurate identification and range discrimination of the building structural slab are realized, and the monitoring and management efficiency of the construction quality is effectively improved.

The present invention discloses a method for liver tumor segmentation in CT sequence images based on deep learning. The method mainly comprises: (1) constructing a U-shaped 2D convolutional network based on dilated spatial pyramid convolution, and using the network to perform two-dimensional slice segmentation of CT sequence images from three viewing directions: sagittal, coronal, and transverse; (2) using a lightweight 3D convolutional network to fuse the segmentation results obtained from different viewing directions to obtain the probability that each pixel in the CT sequence belongs to the target and the three-dimensional segmentation results of the CT sequence liver tumor; (3) constructing a graph cut energy function based on the obtained probabilities and three-dimensional segmentation results to further optimize the segmentation results. By combining 2D and 3D convolutional networks and a graph cut algorithm, the present invention can effectively extract three-dimensional spatial information from CT sequences while using a lightweight network, thereby improving the accuracy of liver tumor segmentation.

The present invention discloses a solid-state hard disk storage optimization method and system, which relates to the field of data storage technology. The method comprises: partitioning storage units into regions based on behavioral layer data and physical layer data of the solid-state hard disk using a topological coloring algorithm and a graph cut algorithm; detecting abnormal behavior of storage units in each region using an isolation forest algorithm to obtain abnormal storage units; obtaining incremental change data of the abnormal storage units; generating an optimization strategy set based on a neural network model using the feature data and incremental change data of the abnormal storage units as input, so that different regions receive different optimization treatments; and optimizing the storage of the solid-state hard disk after obtaining the optimal execution strategy based on the optimization strategy set and under the principle of balancing the optimization strategy of the abnormal storage unit with the overall optimization goal through multi-objective tuning. The present invention can apply different optimization strategies to the internal region, boundary region, and external region of the solid-state hard disk.