19results about How to "Quick division" patented technology

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention provides a global distribution knowledge guided cabbage leaf ball section type region division method and device and a medium, and belongs to the technical field of agricultural informatization. The method comprises the following steps: collecting spectral information of regions with different color types of a cabbage leaf ball profile, and performing feature analysis; calculating hyperspectral characteristic indexes of different types of regions of the cabbage leaf ball section based on the original wave band reflectivity; the method comprises the following steps: acquiring a hyperspectral image of a cabbage leaf ball section by using a hyperspectral imager, and obtaining initial spectral features of different types of regions of the cabbage leaf ball section by using a hyperspectral feature index calculation method; constructing a Chinese cabbage leaf ball section type region division model; inputting the initial spectral features into a Chinese cabbage leaf ball section type region division model to train the model; and collecting a to-be-detected cabbage leaf ball section hyperspectral image, and inputting the image into the model for region division. According to the invention, rapid and accurate division and identification of different types of areas of the cabbage leaf ball profile can be realized, and the division precision and the working efficiency are greatly improved.

The application relates to the technical field of power distribution control, in particular to an active power distribution network fault self-healing system and a method thereof, which comprises a data sensing module, a fault positioning module, an isolation control module, a fault self-healing module and a power supply recovery module, and realizes intelligent processing of the whole process from fault detection to power supply recovery through cooperation of the multiple modules. The system can quickly position faults based on FTU layout structure and operation data, optimizes analysis efficiency in combination with a GPU computing power dynamic distribution mechanism, and realizes accurate isolation of a fault area, self-healing repair and smooth grid recovery by using intelligent strategy generation and digital twin technology. The application effectively improves the power supply reliability, system stability and operation intelligent level of the power distribution network.

The application provides a remote sensing target matching method based on a convolutional neural network of fused local features, compared with a CNN feature based on an overall image, the CNN feature extracted at a region level has higher robustness and flexibility in identifying small similar regions between partial matching images, a matching region pair is accurately matched through a generating step, which makes the CNN feature extracted from the regions more robust for partial repeated image matching, because a region suggestion model in a rough division efficiently matches local features between images to select a small number of top candidate images, which can greatly reduce the target detection range of the partial repeated images, and the application can realize relatively accurate matching because the good robustness of SIFT features and the high resolution capability of CNN features are fully utilized.

This application provides a method and system for processing surface defects on silicon wafers, an electronic device, a computer-readable storage medium, and a product. The processing method includes: acquiring image information, whereby the image information characterizes defect features on the silicon wafer surface, and the defect features characterize the peak and valley differences on the silicon wafer surface within a preset unit area; processing the image information to convert it into a surface feature model, where the surface feature model includes grayscale values ​​corresponding to the defect features, with different defect features corresponding to different grayscale values; coloring the surface feature model to generate a color model, where the color model includes a preset set of colors, with different colors corresponding to different grayscale values; and outputting defect identification results based on the color model. In this way, the nanometer-level peak and valley differences on the silicon wafer surface are transformed from discrete numerical values ​​into a visualized color model, thereby solving the problem of not being able to intuitively present defect features and improving the efficiency and accuracy of defect identification.

The invention relates to a risk comprehensive analysis method based on multi-measurement-item association and measurement point neighbor information, and the method comprises the steps: S1, collecting the basic information of a foundation pit project, carrying out the digital and structured processing of the basic information, and determining the position relation between a foundation pit and a measurement point, and the construction influence range of the foundation pit project; s2, on the basis of a directed graph depth-first search method, the alarm data of different measurement items and measurement points in the S1 form a plurality of correlation analysis networks, wherein the measurement items and the measurement points included in each correlation analysis network have correlation; s3, performing comprehensive analysis of line node data on each correlation analysis network in the S2 by adopting directed graph depth-first search, and judging an early warning value of the correlation analysis network; and S4, performing normalization processing on the early warning value of each correlation analysis network based on distance weighting, and performing comprehensive judgment on the safety state of the foundation pit engineering. The method has the advantage that the rapid and automatic comprehensive judgment of the underground engineering monitoring data risk is realized by comprehensively analyzing the measuring point data in the correlation analysis network.

This invention relates to the field of digital twin technology, and more particularly to a predictive maintenance platform for the entire lifecycle of a heat dissipation system based on digital twins. The platform includes: a data acquisition module; a model building module; a block partitioning module for determining whether to perform direct partitioning based on regional coupling strength or related partitioning based on heat exchange correlation, according to the fault propagation coefficient; a category analysis module for determining block categories based on temperature deviation and determining whether to adjust the block categories of the second-class blocks based on the contaminant deposition impact and fluid pressure anomaly of the second-class blocks, according to the maximum fluctuation cycle decay rate; an update analysis module for determining the update frequency of each updated block based on a comprehensive evaluation value; and an update optimization module for determining whether to perform optimization adjustments based on the block change coefficient. This invention can improve model update efficiency.

The present invention relates to the field of image processing technology, and more specifically, the present invention relates to a method for monitoring the operating status of a hydraulic axle piercer based on image processing, comprising: collecting a thermal sensing image of the hydraulic axle piercer, and graying it to obtain a grayscale image; for any pixel point in the grayscale image, calculating the degree of change of the pixel point. The present invention detects the thermal sensing image of the hydraulic axle piercer collected during the operating status, calculates the monitoring feature quantity of the pixel point according to the characteristics of the hydraulic axle piercer when working, and then determines the pixel point cluster according to the monitoring feature quantity, divides the pixel point cluster, and then obtains the degree of abnormality of the cluster according to the characteristics of the cluster. It is possible to obtain the precise location of the abnormality while determining the abnormality based on the cluster. Through real-time monitoring and timely warning, unplanned downtime is reduced, thereby improving overall production efficiency and avoiding losses to overall production efficiency or safety hazards.

The utility model discloses an automatic feeding device for plastic product processing, including transmission link and baffle, transmission link is through the front and rear both ends of distributing roller and is connected with distributing roller sliding, and the outer surface of distributing roller is opened with a plurality of insertion slot in the circumference array, and one end of baffle is located in the inside of insertion slot and is connected with insertion slot sliding, and distributing roller is located in the inside of distributing bin, and the surface of one end of baffle towards distributing bin is equipped with brush, the utility model discloses the combination design of insertion slot on the surface of distributing roller and baffle, can flexibly increase or decrease the number of baffle and adjust interval angle, and the inside space of distributing bin is divided quickly, realizes accurate intermittent feeding, when baffle rotates with distributing roller, can evenly separate raw materials, and cooperate brush to clean the remaining of distributing bin inner wall, both ensure that the feeding quantity is stable and controllable, and adapt to the feeding demand of different processing scene, and the practicality improves obviously.

The invention discloses a cross-border e-commerce transaction data management system, which relates to the field of data management, and is characterized in that a data acquisition unit acquires data commodities, preprocesses the data commodities and marks the data commodities, and a data processing module is used for acquiring data types and initial information total quantity contained in the data commodities and sending the data types and the initial information total quantity to a server; comprising an AdaBoost optimization model and a counting unit, data commodities are imported into the AdaBoost optimization model, data types included in the data commodities are obtained, the counting unit is used for obtaining the total amount of initial information in each type, and a calculation unit is used for calculating direct benefits obtained by all suppliers according to the provided data commodities. And the quantity updating unit is used for obtaining the total data information quantity after the homogenized data information is removed, obtaining the real total benefit obtained by the supplier through an RBF neural network model, realizing the accurate prediction of the total benefit obtained by the supplier investment, and providing data support for the benefit distribution of the project.

The invention relates to the technical field of simulation design of tires, in particular to a method and system for quickly dividing complex pattern grids of a tire and a computer software product. The method comprises the following steps: carrying out grid division on a plane graph of a tire pattern, and dividing a pattern region into quadrilateral or triangular units; drawing contour curves according to the tire tread contour, wherein the contour curves comprise a tread curve, a pattern groove bottom curve and a pattern bottom curve; generating nodes and unit information of the planar graph, and projecting the planar nodes to the tread curve to construct three-dimensional node coordinates; projecting tread nodes to the interior of the pattern, and generating internal nodes by using intersection points of node normal lines and profile curves; and finally, a three-dimensional unit structure is generated, so that three-dimensional grid division is formed. Compared with a traditional method, the method does not depend on three-dimensional modeling, the grid division efficiency can be remarkably improved, the calculation cost can be reduced, and the method is suitable for accurate simulation analysis of complex tire patterns.

The present invention proposes a VCC coding unit fast division method based on spatiotemporal depth information, the steps are: quadtree division of the CU of the maximum coding unit, multi-type tree division after the quadtree division; when the quadtree division depth of the maximum coding unit is 2, the average depth of spatiotemporal adjacent CUs is less than 2, the quadtree division is terminated; when the quadtree division depth of the maximum coding unit is 3, the average depth of spatiotemporal adjacent CUs is less than 3, the quadtree division is terminated; when the quadtree division depth of the maximum coding unit is between 2 and 4, the CU is divided into multi-type trees; when the multi-type tree division depth of the maximum coding unit is 0, the average depth of spatiotemporal adjacent CUs is less than 0.1, the CU does not need multi-type tree division, otherwise all division modes are traversed; the optimal prediction method is determined according to the rate distortion cost. The present invention can save an average of 35.72% of the encoding time, and the average bit rate increases less under the same encoding quality.

The application discloses a kind of urban green space extraction methods based on prior knowledge and remote sensing data, method includes: obtaining remote sensing data and pre-processing to obtain multispectral image data, select several preset bands of multispectral image data, set the prior knowledge threshold value corresponding to each band one by one, the pixel point of band is compared with corresponding prior knowledge threshold value, according to the comparison result, pixel point is initialized as first value or second value, obtain several preliminary hierarchical data, based on the normalized difference vegetation index, after the integrated data is corrected, the modified integrated data is obtained;According to the distribution result of multiple ground object types obtained by the modified integrated data and the preset classification rule. According to the result of prior knowledge extraction, the quality is high, the robustness is stronger, can quickly divide urban green space and other ground object types, reduce workload, can be widely applied in data processing technical field.

The invention discloses a power supply risk early warning method and device based on a wind and light output safety domain. The method comprises the following steps: carrying out clustering analysis on historical data of a wind and light output scene by adopting a deep embedding clustering method, and determining a clustering result; determining a typical scene set of a wind and light output scene according to a clustering result, and determining a wind and light output extreme value scene according to historical data; performing risk quantitative evaluation on the wind and light output extreme value scene according to a pre-established new energy power supply risk quantitative evaluation model, and determining a risk quantitative result of the wind and light output extreme value scene; when the risk quantification result is a dangerous scene, searching a scene adjacent to the dangerous scene in the typical scene set, and evaluating an early warning result of the wind and light output scene according to the adjacent scene; and when the risk quantification result is a dangerous scene, determining that the wind and light output scene has no operation risk.

The application relates to the field of communication, in particular to an intelligent communication operation management and control system deployment device and a use method thereof. The intelligent communication operation management and control system deployment device comprises a base, further comprises a lifting cylinder movably arranged on the base, the outer periphery of the lifting cylinder is provided with spiral blades, and the lifting cylinder is connected with a power driving mechanism mounted on the base; the power driving mechanism is used for driving the lifting cylinder to maintain a rotating state and execute a lifting action; a limiting mechanism is mounted on the base and is used for limiting wood piles arranged at equal distances; the limiting mechanism is connected with a sensing control mechanism mounted on the base; the sensing control mechanism communicates with the power driving mechanism; finally, when a physical construction of a communication operation management and control system is carried out, through mutual cooperation between various mechanisms and components in the device, effective deployment auxiliary functions are realized, the communication management and control area is quickly divided, and the work efficiency is remarkably improved.

The invention provides an electric meter operation error estimation judgment method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining the operation data of an electric meter; eMD decomposition is carried out on the ammeter operation data to obtain a series of intrinsic mode functions; according to the characteristics of the intrinsic mode function, identifying and removing mode functions related to noise to obtain residual mode functions; reconstructing the residual modal function to obtain de-noised operation data of the electric energy meter; analyzing the de-noised operation data of the electric energy meter to obtain the minimum roughness; according to the minimum roughness, constructing a decision tree of ammeter operation error estimation; and judging and calculating the roughness attribute according to the constructed decision tree so as to realize the operation error estimation of the electric energy meter. According to the method, the decision tree for the operation error estimation of the electric meter is constructed on the basis of the minimum roughness, the data subsets can be quickly and accurately divided, and the corresponding error estimation value is distributed to each subset.

The slope protection structure for the water conservancy project comprises a slope body, a mounting frame is arranged at the top of the slope body, a sliding way corresponding to the mounting frame is arranged on the slope body in an embedded mode, grooves are formed in the two sides of the sliding way, bottom-layer stone is arranged in the grooves, an inserting groove is formed in the top of the mounting frame, and the bottom-layer stone is arranged in the inserting groove. A rhombic frame is arranged in the center of the interior of the inserting groove, locking bolts are arranged at the four corners of the rhombic frame, a rectangular frame is arranged in the rhombic frame, a net plate is fixedly connected to the bottom of the interior of the inserting groove, and upper-layer stone and ecological vegetation are arranged at the top of the net plate. According to the slope protection structure, the rhombic frame, the rectangular frame and the mounting frame are matched with one another, modular design is adopted, and only the rhombic frame and the rectangular frame are adopted, so that one mounting frame area can be quickly divided into a plurality of plates, vegetation and stone can be conveniently filled, the mounting time is shortened, and the mounting efficiency of the slope protection structure is effectively improved.

A kind of user segmentation method based on high-speed ETC charging data, the pre-processing of highway toll data is carried out, the field information required for highway user classification is extracted, and the basic information is stored with the license plate number of highway user as key field, to form the travel basic data of highway user;The high-speed toll records of each highway user are sorted according to time, and the data is cleaned according to the abnormal state of time and space, to obtain the high-speed toll data after data cleaning;According to the cleaned data, the information of three dimensions of highway user time index, space index and personal attribute index is extracted respectively, to form the user classification evaluation index system, and the classification of highway user is completed;According to the time index and space index of highway user travel, the classification is carried out in month cycle, to identify various travels such as commuting travel, operation travel, sporadic travel and business travel.The present application has complete information and high precision, and provides basis for highway planning and construction.

The present invention provides an electric meter operation error estimation and judgment method and system, which relates to the field of data processing technology. The method comprises: obtaining electric meter operation data; performing EMD decomposition on the electric meter operation data to obtain a series of intrinsic mode functions; according to the characteristics of the intrinsic mode functions, identifying and removing the mode functions related to noise to obtain the residual mode functions; reconstructing the residual mode functions to obtain the denoised electric energy meter operation data; analyzing the denoised electric energy meter operation data to obtain the minimum roughness; constructing a decision tree for estimating the electric energy meter operation error according to the minimum roughness; judging and calculating the roughness attribute according to the constructed decision tree to realize the operation error estimation of the electric energy meter. The present invention constructs a decision tree for estimating the electric energy meter operation error based on the minimum roughness, and can quickly and accurately divide the data subsets and assign corresponding error estimation values ​​to each subset.