200 results about "Graph algorithms" patented technology

The invention discloses a synchronous positioning and composition algorithm based on point cloud segmentation matching closed-loop correction, and belongs to the technical field of robot autonomous navigation and computer graphics. According to the algorithm, inter-frame matching is carried out on feature points extracted from three-dimensional point cloud to obtain relative pose transformation ofthe robot, meanwhile, the obtained pose is stored at the rear end in a graph form, and then the point cloud is recorded based on the pose to form a map; the point cloud is fragemented and stored by using a point cloud segmentation and description algorithm, and the point cloud fragments are matched by using a random forest algorithm to form a closed-loop constraint condition; and finally, the historical poses and the map are corrected through a graph optimization algorithm to realize synchronous positioning and composition of the robot. According to the method, while the local positioning precision is ensured, the historical poses and maps are stored and corrected, accumulated errors in an outdoor long-distance environment are effectively reduced, and then synchronous positioning and composition of the robot with good global consistency are achieved.

The invention discloses an intelligent suspicious transaction monitoring method based on a semi-supervised graph neural network. The method comprises the steps of collecting and storing original transaction flow; constructing a fund transaction network based on a transaction relationship at the account level; dividing accounts in the fund transaction network into different transaction communities;performing risk assessment and screening on the transaction community to generate a high-risk-density fund transaction network; deriving individual transaction characteristics of the account; and inputting the individual transaction characteristics of the high-risk-density fund transaction network and the account into a semi-supervised graph neural network, outputting the fund transaction risk probability of the account by the semi-supervised graph neural network, and judging the account of which the fund transaction risk probability is higher than a first threshold value as a high-money laundering risk account. The method has the advantages that the abnormal risk of an individual account can be judged, an advanced semi-supervised classification model is constructed through deep data mining and graph algorithm mining, and a traditional risk control means can be remarkably improved.

The invention discloses a substation anti-misoperation logic intelligent generating method based on topology analysis, and aims at avoiding configuring locking logic for preventing misoperation and self-adapting, reducing building and maintenance workloads, and reducing use complexity, based on ensuring the reliability and the rigor of a Five Prevention system. The substation anti-misoperation logic intelligent generating method based on topology analysis comprises the following steps: analyzing and exporting primary equipment of the substation and connection relation thereof included in an SSD file, mapping the primary equipment of whole substation and connection relations thereof into an undirected graph according to the topological relation, analyzing and summarizing the operation rules of various kinds of primary equipment and abstracting to be a language which can be used by a computer, combining the undirected graph with the language which can be used by the computer to generate equipment locking logic by using nomography from the aspect of mathematics, and thus achieving the purpose of preventing misoperation. According to the substation anti-misoperation logic intelligent generating method based on topology analysis, innovation is provided for the inference engine of the traditional Five Prevention system from the aspect of implementation model, the technological level of intelligent substation for preventing misoperation is improved, and the construction and maintenance efficiencies are greatly improved.

The invention relates to the data mining, in particular to a method for constructing a data association network. The method for constructing the data association network of the invention adds a graphalgorithm and the text similarity mapping, and constructs the data association relation, and comprises the following steps of collecting and pre-treating data; constructing a data association algorithm; constructing a table-between association data model based on the data flows of the above algorithm. Compared with the prior art, the invention improves the scientific quantitative analysis method,adopts the simple text mapping and the graph algorithm to find part of the correlation relationship, and by introducing the text similarity as a quantitative index, improves the accuracy of the matching judgment greatly.

The APT detection correlation analysis method based on the graph algorithm provided by the invention comprises the following steps: acquiring terminal user behavior data and kernel-level data generated by a detection system to obtain original audit log data; storing the original audit log data into a database with a graph algorithm; performing TTP rule matching on the original audit log data according to an ATT & CK knowledge base model to obtain an alarm event; and evaluating the strength of the dependency relationship between the alarm events by utilizing the path correlation degree, constructing an APT attack scene graph, and promoting the original audit log data to an APT attack step. According to the method, the APT activity is detected with high accuracy and low false alarm rate by analyzing the correlation between suspicious information flows of an attacker at an attack stage, so that the ongoing attack activity can be summarized and backtracked effectively in real time, the real-time network response activity is helped to be carried out, and the visualization of an attack scene is realized.

The invention provides a method for analyzing the evolution of associated networks in forums, 1. Reasonable time division and segment extraction; 2. Measurement parameters for measuring community evolution; 3. Longest common subsequence algorithm for solving public nodes in groups; 4. , Discovery of cut points in community network graph based on DFS algorithm. The present invention is applicable to different applications, networks of different scales, and networks of different types. It can be used as long as it can be converted into an undirected graph and follows the same principles. Not only can it be used for the visualization of community network discovery, but also it can analyze the changes of the extracted network over time. Through the analysis of multiple posts, it is found that the evolution process of different posts is also different. For posts with unattractive and boring content, they only have a certain number of replies in the initial stage of posting, and the number of generated communities is relatively small. Important There are also relatively few active nodes. And for those attractive posts, it will last for a long time and the number of communities is large.

The invention discloses an ORB algorithm-based cognitive map construction method. The method comprises the following steps: a robot movement platform detects an external environment, and a visual sensor acquires a visual image and extracts the ORB features of the visual image to construct a local scene cell model; an excitation connection between every local scene cell and a posture cell is established, and a pose cell model controls the stability of an internal pose network through a series of internal activities; path integration of internal mileage information is carried out to estimate thecurrent position of a robot in real time; and a mapping algorithm uses the spatial information characterization of the pose cell model to construct the cognitive map of the environment in real time.The ORB algorithm-based cognitive map construction method can solve a defect that original cognitive map models cannot work indoors, and has high robustness, biological inspiration and practicality.

The invention relates to a three-dimensional laser mapping method and a system, and the method comprises the steps: obtaining data through a sensor placed on a robot, and filtering out unqualified data; calculating the pose of the robot by using a coarse-to-fine matching algorithm; and constructing a new image according to the pose of the robot, and then modifying a map according to a loop closingdetection result. According to the method, the scene map with high consistency can be generated, the algorithm is simple, and the accuracy is high.

The invention provides a semantic map construction method based on a depth camera and a sweeping robot, and is applied to the technical field of robots. The method comprises the following steps: obtaining images of the sweeping robot at multiple positions in the located environment space through the depth camera, wherein the image comprises an RGB image and a depth image; constructing a three-dimensional map of the environment space through a simultaneous localization and mapping (SLAM) algorithm based on each depth map; carrying out semantic recognition on each RGB image through a pre-trainedneural network recognition model; obtaining semantic information of each obstacle in the environment space; fusing the three-dimensional map and the obtained semantic information of each obstacle toobtain a three-dimensional semantic map, that is, the three-dimensional semantic map of the environment space constructed by the application, compared with a two-dimensional map of the environment space constructed according to a laser radar, the richness of information contained in the constructed map of the environment space and the accuracy of the constructed map are improved, and the effectiveworking space range of the sweeping robot is expanded.

A system and method of determining a common neighborhood of users sharing a common activity from a plurality of users is provided. The system and method may be used to predict, for a user in the common neighborhood of users, a potential activity from the activities of at least one other user in the common neighborhood of users.

The invention provides a mobile robot mine scene reconstruction method and system based on SLAM. The method comprises the following steps: acquiring laser point cloud data and visual point cloud data measured by a mobile robot and calibrated synchronously; fusing the obtained laser point cloud data and visual point cloud data; carrying out point cloud motion distortion removal processing and point cloud filtering processing on the fused point cloud data; adding IMU pre-integration data, point cloud key frame data and GNSS data into a constraint sub-graph by combining the processed point cloud data and adopting a multi-constraint factor graph algorithm based on graph optimization, and performing loopback detection to obtain a reconstructed three-dimensional map. According to the invention, three-dimensional reconstruction in the mine scene can be effectively realized, the point cloud map with colors is finally obtained, and the precision of mine scene reconstruction is improved.

The invention discloses an aluminum profile defect detection method. The method comprises the following steps: detecting the surface of an aluminum profile by adopting an aluminum profile surface defect detection model; if flaws are detected, performing category judgment and regression positioning on the flaws, wherein the aluminum profile surface defect detection model takes Resnet-101 as a main network; the aluminum profile surface flaw detection model adopts a feature pyramid network and a deformation convolution algorithm and is used for performing targeted detection on irregular flaws on the surface of an aluminum profile; according to the aluminum profile surface defect detection model, an ROI Align feature map extraction algorithm and an improved candidate box generation network loss function are adopted to position tiny defects. A feature pyramid network model is improved through a deformation convolution algorithm, so that the detection model has higher matching capability for irregular flaw features; and by improving a candidate box generation network loss function, the regression positioning of the detection model on the tiny flaws is more accurate.