218 results about "Model graph" patented technology

An identity authentication method based on a Web user behavior model comprises performing behavior modeling by collecting Web user web page browsing records on a client-side and depicting behavior characteristics of a user within a certain time period; performing web page clustering with web page contents and time series of the Web user accessing Web pages as the standard, and enabling formed classes to serve as behavior model nodes; enabling Web pages skip links among the nodes (the Web page classes) to be directed edges, and enabling linking number to be edge weight; building a directed graph with an edge weight value to serve as a behavior model graph of the Web user, and converting to a markov model. The identity authentication method can be combined with an existing Web user identity authentication mode, user identity is authenticated from two aspects, the authentication accuracy rate is improved, and property safety of the Web user is guaranteed.

The invention discloses a two-dimension animation automatic generating method based on a skeleton motion database. The two-dimension animation automatic generating method comprises the steps of establishing a skeleton and a skeleton motion, storing the skeleton and the skeleton motion into the skeleton motion database, drawing an animation modeling, drawing a modeling graph according to an appearance requirement of the modeling, binding the skeleton motion with the modeling graph, calling the skeleton motion from the skeleton motion database, binding the skeleton motion with the modeling graph, mapping the skeleton motion to the modeling graph, interpolating an animation, interpolating a skeleton motion frame according to an animation requirement, and obtaining an animation sequence. According to the two-dimension animation automatic generating method based on the skeleton motion database, efficiency and quality of two-dimension animation production are improved, and production cost is effectively saved.

A technique for image processing that includes receiving a model image, an input image, and registering the input image with the model image. A modified input image is determined that includes a first component that is substantially free of error components with respect to the model image and a second component that is substantially free of non-error aspects with respect to the model image. The technique determines an improved alignment of the modified input image with the model image where the improved alignment and the first and second components are determined jointly.

The invention discloses a convolutional neural network and user habitual behavior analysis combination-based AR system gesture identification method. The method comprises the following steps of 1, collecting user habitual gesture images: randomly providing a group of gestures by a user, taking the group of the gestures as standard gestures, collecting images of the group of the gestures, and marking the images as a standard group; according to a gesture model graph of the standard group, establishing corresponding actual tag types; setting different tag types for triggering corresponding specified functions of an AR system; 2, detecting gesture region images: performing gesture region image detection on gesture images of the standard group, a training sample group and a test sample group to realize segmentation of skin color and non skin color regions in the images; and 3, realizing gesture feature identification by a convolutional neural network: designing a convolutional neural network preliminary structure model, training, testing and adjusting the convolutional neural network model by using sample data, and directly inputting a binary image to the convolutional neural network.The gesture identification accuracy is relatively high and the additional device cost is relatively low.

The invention discloses a multicenter data task processing method, a multicenter data task processing device and a multicenter data task processing system. The processing method comprises the steps: A, collecting data task information; B, generating a data task model graph according to the data task information, cutting the data task model graph, namely grouping all nodes in the data task model graph, wherein each node of the data task model graph is one data task; and C, allocating data tasks of different groups into corresponding data centers. By means of the multicenter data task processing method, the device and the system, through the establishment of a model reflecting the correlations among data tasks, multicenter task allocation is performed based on the model, and thus the task allocation is visual, accurate and balanced. Transfer and redundancy of large amount of data are avoided, data volume transmitted among each center is small, therefore pressure of network load is reduced, overall data processing efficiency is improved, and loads in all the data centers are basically the same.

The invention relates to a method using the 3D printing technology to prepare dropping pills. The method using the 3D printing technology to prepare the dropping pills comprises the steps of (1) using polyethylene glycol and the like as base materials, carrying out heating melting, (2) adding prescription drugs at the specified temperature, carrying out mixing, even stir and freeze drying to obtain solid powder, carrying out sieving on the solid powder, and (3) placing the solid powder into a powder box of a 3D printer, and preparing the dropping pills through electronic drafting, remote data transmission and laser scanning printing. According to the method using the 3D printing technology to prepare the dropping pills, due to the facts that through a series of technologies such as the electronic design technology, the remote data transmission technology, the laser scanning technology and the raw material and auxiliary material melting technology, the specific drug powder materials are melted, and electronic model graphs are changed into the real dropping pills according to designed indications, the advantages of the dropping method of preparing the dropping pills are kept, and the defects that due to the influence of the factors such as physical properties of liquid medicine, droppers and cooling liquid, pill weights are different, the roundness of pills is difficult to control, the percent of pass is low, and specially-shaped dropping pills cannot be easily prepared when the dropping method is used for preparation are overcome.

The invention discloses an indoor scene scanning reconstruction method and apparatus. The method comprises: obtaining scanning image information of an indoor space, captured by a robot, and according to the scanning image information, reconstructing a three-dimensional scene model graph of the indoor space; segmenting the three-dimensional scene model graph into a plurality of regions of interest; for each region of interest, performing the following operation: segmenting the region of interest into a plurality of small regions; controlling the robot to apply a thrust to an object corresponding to each small region to obtain images of the plurality of the small regions after the thrust is applied; comparing the small region images after the thrust is applied with the small region images before the thrust is applied, segmenting the objects in the images according to comparison results, and controlling the robot to scan non-completely scanned parts in the objects separated after the thrust is applied to obtain complete three-dimensional data of the objects; and according to the segmented objects and the complete three-dimensional data of the objects, reconstructing an indoor scene. According to the technical scheme, the efficiency and precision of reconstructing the indoor scene are improved.

Deployment pattern matching is implemented by accessing a target computing environment model that captures environment modeling parameters relating to resources and resource-resource relationships of a corresponding computing environment and expressing the target computing environment model as a model graph defined by target resource elements and resource-to-resource relationship links. Deployment pattern matching is further implemented by accessing a realization pattern that captures deployment parameters relating to resources and resource-resource relationships of a deployment of interest and expressing the realization pattern as a pattern graph defined by conceptual resource elements and constraints arranged by resource-to-resource relationship links and constraint links. The realization pattern is then evaluated against the target computing environment model by executing at least one pattern matching algorithm that attempts to match the pattern graph to the model graph and information corresponding to results of the evaluation are conveyed.

The invention relates to the field of information processing, in particular to a visual ontology construction system and method for a knowledge graph, and the method comprises the steps: drawing an ontology model graph in a mode of adding nodes and edge graphs through the system; and the system generates ontology model data according to a preset data conversion format and stores the ontology modeldata. According to the method, the ontology model is created by adopting a visual drawing mode, so that the logic structure of the ontology model is expressed more intuitively. A user adds entities,event nodes and relation connecting lines in a graph form to create an ontology model graph, complex language description or configuration form filling is not needed, the ontology model drawing process is simple and convenient, and the logic structure of the ontology model is clear in the creating process. The threshold for creating the ontology model is lowered. And a powerful tool and method areprovided for application and popularization of the knowledge graph.

The invention relates to an intelligent judgment method for identifying high-risk and important user power supply risks. Compared with the prior art, the method overcomes the defect that the high-risk and important user power supply risks cannot be intelligently judged. The method includes the following steps that a future power grid operation model is predicted, current power grid load flow calculation model data are acquired, a repair plan, load prediction, a plan to be put into production and a power generation plan are added, and then data of the future power grid operation model are formed; load flow calculation is conducted on the future power grid operation model, load flow calculation is conducted based on the data of the future power grid operation model, and then a load flow calculation result is acquired; the power model is made to be graphical, a power grid topological relationship in the power grid model is extracted based on a graph-theoretical algorithm, and a power grid graphical model is established; the high-risk and important user power supply risks are identified, and the high-risk and important user power supply risks are judged based on the established power grid connection graph model with the power grid topological relationship. By means of the method, the high-risk and important user power supply risks of a power grid are intelligently identified.

A method of providing routes through heterogeneous subsystems in an optical network is disclosed, which includes generating, using a processing device, a reachability matrix based on subnetwork information; and generating, using the processing device, a topology associated with the optical network using the reachability matrix. The method also includes determining, using the processing device, a shortest path through the optical network using the reachability matrix and a cost model graph; and displaying, using a graphical user interface, subsystems associated with the shortest path, regeneration locations associated with the shortest path, wavelengths associated with the shortest path, the topology, and the shortest path. Corresponding apparatus and computer-readable storage media are also disclosed.

The invention discloses a sensor network software modeling platform development method based on a unified modeling language and a genetic algorithm. By combining with the thought of an intelligent agent and the genetic algorithm, a model suitable for sensor network software development is built with the unified modeling language; the built model is optimized by the genetic algorithm; and a modeling platform based on the model is built. A model formed the unified modeling language based on the intelligent agent is built, and by defining a meta-model of a domain model formed by the unified modeling language based on the intelligent agent, the domain model comprises a plurality of diagrammatic figures, namely, an activity diagram of a group, an intelligent agent diagram owned by the group, a group diagram to which the intelligent agent belongs, a role diagram owned by the group and a relational graph between groups. The model is optimized by the genetic algorithm, so that attribute redundancy in the group in the model is lowered, and the model with the perfect performance is obtained. According to the sensor network software modeling platform development method, a purpose that a sensor network software model is visually built is realized, and the sensor network software development difficulty is lowered.

The model training method, the image similarity measurement method, the terminal and the storage medium are used for training and utilizing an image similarity measurement model, and the image similarity measurement model comprises at least one pair of image processing neural networks with the same structure and a similarity calculation module; in the model training method, a training image pair is input into a pair of image processing neural networks to obtain a pair of training feature vectors, a similarity comparison module compares the pair of feature vectors to obtain an estimated similarity result, and the loss between the estimated similarity result and a real similarity result is used for adjusting parameters of a model; if one or more test results obtained by inputting the test image data into the model do not reach the preset condition, repeating the previous steps until the training is completed; according to the method, a novel structure model different from a traditional image similarity algorithm is realized, feature representation is carried out on the training sample through the pair of image processing neural networks, joint learning of similarity measurement is carried out, and the measurement accuracy and the calculation efficiency are improved.

A method, medium and apparatus determining whether models collide with each other. In the model collision detecting method, medium and apparatus, it is possible to quickly examine whether models collide, and to recognize the collision generated at an arbitrary time, which is not a predetermined collision check time of the models, without missing the recognition of the collision generated at the arbitrary time, by determining whether paths reflecting movement of the model primitives between the current time and a previous time overlap when the model primitives do not collide at the current time, determining whether the model primitives collide with each other at an arbitrary time between the current time and the previous time when the path primitives overlap each other, and recognizing that the models collide with each other at the arbitrary time when the model primitives collide with each other at the arbitrary time.

The invention discloses a product quality information transmission method based on augmented reality. A three-dimensional CAD model represented by MBD (Model Based Definition) is used as an information input source, and the product quality information based on manufacturing features is extracted; a CAD model image set is generated, and during the generating process, the association relationship between an image and the manufacturing features is built; and finally, a manufacturing field image and a template image are matched, an attitude parameter of a virtual camera corresponding to a templateimage with the highest similarity is used as an initial attitude of an external camera, and superposition of the product quality information in the real manufacturing field scene is realized. The manufacturing feature is used as a basic carrier for the product quality information, and the requirement of only detecting a specific feature by an operator is met; through building the association relationship between an image and the manufacturing feature, the requirement of the operator for visual features of a part from different perspectives can be met; and by using a tracing registration technology and in combination of an external database, the requirement of transmitting the product quality information to the manufacturing field can be realized, and the detection efficiency and the quality are improved.

The invention discloses an online analysis method for comprehensive equivalent damping characteristics of a regional generator group. The online analysis method includes calculating whole-network steady-state trend at first and dividing the regional generator group on the basis of an electrical distance method; establishing a regional equivalent model which has regional generator group equivalent damping characters and is showed as a classic model by the aid of online information provided by whole networks of a wide-area measurement system and an energy management system so as to replace the regional generator group; carrying out model analysis for the whole networks after the regional generator group is replaced by the regional equivalent model, and obtaining influence of the regional generator group represented by the regional equivalent model to small interference stability of the whole networks form two aspects of a main participation model and a correlation factor and model graph of the main participation model, namely obtaining the comprehensive equivalent damping characteristics of the regional generator group. Relative oscillation conditions between a relevant unit with a low-frequency oscillation mode and a generator are provided, connection between the regional generator group and an actual power grid can be realized easily, calculation is simple, anti-interference ability is good, and practicality is high.

The invention belongs to the technical field of artificial intelligence, and discloses a house type graph intelligent identification method, which comprises the steps of obtaining a two-dimensional house type graph to be identified; identifying the two-dimensional house type image based on a preset wall body identification model, and outputting the wall body information in the two-dimensional house type image; and generating a three-dimensional house type model graph corresponding to the two-dimensional house type graph based on the wall body information, and outputting the three-dimensional house type model graph. The invention also discloses a house type graph intelligent identification device, an apparatus and a computer readable storage medium. According to the invention, the accuracyof wall body identification in the house type graph is improved.

The invention discloses a complex electromechanical system anomaly detection method based on multi-source data, and the method comprises the steps: firstly carrying out the quantitative analysis of the correlation between multi-dimensional variables through employing detrended cross-correlation analysis (DCCA), and building a complex system multivariable coupling relation network taking the inter-variable coupling relation as an edge and a monitoring variable as a node; on the basis, establishing a variational diagram self-encoding model based on unsupervised learning; carrying out feature extraction on the multivariable coupling relation network of the system; training the model by using normal data, learning distribution of input data by using a graph convolution network as an encoder, performing sampling to obtain potential representation of the input data so as to realize reconstruction of a coupling network, obtaining a reconstruction probability threshold value by training a sample, and using a reconstruction probability as a system multi-dimensional polymorphic monitoring data anomaly detection evaluation index. According to the method, the coupling relation between the multi-source data is considered, the variational diagram auto-encoder model is introduced, the experience dependence is reduced, the problem of few abnormal samples is solved, and the accuracy and reliability of system anomaly detection are improved.

The invention relates to a unified storage method for key value model, document model and graph model data, which comprises the following steps of: establishing mapping from a key value model to a document model and mapping from a graph model to a document model by utilizing the description capability of the document model to the data. Unified storage of the key value model and the document modelcomprises the steps that Class is designed, and records of the document model are stored in a common table after the Class is instantiated; the unified storage method of the graph model and the document model comprises the steps that a Vector class and an Edge class are designed, records of the document model are stored in a vertex table obtained after instantiation of the Vector class, and records of the document model are stored in a side table obtained after instantiation of the Edge class. Unified storage of a key value model, a graph model and a document model includes: storing key valuemodels, graph models and document models in a unified manner; wherein the Vector subclass and the Edge subclass are inheritance relationships with the parent Class; wherein the Class supports a key value model and a document model, the Vector class and the Edge class support vertexes and edges, and the vertexes and the edges instantiated by the Vector class and the Edge class can be used for storing data of the key value model, the document model and the graph model at the same time.

The invention relates to the technical field of teaching systems, and, more specifically, relates to an AR (augmented reality) CAD teaching method and system. The teaching method mainly comprises thesteps: building a three-dimensional model through 3D software, importing a stereoscopic model, a model graph and three views into three-dimensional animation software, carrying out the collection of two-dimensional images in a classroom through a camera, carrying out the recognition through the three-dimensional animation software, exporting a three-dimensional stereoscopic model file, and displaying the three-dimensional stereoscopic model file, wherein the display mode can be changed so as to observe the three-dimensional stereoscopic model at multiple angles. The teaching system comprises aplatform main body, and a computer and a display mounted on the platform main body. The platform main body is provided with a camera, a gesture sensor and a projector, wherein the camera, the gesturesensor and the projector are electrically connected with the computer. The method and system employ the AR mode, also can achieve the real-time interaction through gestures, achieve the conversion between the two-dimensional images of the classroom and the three-dimensional mode, bring convenience to students for quickly building a three-dimensional model stereo architecture, and help students create 3D thinking.

The invention provides a prefabricated part butt joint interface quality detection and treatment method and relates to the field of assembly type construction. The method comprises the following steps: performing three-dimensional scanning on the installed first prefabricated part and generating an actual three-dimensional graph of the first prefabricated part by a background processing system; and performing the virtual assembly of the actual three-dimensional graph of the first prefabricated part and the design model graph of a second prefabricated part to be installed on the background processing system, and comparing and analyzing whether deviation exists between the perpendicularity of the first prefabricated part and the second prefabricated part and the flatness of the butt joint interface, and when no deviation exists, directly processing the second prefabricated part to be installed and entering the next step; and when deviation exists, calculating an adjusting parameter for machining the butt joint interface of the second prefabricated part to be installed, and machining and manufacturing the second prefabricated part to be installed, and completing the maintenance; conveying the second prefabricated part to a construction site, and completing the assembling construction of the first prefabricated part and the second prefabricated part according to the requirement ofthe assembled design diagram.

The invention discloses a building method for a common mode EMI model and an equivalent circuit of a complete train of a high-speed motor train unit. The method comprises the steps of establishing anEMI model graph of the complete train; establishing an EMI model graph of a traction converter; establishing an equivalent circuit graph of a common mode EMI loop caused by a potential point A of a rectifier bridge of the high-speed motor train unit, and then according to the equivalent circuit, calculating out a conducted EMI current; and establishing the equivalent circuit after passing the converter, and then according to the equivalent circuit, calculating out the conducted EMI current. The model and the equivalent circuit in the method describe common mode EMI characteristics of the complete train of the high-speed motor train unit, wherein the common mode EMI characteristics include common mode ground capacitance distribution, a cable high-frequency model, and high-frequency parasitic characteristics of transformer primary and secondary sides. The model and the equivalent circuit predict the common mode EMI characteristics of the complete train and serve as technical means of research on common mode EMI of the high-speed motor train unit; the equivalent circuit obtains a mathematic expression of a common mode EMI current; and the expression predicts the magnitude of the common mode EMI current of the complete train.

An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. A structural representation that captures the essence of all of the relationships in a unified manner has concise information about the educational institution, and visualization is a way to bring out all this information in an explicit manner so that the various of the users of the educational institution system understand effectively their system. A system and method for visualization based on the structural representation of a university along a variety of dimensions is discussed.

The invention discloses a cross-modal retrieval method for a sketch retrieval three-dimensional model based on spatiotemporal feature information, which comprises the following steps: firstly, selecting data, then constructing a sketch three-dimensional model image sequence and a spatiotemporal feature information extraction network, extracting spatiotemporal feature information of a sketch and athree-dimensional model by using the spatiotemporal feature information extraction network, combining the spatial and temporal feature information of the sketch with the spatial and temporal feature information of the three-dimensional model by using deep metric learning; and finally, calculating the similarity according to the Euclidean distance between the spatial and temporal feature information of the sketch and the spatial and temporal feature information of the three-dimensional model in the spatial and temporal feature information combination. The method is outstanding in retrieval performance, capable of effectively completing cross-modal retrieval of the sketch retrieval three-dimensional model, better in accuracy, easy to operate and high in practicability.

The invention relates to a closed-loop control method of a power scheduling operation order and a ground wire operation. The closed-loop control method comprises the following steps of: (1) using an electric fence topological graphic module to perform modeling according to a primary wiring graph of an electric fence and display a modeling graph; (2) using a scheduling back brake operation process module to generate and execute a scheduling operation order according to the modeling graph; and (3) analyzing the states of the electric fence before and after the operation of the electric fence, prompting a ground wire hanging operation on the basis of a safety analysis result and recording the current state of a ground wire when a ground wire management module performs a simulated operation on the electric fence topological graphic module, and prompting a dismounting operation of the ground wire when the operation order is replied. According to the closed-loop control method disclosed by the invention, the synchronous linkage management of order placement and ground wire management is realized, scheduling staff can be helped to judge the state where the whole-network ground wire is located in an intelligent manner, the hanging and dismounting situations of the whole-network ground wire can be controlled as a whole, the occurrence of mis-hanging, mis-dismounting and other accidents of the ground wire can be prevented, and the difficult problem that the state of the ground wire is difficult to identify by the scheduling staff during the processes of order placement and order replay of the operation order is solved.