442 results about "Object model" patented technology

The invention relates to an intelligent equipment access system and access method based on an AI large model, and relates to the field of artificial intelligence and Internet of Things. According to the technical scheme, a dynamically adaptive universal object model access framework is constructed, functional attributes, control instructions and data formats of intelligent devices of different brands and protocols are mapped into a standardized logic model, meanwhile, a multi-mode AI large model engine is combined, high-precision voice recognition, voiceprint feature detection and multi-level semantic intention analysis capabilities are integrated, and the intelligent devices of different brands and protocols can be obtained. And an intelligent interaction closed loop of end-cloud collaboration is formed. Semantic analysis adopts a mixed strategy, a natural language instruction is converted through function mapping, a local knowledge base is utilized to match user scene requirements, and external data is queried by means of cloud networking to optimize instruction logic. According to the scheme, the multi-device access and operation and maintenance cost is remarkably reduced, the semantic understanding accuracy and scene adaptability are improved, and the method is widely applied to the fields of smart home, smart medical treatment and industrial Internet of Things.

The invention discloses a mixed collision detection method in a virtual operation simulation system. The method comprises the following steps: 1) constructing a surgical instrument and soft tissue model; 2) dividing a virtual space into grids with the same size, indexing an object model, accelerating the construction and updating of a spatial information hash table by utilizing GPU multithreading, mapping the grids occupied by all primitive bounding boxes into the hash table, and determining objects in the same space according to the spatial hash table; 3) before object intersection solving, space reduction is carried out, and hash table data is optimized; 4) constructing a hierarchical bounding box for an object which may collide, and performing intersection detection; if it is detected that a certain node or child nodes of the node intersect, the next step is executed, and otherwise, detection is pushed out; and 5) accurately judging whether the basic triangular units contained in the nodes in the step 4) are intersected or not, and completing collision detection. According to the method, the problem of missed detection and wrong detection in the existing discrete collision detection algorithm is effectively solved, and the detection algorithm has excellent accuracy and calculation efficiency.

Implementations relate to methods, systems, and computer-readable media for performing similarity analysis of three-dimensional (3D) objects. In some implementations, the method includes obtaining a 3D model of the object that includes a mesh and a texture, encoding the mesh into a mesh feature vector using a first neural network, encoding the texture into a texture feature vector using a second neural network, computing a mesh distance between the mesh feature vector and a reference mesh feature vector of a reference object, computing a texture distance between the texture feature vector and a reference texture feature vector of the reference object, determining, based on the mesh distance and the texture distance, whether the object matches the reference object, classifying the object as a dissimilar object if the object does not match the reference object, and classifying the object as a similar object if the object matches the reference object.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.

Embodiments of the present disclosure provide a method that may include defining an object model containing a structural representation of events and artefacts through which contracts are created, changed, and brought to an end. The method may include accessing a machine learning classifier comprising a plurality of rule sets. The method may include applying the plurality of rule sets to one or more words of each corresponding contract document. The method may include linking identified one or more core attributes and one or more words of each corresponding contract document to an applicable object of the object model, determining prevailing terms of each corresponding contract document, and evaluating contract data variables and assigning a contract data risk value to one or more of contract data values. The method may include alerting a user when a contract risk exceeds a threshold value.

The invention relates to the technical field of industrial Internet of Things, in particular to a secondary development system and method based on an industrial Internet of Things gateway. Comprising a client and an industrial Internet of Things gateway. The client issues equipment information and variable information of the configured script equipment to the industrial Internet of Things gateway; the industrial Internet of Things gateway comprises a secondary development module; an initialization module; an object model module; a variable module; and a Python SDK module. According to the invention, system modular decoupling is realized through a publishing and subscribing mechanism of a built-in MQTT server, dynamic extension of multi-industrial protocol conversion and edge computing capability is supported, and a low-code Python SDK development environment is provided. Compared with the prior art, according to the scheme, the edge secondary development period is greatly shortened, the development efficiency is improved, and the core pain points of poor protocol expansibility, openness limitation, high secondary development starting difficulty, large workload and the like in the field of the industrial Internet of Things are effectively solved.

The invention discloses a heterogeneous object model conversion method and device based on a large language model, and the method comprises the steps: (1) an equipment model analyzer is responsible for loading and analyzing an object model file and a profile file of equipment, and obtaining the object model type of the equipment and the core function data of the equipment; (2) the object model mapper plays a crucial role in the object model conversion process, and a model mapping dictionary between different object models is obtained by learning the relation between different equipment object models and fine adjustment of related model mapping rules; (3) the object model converter performs object model macroscopic framework conversion based on the model mapping dictionary, converts source equipment object model core function data in the equipment model analyzer into target object model core function data, and fills an object model and a profile file of target equipment with the target object model core function data; in order to ensure the correctness of conversion, conversion detection is carried out on a converted target object model file and a profile file based on a large model. According to the method, the difference between object models can be overcome, and the universality of conversion is provided.

A method, a system, and a computer program product for generation of templates for electronic documents. A first template is generated based on a plurality of electronic documents. The first template defines a first structural arrangement of one or more portions extracted from the electronic documents and includes one or more portions. A machine learning model determines the first structural arrangement. An update to at least one portion is received. A second template is generated based on the first template and the received update. The second template defines a second structural arrangement of the portions as determined based on the first structural arrangement and the received update. An object model representative of at least one of: the first template, the second template, and a difference between the first and second templates is stored.

The invention discloses an object model access parameter dynamic optimization method and system based on reinforcement learning. According to the method, a multi-agent reinforcement learning model is constructed, a device-level state and a network-level state of the Internet of Things terminal device are used as environment state input, access parameters of the terminal device are modeled as an action decision of a reinforcement learning decision model, and the reinforcement learning decision model outputs optimized access parameters according to the current environment state. And continuously updating the strategy network through a reward function. And finally, the control platform deploys the trained strategy model, automatically selects the optimal access parameter configuration when the terminal equipment accesses, and adjusts the access strategy in real time according to the state change of the equipment and the network. Compared with an existing static strategy, the method has the advantages that efficient, low-time-delay and low-power-consumption access of the power internet-of-things terminal equipment can be achieved, and the system resource utilization rate and the communication reliability are remarkably improved.

The invention discloses an Internet of Things system TAP rule generation method and device based on a large language model, and the method comprises the steps: (1) extracting information from an object model file of equipment, and organizing the information into a proper format; (2) screening out related contents from the equipment information according to user requirements; (3) generating a TAP in a proper format based on the LLM according to the requirements of the user and related equipment information; and (4) converting the TAP format generated by the LLM into a TAP of an actual platform through a Translator component to realize deployment. The core of the method is to define a universal equipment description format and a TAP format, convert user requirements into a universal TAP by means of LLM, and translate the universal TAP into a format required by an actual platform to realize deployment. The method has the advantages that a user can create the TAP in a natural language, manpower and time for realizing simple and repeated work are saved, the method is not limited to a certain specific Internet of Things platform, and the system can be applied to Internet of Things systems needing to create a large number of TAPs in different scenes through proper adjustment and has wide applicability.

Method for determining a grasping hand model suitable for grasping an object by receiving an image including at least one object; obtaining an object model estimating a pose and shape of the object from the image of the object; selecting a grasp class from a set of grasp classes by means of a neural network, with a cross entropy loss, thus, obtaining a set of parameters defining a coarse grasping hand model; refining the coarse grasping hand model, by minimizing loss functions referring to the parameters of the hand model for obtaining an operable grasping hand model while minimizing the distance between the finger of the hand model and the surface of the object and preventing interpenetration; and obtaining a mesh of the hand represented by the enhanced set of parameters.

The present application belongs to the technical field of 3D printing. Disclosed are a method and apparatus for planning a non-planar printing path, and a storage medium and a device. The method comprises: acquiring a single-layer object model to be printed; generating a first printing path for the single-layer object model; generating a compensation path on the basis of a sparse part in the first printing path; using a dynamic model to perform uniformization processing on the compensation path, so as to obtain a second printing path, wherein the second printing path tends to have a uniform distribution of distances during a simulated extrusion process; and determining a printing direction of each node on the second printing path, so as to obtain a final non-planar printing path. In the present application, a non-planar first printing path is formed on the surface of a single-layer object model, a compensation path is formed at a sparse part in the first printing path, and uniformization processing is performed on the compensation path on the basis of dynamic simulation, such that uniform printing during a non-planar printing process when the direction of a path and the direction of a curved surface are consistent is ensured, thereby optimizing the printing effect.

The invention discloses a household appliance and a control method thereof. The method comprises the following steps: acquiring multi-modal data; analyzing the multi-modal data by using a large language model to obtain an intention result corresponding to the multi-modal data; querying a condition triggering rule and object model information according to the intention result to obtain a target rule; and determining a corresponding intelligent agent based on the intention result and the target rule by using an intelligent agent manager, and allocating a task to the intelligent agent, so that the intelligent agent controls the corresponding household appliance according to the task. By means of the mode, household appliance control with higher flexibility and higher intelligence is achieved.

A system is described for controlling access to resources using an object model. Users can specify use cases for accessing resources. The user may be granted access if the user satisfies qualifications required for accessing the resource, selected a use case permissible for accessing the resource, and satisfies qualifications required for the use case. Use cases, qualifications, resources, and / or links between them can be implemented using an object model. The system can be used in addition to authentication and authorization.

The invention provides a flight environment simulation control system multi-mode simulation method, and belongs to the technical field of aero-engines, and the method comprises the steps: selecting a full-digital simulation experiment or a semi-physical simulation experiment, a running object model and a control model, and the control model comprises a feedforward controller, an active disturbance rejection controller and an extended state observer; the feedforward controller calculates a feedforward control quantity according to an engine speed change rate output by the object model, the extended state observer estimates the total disturbance of the system to obtain an estimated value, the estimated value is fed back to the active anti-disturbance controller, and an initial control quantity is obtained through calculation; superposing the initial control quantity with the feedforward control quantity and the estimated value to obtain a final control quantity, and inputting the final control quantity into a hydraulic regulating valve flow model in an object model; the control parameters of the control model are set through a full-digital simulation experiment, and the set control parameters are optimized through a semi-physical simulation experiment, so that the simulation confidence and efficiency are improved, and the cost is reduced.

An example device for retrieving media data includes a memory for storing media data; and a processing system implemented in circuitry and configured to: retrieve data representing a three-dimensional (3D) object model and one or more levels of detail (LODs) for the 3D object model; send a request to the server device to access data for the 3D object model at one of the LODs, the data for the 3D object model at the one of the LODs including a size, complexity, and components of the 3D object model for the one of the LODs; and receive the data for the 3D object model at the one of the LODs in response to the request, the data for the 3D object model at the one of the LODs having the size, the complexity, and the components of the 3D object model for the one of the LODs.

The invention discloses a template turnover calculation application software design method based on an intelligent algorithm. The method comprises the steps that a data source is transferred from an original turnover area to a target area and from an excess material library to the target area; the fixing rule comprises template use frequency constraint, turnover constraint of different types of templates and leftover material definition; the variable rule comprises a turnover template carrying distance constraint and a turnover template cutting proportion constraint; turnover calculation, turnover object model pairing and paired model inner template calculation; and the turnover result gradually solidifies the rule according to different scenes. The method has the advantages that through the integrated calculation module, automatic calculation of the template turnover efficiency is achieved, manual intervention is not needed, and the calculation efficiency and accuracy are greatly improved; an advanced algorithm and a data processing technology are adopted, the accuracy of a calculation result is ensured, template turnover calculation requirements in different scenes can be met, and the resource utilization rate is improved and the operation cost is reduced by accurately calculating the template turnover efficiency.

The invention discloses a spaceflight equipment test system based on scripts and plug-ins, and belongs to the technical field of LVC test verification. The system comprises a master control module, a plurality of slave control modules and an operation support library. Each slave control module is a plug-in sub-module designed according to a unified coding format, the plug-in sub-module defines implementation logic of an object model, and after the plug-in sub-module is loaded, the object model in the operation support library is called to execute an internal function. And the main control module reads external configuration parameters, assigns values to internal variables of the plug-in sub-modules, loads all the plug-in sub-modules, and controls the corresponding plug-in sub-modules to execute in sequence according to a set execution sequence. The operation support library provides an object model library for each plug-in sub-module, and objects defined in an object model comprise messages, state distribution objects and remote methods. Through the application of the invention, communication between subsystem models is realized, and subsystems of aerospace equipment are associated with each other to form a digital equipment overall model.

The invention provides a dynamic human-machine interface generation system for a power conversion device, and the system comprises a configuration file which is used for defining interface elements, attributes and layout rules of an HMI in a structured language; the configuration analysis module is used for reading and analyzing the configuration file and converting the configuration file into a structured object model containing control types, attributes and layout information; the control factory module is connected to the configuration analysis module and is used for dynamically creating a corresponding control instance according to the control type information in the structured object model and injecting the analyzed attribute information into the control instance; the layout engine module is connected to the control factory module and is used for automatically arranging and positioning the control instances according to the layout information in the structured object model; and the rendering output module is connected to the layout engine module and is used for rendering and outputting the interface of which the layout is completed to display equipment. According to the method, diversified HMIs are dynamically generated according to different requirements, and the purpose of improving code quality and development efficiency is achieved.

The invention belongs to the technical field of computer graphics geometric processing, and particularly relates to an anisotropic triangular mesh generation method, which comprises the following steps of: acquiring triangular mesh data of an object model, and expanding the triangular mesh data from a three-dimensional space to a two-dimensional plane by adopting a mesh parameterization method; based on the expanded triangular mesh data, inputting the set data of each vertex into a trained deep neural network model to obtain an anisotropy measurement parameter and a predicted sampling point coordinate of each vertex; constructing an ACVT energy function according to the anisotropy measurement parameters and the predicted sampling points, and minimizing the ACVT energy function to obtain optimized triangular grid data on a two-dimensional plane; mapping the optimized triangular mesh data from a two-dimensional plane to a three-dimensional space through barycentric coordinates; the method can guarantee the quality of the generated grid and improve the operation efficiency at the same time.

The invention provides an interval occupancy logic check system modeling method based on formalized verification, which comprises the following steps: classifying signal equipment according to attributes and behavior characteristics of the signal equipment in an interval occupancy logic check system; according to the classification of the signal equipment, defining object attributes of the signal equipment, and establishing an object model of the interval occupation logic checking system; and establishing a security demand verification model based on the object model according to the rule demand of the interval occupancy logic check system. According to the method, formalized modeling is carried out on the interval occupancy logic checking system, the natural language requirement is abstracted into a mathematical model, formalized verification can be conveniently carried out on the interval occupancy logic checking system, and the test comprehensiveness of the interval occupancy logic checking system is guaranteed.

Test verification code generation may be automated. Automating the generation of the test verification code may include generating an integrated circuit design for an integrated circuit that includes the object model and generating the verification code based on the object model. The object model is used by a parser to generate a verification code. The object model may include field values including a protection parameter field and a sequence field. The protection parameter field may include information that identifies node(s) that are associated with an intended protected region in the integrated circuit design and indicates characteristics of an encoding scheme applied in the intended protected region. The sequence field may include information representing a list of signals that can report an error. The verification code may be configured to inject the error during simulation of the integrated circuit design to verify that the system correctly responds to the error.

The embodiment of the invention discloses an image rendering method and device and related equipment, and belongs to the technical field of computer vision. In the embodiment of the invention, for any to-be-rendered object, the three-dimensional geometric model and the three-dimensional material information of the object are extracted through the implicit object model of the object, and the explicit object model of the object is displayed on the front-end interaction interface based on the three-dimensional geometric model and the three-dimensional material information. And editing the display attribute of the explicit object model, and rendering the implicit object model based on the edited display attribute. In other words, only one implicit object model needs to be acquired, and an image rendering scheme combining the implicit object model and the explicit object model can be realized through the method provided by the embodiment of the invention, so that the editability of an image rendering process can be improved, and the high trueness of a rendering result can be ensured.

The invention discloses a BIM model-based object model rapid placement method, equipment, a product and a medium, and the method comprises the steps: selecting an object model, calculating an AABB collision box of the object model, and constructing a multi-modal fitting vector; collision detection and fitting calculation are carried out, fitting with a target is adjusted by moving an object model according to an overlapping value or a fitting vector value, and different situations of overlapping or not are accurately processed through subdivision steps. And finally, updating the positions of the object models, generating position models in batches, and setting the position models to corresponding positions of the corresponding BIM models according to the positions of the object models. By adopting the method provided by the embodiment of the invention, by introducing the multi-modal fitting vector and fitting calculation, a direction basis is provided for automatic fitting in modeling, and a multi-axial fitting requirement is supported so as to adapt to a complex BIM scene requirement, ensure that the placement positions of the models generated in batches are accurate, and improve the automatic placement efficiency of BIM modeling.

The invention provides a robot simulation task generation method, a medium and equipment, and belongs to the technical field of data processing. The method comprises the steps that natural language task description is obtained, task requirement information of a simulation task is extracted from the natural language task description, and the task requirement information comprises a target object needing to be grabbed by a robot, a related object and the position relation between the target object and the related object; querying a target object model matched with the target object from a preset object library; querying a target scene matched with the related object and a target operation space of the target scene from a preset scene library; and forming layout constraint information of the target object model in the target operation space based on the position relationship and the volumes of the target object and the interferent, and generating at least one simulation task meeting the task requirement information under the layout constraint information. The robot simulation task generation efficiency and the robot simulation task generation quality can be improved.

The invention discloses an object-level environment reconstruction method based on a hyper-quadric surface, and the method specifically comprises the steps: constructing four core assemblies, namely a visual data collection module, a semantic preprocessing module, an object modeling and optimization module, and a data association and loopback detection module, based on an ROS2 frame and employing a modular design; the visual data acquisition module adopts a monocular camera to capture RGB image flow in real time and publish an original image, the semantic preprocessing module carries out semantic information extraction based on decoupling type instance segmentation, the object modeling and optimization module carries out super-quadratic surface initialization and outer point elimination, and then carries out super-quadratic surface optimization, and the object modeling and optimization module carries out super-quadratic surface optimization. Obtaining the precisely fitted geometric representation of the object; and the data association and loopback detection module performs dynamic data association and loopback detection enhancement. According to the method, the object modeling error is reduced, the pose estimation precision is improved, the environment perception capability is enhanced, the real-time processing capability can be maintained, and high-level semantic environment cognition support is provided for autonomous navigation of the robot.

An apparatus for reconstructing a non-rigid object comprises a depth sensor (204); a processor configured to repeatedly perform the following steps: capturing a point cloud using the depth sensor, wherein a first point cloud is set as a canonical model; estimating a scene flow (403) using the captured point cloud and a previous point cloud; pre-warping the captured point cloud using the estimated scene flow; estimating a deformation parameter of the captured point cloud; warping the pre-warping point cloud using the deformation parameter; merging the warped point cloud into the canonical model. The apparatus enables accurate and stable modeling of non-rigid objects.

The present application relates to a method, apparatus, computer device, storage medium, and computer program product for three-dimensional reconstruction of an object. The method comprises: extracting image features from a target object image to obtain multiple target feature maps; detecting key points based on the multiple target feature maps to obtain a two-dimensional position heat map corresponding to each key point of the target object; predicting parent vectors based on the two-dimensional position heat map of each key point and each target feature map to obtain a parent vector heat map corresponding to each key point under each three-dimensional coordinate axis; predicting three-dimensional positions based on the parent vector heat map corresponding to each key point and each target feature map to obtain three-dimensional position information corresponding to each key point; and performing three-dimensional reconstruction based on the three-dimensional position information of each key point to obtain a three-dimensional object model of the target object. The present method can improve the accuracy of three-dimensional reconstruction.