240 results about "Object model" patented technology

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.

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 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.

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.

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 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 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.

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.

A virtual machine (VM) management utility tool may deploy an object model that may persist one or more virtual machine dependencies and relationships. Through a web front-end interface, for example, the VMs may be started in a specific order or re-booted, and the tool automatically determines the additional VMs that need to be re-booted in order to maintain the integrity of the environment. Through the web interface, for example, the object model may be managed, and start-up orders or VM dependencies may be updated. For VMs that may not start under load, the object model may block access to the VM until the VM is fully initialized.

The invention discloses an object identification method and device based on an improved grey wolf algorithm, equipment and a medium. The method comprises the steps that a traditional grey wolf algorithm is optimized through a search strategy based on dimension learning hunting, and an improved grey wolf algorithm is obtained; acquiring historical identification reference data in the process control scene, and performing clustering processing on the historical identification reference data to obtain clustering data corresponding to a plurality of control working conditions; for the clustering data corresponding to each control working condition, performing parameter searching through an improved grey wolf algorithm to obtain a target object model corresponding to each control working condition; determining a transition transfer model between the two control working conditions in each working condition association group based on the target object model corresponding to each working condition association group with the transition relationship, and determining a multi-working-condition object model according to the target object model corresponding to each control working condition and the transition transfer model between the two control working conditions in each working condition association group. According to the embodiment of the invention, the identification efficiency of the controlled object can be improved.

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 application provides a method and device for generating a proxy model in a virtual scene, a computer device and a storage medium. The method comprises the following steps: obtaining potential visible set information of a virtual scene and object models in the virtual scene; obtaining a visible bounding box of the object models based on the potential visible set information of the virtual scene; obtaining a model bounding box of the object models; clustering the object models according to the model bounding box and the visible bounding box of the object models to obtain a plurality of target object clusters; generating a visible bounding box of the target object clusters according to the visible bounding box of each object model under the target object clusters; generating a proxy model corresponding to the target object clusters; and determining a model application position point of the proxy model based on the visible bounding box of the target object clusters. The proxy model corresponding to each target object cluster and the application position range thereof are used to reduce DrawCall and game picture loading overhead while ensuring the picture effect of the game picture and avoiding the degradation of the game picture.

The application relates to a data processing method and device based on a configuration management system, computer equipment and a storage medium. The method comprises the following steps: acquiring an object model, an attribute model and a relationship model in the configuration management system; determining a first association relationship between the object model and the attribute model and a second association relationship between the object model and the relationship model based on object coding corresponding to each object; constructing a relationship architecture of the configuration management system based on the first association relationship and the second association relationship; and performing data processing on the configuration management system based on data processing parameters corresponding to a data processing operation and the relationship architecture. Through the above method, data management is directly performed based on the data processing operation, and the data management of the configuration management system is simplified.

The application relates to a construction method for obtaining a viewpoint evaluation object model, and belongs to the field of public opinion analysis. An event abstract text generation model is established, which is used for inputting information text containing event content to obtain event abstract text; a sentence annotation model is established, which is used for inputting viewpoint text for evaluating the event and the event abstract text to obtain a viewpoint with the event abstract text as semantic guidance; wherein the viewpoint is a sentence sequence extracted from the viewpoint text; a viewpoint evaluation object extraction model is established, which is used for inputting the viewpoint and the event abstract text to obtain an evaluation object of the viewpoint; wherein the evaluation object is a word sequence extracted from the event abstract text. The application can accurately obtain a viewpoint with the event as an evaluation object, and improves the analysis performance.

The invention provides an image processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a control signal and an observation view angle vector for a target object model; decoding based on the control signal to obtain a dynamic neural texture map of a first area in the target object model in an appearance dimension; decoding based on the observation view angle vector to obtain a view angle neural texture map of the first region in the view angle dimension; and fusing the dynamic neural texture map, the visual angle neural texture map and a pre-trained diffusion neural texture map to obtain a texture map of the first region. According to the invention, the detail expression capability of the texture for rendering the image can be improved.

The invention provides an intelligent association method and system for a protocol template and an object model based on a large model, and relates to the technical field of intelligent conversion. The method comprises the steps that an input protocol template XML file is loaded and analyzed, and first key feature information is extracted; converting the extracted first key feature information into data in a structured intermediate representation form; loading a standard object model from a local object model library, and extracting second key feature information of the standard object model; and according to the target large model, matching the second key feature information with the data in the structured intermediate representation form by calculating semantic similarity in combination with a cue word optimization method, and generating a standardized XML format association relationship table. The problem that in the prior art, a manual analysis method is difficult to meet the rapid and accurate processing requirement is solved.

The execution-time reporting of telemetry of execution of a software program includes identifying a software program and accessing an event stream with multiple event entries associated with the software's components. An object model is generated that comprises a hierarchy of objects and properties associated with the events by creating objects for the software's components, including identifiers, properties, and timestamps. When an AI entity is instantiated for use during the software's execution, an AI object is created with an identifier and properties. The AI object is dynamically modified to reflect events associated with the AI entity during operation of the software program. The telemetry associated with the AI entity is reported according to subscriptions for the telemetry sessions.

Automating testing or use of an application under test (AUT) is greatly simplified when domain object model (DOM) information is available. However, DOM information is not always available. Systems and methods are provided wherein graphical elements are identified and converted to computer code. The computer code is applied to a transparent layer and as a user visually interacts with the AUT, the transparent layer identifies the inputs and the root graphical elements. The inputs are then forwarded to the root graphical elements. The inputs and target graphical elements may be recorded and played back (as-is or modified) on the same or different AUT. The playback then allows a specific graphical element to receive a particular input, even if DOM information remains unavailable.

The invention discloses a subcritical coal-fired unit coordination control model identification method and system, and the method comprises the steps: collecting the data of the target operation parameters of a subcritical coal-fired unit, forming historical data, and building two neural network models based on the historical data; selecting historical data under different load change conditions, reconstructing the selected historical data, and respectively inputting the reconstructed historical data into the corresponding trained neural network models to obtain simulation values of the main steam pressure and the main steam flow; and based on the simulation values and historical data of the main steam pressure and the main steam flow, calculating a dynamic relationship among different variables in the controlled object model of the coordination control system of the subcritical coal-fired unit by using an optimization algorithm. According to the method, the problems that the subcritical coal-fired unit cannot be switched to the manual state to carry out the open-loop test in the AGC mode and an engineering calculation method is inaccurate due to variable loads are solved, the identification precision is improved, the on-site debugging time of engineers is shortened, and the working efficiency is improved.

A computer-implemented method includes generating a user interface; receiving a set of object models; processing the received object models; and rendering the document model via a viewport. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to generate a user interface; receive a set of object models; process the received object models; and render the document model via a viewport. A computing system includes a processor and a memory having stored thereon instructions that when executed by the process, cause the computing system to generate a user interface; receive a set of object models; process the received object models; and render the document model via a viewport.

The invention relates to a method for performing static inspection on hardware description language codes, which is characterized in that an accurate chip object model is constructed and a submodule interface model is utilized to perform efficient cross-module analysis, so that the inspection speed is effectively improved, and time consumption caused by complete analysis of whole design details in a traditional method is avoided; meanwhile, deep semantic level analysis is carried out based on the object model, so that the false report rate and the missing report rate are effectively reduced, and the problem positioning accuracy is improved; besides, by carrying out intelligent classification and priority ranking on inspection results and supporting unified inspection on design and verification codes, user experience is greatly optimized, the error correction time of engineers is shortened, and therefore the efficiency and quality of chip design early-stage defect discovery are comprehensively improved.

The invention discloses a three-dimensional model generation method and device, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring an object image of a first object; extracting texture feature representation of the first object from the object image; obtaining a first object model; adjusting a model structure of the first object model based on the texture feature representation, so that the first object model is matched with the texture feature representation to obtain a second object model; and converting the texture feature representation into a texture image, and fitting the texture image to the second object model to obtain a three-dimensional model corresponding to the first object. The contour of the first object model is adjusted under the constraint of the appearance performance, so that the texture is matched with the adjusted first object model, and the three-dimensional model corresponding to the first object is obtained after the texture image is mapped and covered to the adjusted first object model, so that the generation efficiency of the three-dimensional model and the man-machine interaction efficiency are improved, and the user experience is improved. And time resources and human resources are saved.

The present invention discloses a semantic component pose estimation method based on deep learning, and proposes a SURF-FPS algorithm and a CPFNet model. The SURF-FPS algorithm is a three-dimensional key point sampling method for object models combined with the SURF algorithm, which is used in the data preprocessing stage. It can comprehensively consider the geometric features and potential surface texture features of the object, so that the selected object key points have more stable feature representativeness; the CPFNet model can make full use of the RGB image and depth information of the sample, and perform high-dimensional feature fusion between image information and point cloud information between each encoding layer and decoding layer; the point cloud feature encoding module merges the spatial information of the point cloud with the corresponding high-dimensional feature information, and the module can more completely extract the contextual component features of the target object, and perform global information fusion in the subsequent attention module, so as to comprehensively improve the pose estimation effect by utilizing key components with more obvious features.

The invention provides a data transmission method and device for a variable-length data class of a high-level architecture standard, and the method comprises the steps: constructing a standard data object of the variable-length data class in an object model template of the high-level architecture standard, and generating a federal standard file; calling a bottom support environment and data length analysis and release operation of a release processing function based on a federal standard file through a data sending end federal member, and releasing transmission data of a variable-length data class to a distributed network; through the data subscription end federation member, the bottom layer supporting environment and the callback function of the variable-length data class are called based on the federation standard file, the transmission data of the variable-length data class are received, direct and efficient transmission of the dynamic length data is achieved, the data bearing flexibility and the operation efficiency of the simulation system are remarkably improved, and the simulation efficiency is improved. The development and maintenance process is simplified through a standardized interface, and the overall expandability of the system is enhanced on the basis of being completely compatible with an existing framework.

The invention is suitable for the technical field of deep learning, and provides a data set generation method, a model training method and a computer, and the method comprises the steps: obtaining a video stream containing a target object, and enabling a camera to move around the target object, and carrying out the shooting of the video stream; receiving interactive annotation input of a user on the target object in a starting frame image of the video stream; calling an image segmentation model to generate an initial mask based on the interactive annotation input; based on the initial mask, automatically tracking and generating an object mask sequence of subsequent multiple frames of the video stream through a time sequence consistency model; and according to an object model of the target object, the initial mask and the object mask sequence, performing pose labeling on the multiple frames of images in the video stream to obtain real pose data. Through the method, the training data set can be automatically generated without manual annotation under the condition of not depending on equipment such as a mechanical arm and a rotary table.

Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

The application discloses a kind of based on system disturbance response's self anti-interference controller setting method, comprising the following steps: determining the model order of controller and controlled object;According to model order, data vector and model parameter vector are set;Select the initial value of calculation of feedback gain matrix and data covariance matrix;Recursive calculation feedback gain matrix, data covariance matrix and the estimated value of the model parameter vector, until the estimated value of model parameter vector tends to be stable;According to stable value, calculate the coefficient estimate of controlled object model;Based on each coefficient estimate, design self anti-interference controller parameter.The method of the application can guarantee system closed loop and system set value unchanged, only according to the disturbance response of system setting self anti-interference controller parameter, overcome the problems existing in prior art, the method of the application has good tracking and anti-interference performance, help to further promote the field application of self anti-interference controller in thermal process control.

1. Name of this design product: Product management graphical user interface for electronic equipment. 2. Purpose of this design product: an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. The electronic equipment is of conventional design, and the rear view, left view, right view, top view and bottom view are omitted. 6. Designate Design 1 as the base design. 7. Purpose of the graphical user interface: The main view of Design 1 is the homepage operation interface after entering the system; Design 1 interface change status diagram 1 is the interface displayed after selecting "Test Product 1" and clicking "View" in the main view of Design 1; Design 1 interface change status diagram 2 is the interface displayed after clicking "Object Model" in Design 1 interface change status diagram 1; Design 1 interface change status diagram 3 is the interface displayed after clicking "Add New Attribute" in Design 1 interface change status diagram 2; Design 1 interface change status diagram 4 is the interface displayed after entering the new attribute information in Design 1 interface change status diagram 3 and clicking "OK". The main view of Design 2 is the homepage operation interface after entering the system; Design 2 interface change state diagram 1 is the interface displayed after selecting "Test Product 1" and clicking "View" in the main view of Design 2; Design 2 interface change state diagram 2 is the interface displayed after clicking "Object Model" in Design 2 interface change state diagram 1; Design 2 interface change state diagram 3 is the interface displayed after clicking "Function Definition" under the object model in Design 2 interface change state diagram 2; Design 2 interface change state diagram 4 is the interface displayed after clicking "Add Function" in Design 2 interface change state diagram 3; Design 2 interface change state diagram 5 is the interface displayed after clicking "Add Parameter" in Design 2 interface change state diagram 4; Design 2 interface change state diagram 5 is the interface displayed after entering the new parameter information in Design 2 interface change state diagram 4 and clicking "OK"; Design 2 interface change state diagram 6 is the interface displayed after clicking "OK" in Design 2 interface change state diagram 5. The main view of Design 3 is the homepage operation interface after entering the system; Design 3 interface change status diagram 1 is the interface displayed after selecting "Test Product 1" in the Design 3 main view and clicking "View"; Design 3 interface change status diagram 2 is the interface displayed after clicking "Object Model" in Design 3 interface change status diagram 1; Design 3 interface change status diagram 3 is the interface displayed after clicking "Event Definition" under the object model in Design 3 interface change status diagram 2; Design 3 interface change status diagram 4 is the interface displayed after clicking "Add Event" in Design 3 interface change status diagram 3; Design 3 interface change status diagram 4 is the interface displayed after entering the new time information in Design 3 interface change status diagram 3 and clicking "OK".