426 results about "Object Attribute" patented technology

The invention discloses a distributed data storage management system and method, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining an access log, metadata and content features of each node; data object attributes are judged, cold data are marked, and cold data distribution nodes are positioned; the method comprises the following steps: dividing cold data into permanent, periodic and sudden basic access modes through first-layer classification; generating a composite classification label through second-layer classification; matching a predefined strategy matrix according to a classification result, dynamically selecting a compression algorithm and a storage medium, and allocating bandwidth; operation auditing, compliance verification and exception handling are realized through the smart contract; and collecting strategy execution indexes in real time, and adjusting classification model parameters and resource scheduling weights. Through an intelligent cold data judgment and classification mechanism, cold data is accurately identified and managed, occupation of storage resources is reduced, and the overall utilization rate of the storage resources is improved.

The invention discloses an autonomous visual semantic navigation system and method based on interactive semantic mapping, a semantic scene graph is constructed by sensing the environment through a first visual angle of a robot and combining top-down environment semantic priori, and the method comprises regional spatial distribution identification, object attribute and layout analysis and room type clustering. And in combination with priori knowledge, complete scene representation is formed, the scene graph can be dynamically adapted and updated according to real-time observation of the robot, and the accuracy and timeliness of scene representation are ensured. Besides, a high-level and low-level AI agent module cooperation mechanism is adopted, and the high-level AI agent module is responsible for understanding and decomposing navigation tasks and distributing subtasks and supervising the execution process; the low-layer AI agent module is responsible for further dynamic decomposition and execution of the subtasks and feeds back execution results to the high-layer Agent, a complete task closed-loop monitoring mechanism is formed, and the execution efficiency and robustness of the navigation tasks are improved.

A three-dimensional encasement reinforcement learning method based on multi-modal sensing distillation comprises the steps that a high-fidelity physical simulation environment is constructed, strategy optimization is achieved through a teacher-student double-stage framework, a teacher network fuses vision, article attributes and internal physical state privilege information, and a high-stability encasement strategy is trained through hierarchical action masks and domain randomization; a student network only depends on visual input, teacher action distribution and value functions are aligned through KL divergence and L2 loss, and knowledge distillation is achieved; after deployment, a motion mask is generated in combination with lightweight collision detection. A failure scene can be played back to a simulation environment through a playback mechanism, and a student network is continuously optimized by using a teacher strategy. According to the method, the space utilization rate is remarkably improved in simulation and reality, the turnover rate is reduced, complex physical constraints can be adapted without expensive sensors, and an efficient and robust solution is provided for industrial automation.

The invention relates to the technical field of robots, and discloses a task planning method and device for a robot, equipment and a medium, and the method comprises the steps: building a scene relation graph model containing environment space topology and object attributes; inputting the received natural language instruction into the first language model, and outputting a structured task target; on the basis of the structured task target and the real-time environment perception data, auxiliary information is generated through a second language model, and the scene relation graph model is updated; inputting the updated scene relation graph model into a reinforcement learning model, and outputting an action decision sequence; and the target robot is controlled to execute the action decision sequence, and reinforcement learning strategies of the scene relation graph model and the reinforcement learning model are adjusted according to the environment feedback information. According to the invention, efficient, flexible and accurate task planning of the robot in a complex dynamic scene can be realized, and the efficiency and adaptability of task planning of the robot are improved.

A method of integrating a cryptographic digital asset into a digital software application, includes receiving a digital asset identification (ID) code, the digital asset ID code existing together with a unique owner ID code on a distributed blockchain ledger. The digital asset ID code represents the cryptographic digital asset, and the cryptographic digital asset comprises a plurality of attributes. A virtual object is then represented on the display via the digital software application. The virtual object has an appearance that is based on the plurality of attributes of the cryptographic digital asset, and the virtual object includes a plurality of object attributes. The method further includes modifying at least one of the object attributes according to an aspect of the digital software application or interaction between the character avatar and the virtual object and representing a modified virtual object on the display via the digital software application.

The invention provides a semantic information reasoning method guided by a remote sensing scene graph, which can be applied to the technical field of remote sensing image processing. The method comprises the following steps: segmenting a remote sensing image to generate a scene segmentation image; executing target detection to generate an image block set; performing fine-grained analysis on the image global features and the image block set to generate a scene description text; performing grammar analysis, extracting a triple of objects, object attributes and relation information among the objects, and generating a remote sensing scene graph related to the problem; encoding the problem text into an embedded vector, projecting the embedded vector to a visual feature space, processing the embedded vector through a frozen self-attention layer and a learnable gating layer, and outputting an image-text interaction feature; carrying out attention fusion and double gating balance on the question coding features, and outputting a text code guided by a scene graph; and fusing the interaction features and the text codes, and reasoning local semantic information of the target area. The invention further provides a semantic information reasoning device and equipment guided by the remote sensing scene graph and a medium.

The invention provides a task execution method, device and system based on a model context protocol, and relates to the technical field of artificial intelligence, in particular to the technical fields of cloud services, large models, model context protocol services and the like. The task execution method based on the model context protocol comprises the following steps: in response to a received task request related to a target object, determining tool related information associated with an object attribute of the target object in a model context protocol service; tool related information is sent to a model end where the large model is deployed, and target related information determined from the tool related information based on the large model is received; and based on the target related information, calling a target tool for the model context protocol service to execute a target task related to the task request, and obtaining a task execution result.

The invention relates to the technical field of video generation, in particular to a functional scene modeling and large model cross-dimension content generation method and system.The method comprises the steps that multi-modal scene description data input by a user are received, and the scene description data comprise text data, image data and audio data; analyzing the multi-modal scene description data, and extracting scene object entities, object attribute parameters and inter-object interaction logic; constructing a dynamically adjustable scene model, and instantiating the scene object entity into a functional unit; calling a pre-trained large language model or a multi-modal generation model, and executing cross-dimension content generation; dynamically optimizing output based on user feedback data; and outputting the optimized cross-modal content to the application terminal. According to the method, a scene entity is abstracted into a programmable function unit, real-time dynamic adjustment is supported through an internal parameter interface, the states of all associated units can be linked and updated in a short time by a dependency relationship topological graph, and the hysteresis of traditional script coding is avoided.

The invention belongs to the technical field of video material processing, and discloses a video material intelligent processing method and device based on dynamic semantic driving, and the method comprises the steps: carrying out the frame-by-frame analysis of an input video through a pre-trained semantic feature extraction model, and extracting a deep semantic vector containing an object attribute, a scene context and a multi-modal emotion feature; performing dynamic analysis on the deep semantic vector based on a time sequence attention mechanism, and generating a semantic state change map reflecting a video content evolution rule in real time; generating an adaptive processing strategy through a reinforcement learning strategy network based on a real-time matching result of a personalized processing demand input by a user and the semantic state change graph; performing dynamic processing operation on the input video according to a self-adaptive processing strategy to generate a target video material; according to the invention, the problems of shallow semantic understanding and fixed processing mode in the prior art are effectively solved, the video processing effect and the user experience are improved, and the requirements of diversification, timeliness and individuation are met.

The invention discloses a man-machine interaction method and system based on an intelligent sensor, belongs to the technical field of robots, solves the problem of misoperation caused by abnormal sensor data to a certain extent, and enables the system to still run stably through quality credibility evaluation and a dynamic weight distribution mechanism when part of sensors fail. Meanwhile, based on a dynamic safety boundary strategy of scene semantic understanding, the robot can automatically adjust motion constraints according to object attributes and spatial relations, and collaborative optimization of safety and operation efficiency is achieved in complex tasks.

The present application provides a method for device, and computer storage medium for generating an image caption. The method comprises: obtaining an image to be processed and auxiliary caption information, wherein the image to be processed includes a main object, and the auxiliary caption information includes at least one of the following: name information corresponding to the main object, object category corresponding to the main object, an object attribute corresponding to the main object, and an image tag corresponding to the image to be processed; determining an image feature corresponding to the image to be processed, and an auxiliary feature corresponding to the auxiliary caption information; generating the caption based on the image feature and the auxiliary feature to obtain a target caption corresponding to the image to be processed, wherein the target caption includes the name information of the main object.

The invention provides a control method and system of a sweeping robot, and the method comprises the steps: obtaining image data, three-dimensional space data and sound data of a surrounding environment, and extracting object attribute features, space features and sound attribute features in the environment; fusing the object attribute feature, the spatial feature and the sound attribute feature to obtain an identification result; based on an identification result, executing hierarchical scene adaptive planning and executing a cleaning task by adopting a self-adaptive variational cleaning strategy; after the cleaning task is completed, second image data of the cleaned area are collected; recognizing residual stains on the ground according to the second image data and a preset cleaning effect recognition model; and if the area of the residual stain exceeds a preset area threshold value, triggering a re-cleaning program, and automatically switching a corresponding cleaning mode according to the type of the residual stain. According to the scheme, when the sweeping robot encounters the obstacles in the sweeping area, the sweeping robot can intelligently take accurate countermeasures, and an excellent sweeping effect is achieved.

The invention relates to a method and a device for generating a video with a body and electronic equipment. The method comprises the following steps: analyzing a task instruction and initial environment observation data, generating a key operation step sequence and an associated object set thereof, and constructing a three-dimensional physical constraint graph; generating an initial action video sequence based on the coding condition; in response to single-frame action execution, calculating a spatial error index, and if the object contact distance deviation, the trajectory collision probability or the physical constraint violation value exceeds a preset threshold, triggering the space-time diffusion model to generate a correction frame based on the current physical constraint graph; in response to the completion of the key operation steps, verifying the semantic matching degree of the generated result and the task target, and if the key object attribute loss or the operation logic error is detected, regenerating the task operation step sequence and the physical constraint graph; and updating the object position, the distance threshold and the motion feasible region in the physical constraint graph, and inputting the space-time diffusion model. According to the method, the problems of physical consistency deficiency, error accumulation and semantic understanding splitting are solved.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention relates to the technical field of metadata management, and discloses a method for improving metadata storage and query efficiency, comprising: establishing a meta-model of each type of metadata, the meta-model comprising an object, an attribute and a relationship of the metadata; obtaining a heat value of each meta-model based on historical information of the meta-models, performing dynamic heat partitioning on the meta-models according to the heat values, and adjusting an index strategy, a storage strategy and a cache strategy according to the dynamic heat partitioning of the meta-models; the storage and query process is monitored in real time, index data monitored in real time in different dynamic popularity partitions are analyzed, and the meta-model is adjusted according to the analysis result. According to the method, the corresponding management mode can be dynamically adjusted according to the popularity state of the meta-model, so that the metadata storage and query efficiency is improved on the whole; meanwhile, real-time optimization of the meta-model is achieved by monitoring the storage and query processes in real time, and the metadata storage and query efficiency is improved.

Methods and systems for prompting a large language model (LLM) to generate a description of an object with indications of any unsubstantiated information are disclosed. A prompt is generated to a LLM to generate a description of an object, where the prompt includes one or more object attributes to include in the generated description. The prompt also includes an instruction for the LLM to annotate any portions of the generated description that are, involve, and / or include unsubstantiated information according to a defined format. The prompt is provided to the LLM and the generated description is received. The generated description is parsed to identify, based on the defined format, one or more annotated portions indicating unsubstantiated information. The generated description is presented for display via a user device.

A computer-implemented method is disclosed. The method includes: receiving, via a first user interface, a selection associated with an object; determining a first set of object attributes based on the selection; presenting, via a second user interface, a text prompt for a user to identify a subset of the first set of object attributes; receiving, via the second user interface, an indication of one or more preferred object attributes of the identified subset; and updating the first user interface to display content relating to objects associated with the one or more preferred object attributes.

The invention discloses a multi-object arrangement method and system for a mobile operation robot, and the method comprises the steps: obtaining an object image of the robot at a current visual angle, and extracting the attribute information of an object; constructing a bearing relation matrix and a semantic scene graph; the bearing relation matrix is converted into a directed acyclic graph, an edge set serves as a state space in reinforcement learning, an action space is defined to select one of unselected edges, the depth of the edge set serves as the basis of current grouping, and objects are grouped based on a high-level reinforcement learning model; obtaining a stacking execution sequence of the objects in the group based on the semantic scene graph; and the grabbing posture of the current to-be-operated object is obtained through prediction of the grabbing network, the object is grabbed according to the grabbing posture, and the grabbed object is stacked according to the planned stacking sequence. According to the robot, efficient action decision making is achieved in the grabbing, stacking and placing processes, and the robot has the capacity of carrying multiple objects in different places and orderly placing the multiple objects in a complex environment.

The invention discloses a multi-target constraint force control parameter determination method and a control system for a machine dexterous hand, and the method comprises the steps: obtaining multi-mode state data, determining a current operation stage and an object attribute, constructing a candidate force control parameter set, carrying out the multi-target optimization through employing an NSGA-II algorithm under multiple constraints, obtaining a Pareto optimal solution set, and carrying out the multi-target constraint force control of the machine dexterous hand. And after the target force control parameter group is determined, performing safety projection correction on original control output, and controlling the dexterous hand to execute operation. According to the scheme, through closed-loop collaborative design of stage recognition, parameter optimization, safety correction and online reconstruction, contact safety, grabbing stability, response performance and energy consumption control are considered, the grabbing success rate is increased to 95% or above from 75%, the contact peak force is reduced by 36%, and the control reliability of multi-finger collaborative operation is improved.

Techniques are discussed herein for generating and evaluating driving simulations based on synthetic scenarios. Simulated objects may be controlled based on parameters determining the attributes and behaviors of the objects, and scenarios may be synthetically modified by changing the parameters for a simulated object. For a driving scenario with a synthetic simulated object, a simulation system may analyze driving log data to determine a probability or prevalence associated with the driving scenario. In various examples, the simulation system may determine marginal density estimates for individual attributes of the simulated object, as well as a cumulative distribution function modeling the dependence between the attributes. The joint probability distribution determined for the synthetic simulated object can be used for evaluating the efficacy of the simulation and the performance of the simulated vehicle controllers.

The invention provides an information display method and device, equipment, a storage medium and a product, and relates to the technical field of computers. The method comprises the steps that in a first device, a first object information set is displayed in a first preset page, the first object information set comprises first object information of a preset object in a preset object set, the preset object set is associated with a target live video interface, and target prompt information is displayed at the position adjacent to the first object information of the first target preset object, the business data of the first target preset object meets a preset requirement, the target prompt information is used for prompting that the first target preset object is set as a preset object with a target object attribute, and second object information of the preset object with the target object attribute contains a target label and is displayed at a target position; the second object information set is displayed on a second preset page associated with the target live video interface in the second device. By adopting the technical scheme, the setting efficiency of the target object attribute of the preset object can be improved.

The invention discloses an object identification method and device, a storage medium and electronic equipment. The method comprises the steps that a to-be-recognized image is acquired, and the to-be-recognized image is used for indicating object information in a detection area; the to-be-recognized image is input into a target recognition model to obtain a target recognition result, the target recognition model comprises a defogging branch and a super-resolution branch, the super-resolution branch is used for amplifying the image resolution of the to-be-recognized image to determine the super-resolution feature of the to-be-recognized image, the defogging branch is used for adjusting the local contrast of the to-be-recognized image, and the super-resolution feature of the to-be-recognized image is obtained. The target recognition result is determined according to the super-resolution feature, the defogging feature and the original image feature of the to-be-recognized image, and the target recognition result is used for indicating the object position and the object attribute of the target object in the detection area. The technical problem that the object recognition accuracy is low due to the fact that the image collected in the to-be-detected area is not clear enough is solved.

Embodiments of the present disclosure provide large language models (LLMs) and large multimodal models (LMMs) for managing imaging data. An example method includes extracting a database schema and a data dictionary associated with a medical imaging database that maintains medical images and non-image data associated with a plurality of clinical studies; extracting object attributes associated with the medical images that are descriptive of the medical images and corresponding studies; and training an LLM to generate search queries from natural language requests based on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template including partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries. The trained LLM can then be used to generate a first search query from a first natural language request.

A system for faster object attribute and / or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and / or intentions are discussed herein.

The invention discloses a visual cooperative processing method, system and device and a medium, and relates to the technical field of big data processing, and the method comprises the steps: obtaining a target detection result through detecting a target object in target video data and / or the attribute of the target object; aggregating the target detection results to obtain multi-dimensional object attribute information; for a current frame and a preorder frame in the target video data, judging whether the multi-dimensional object attribute information has a change within a preset range, and obtaining a judgment result; and calling a corresponding processing model according to the judgment result, and outputting a model co-processing result. Complex visual information is decoupled into multiple dimensions, and rich object attribute information is generated. A lightweight model is used in a simple task, and a large model is called in a complex task, so that the balance between high performance and low power consumption is realized. And different task complexities can be flexibly adapted, and efficient operation of the system in a resource-limited environment is ensured.

The invention provides a deep processing method and system for a digital delivery result, and relates to the technical field of data processing. A delivery result file set is disassembled and recombined into a unified object main data set and a three-dimensional entity set; each object attribute record and each object relation record have a unified object identifier, a source identifier and evidence positioning information at the same time, and multiple sets of coding calibers of the same object can be gathered to the same unified object identifier; refined model replacement is executed by taking a unified object identifier as an anchor point in three-dimensional treatment, continuity of a composition relationship and a connection relationship is kept, and after project handover and commissioning, online and field inconsistency found by operation and maintenance personnel is converted into a revision chain capable of being rechecked, judged and audited, so that the operation and maintenance efficiency is improved. Therefore, the problems that the unstructured data is difficult to reuse, the coding calibers are different, the object relationship is easy to lose and the data in the operation and maintenance period is not credible are solved.

An object-based data storage system implemented by a computer for storing data in a plurality of objects (16A, 16B, 25A, 25B). The data storage system comprises a storage medium (80) configured to store said plurality of objects. Each one of the plurality of objects comprises a plurality of fields including a data field (18) configured for storing said data therein and, a separate object ID attribute field (17) configured for storing identification information associated with the object. The information stored within the object ID attribute field of at least one (16A, 16B) of the plurality of objects comprises metadata other than identification information associated with the at least one object. A processor is configured to access the at least one object (16A, 16B) from amongst the plurality of objects stored within the storage medium at least to retrieve information stored within an object ID attribute field (17) hereof thereby to retrieve the metadata.

Embodiments disclosing a controller for controlling a robot to perform a task are provided. The task is performed in an environment that is represented by an input image. The controller causes segmenting of an object in the input image. A confidence level of segmentation is updated by comparing the segmented object with constrained affined transformations of a template of the object. The constrained affine transformations are based on constraints indicative of a property of the object. The property of the object and the updated confidence level of segmentation are then used for performing the task.

The embodiment of the invention provides a method and device for identifying logistics attributes, electronic equipment and a storage medium. The method comprises the steps that object information of a logistics object to be recognized is acquired, and the object information is used for representing object attribute information of the logistics object; a plurality of logistics categories of the logistics object are determined according to the object information, and the logistics categories represent corresponding categories of the logistics object in a category system constructed based on logistics attributes; according to a preset mapping relation, candidate logistics attributes corresponding to all the logistics categories are determined, target logistics attributes corresponding to the logistics objects are determined based on the priorities of all the candidate logistics attributes, and the preset mapping relation comprises the mapping relation between the logistics categories of the logistics objects and the candidate logistics attributes. According to the embodiment of the invention, the identification of the logistics attributes is converted into the identification of the logistics categories, the influence of the change of factors such as logistics policies on the identification result of the logistics attributes is avoided, and the accuracy of the identification of the logistics attributes is improved.