63 results about "Object type" patented technology

Techniques may generate realistic synthetic data by programmatically generating a configuration file object type and relationship data. This configuration file may be used to retrieve source data matching the object type(s) and / or specific records indicated by the configuration file. The techniques may detect and anonymize private / proprietary information and may determine statistical characteristic(s) of the source data. A batch of prompt(s) may be generated using the source data, the statistical characteristic(s), and the configuration file and may be transmitted to one or more instances of a transformer-based machine-learned model. Sets of synthetic data received from the model instance(s) may be de-duplicated, checked for similarity to the source data (e.g., via embedding the synthetic data and the source data), and may be used to generate synthetic object(s) using the relationship(s) and / or other data indicated by the configuration file. These synthetic object(s) may then be deployed in a software environment.

The invention discloses an object track prediction method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computers, and the method comprises the steps: obtaining the multi-modal motion data of a target object, inputting the multi-modal motion data into a Transform-based object type classification model, and obtaining the object type of the target object; determining a target language instruction according to the object type and a preset language instruction template, and combining the target language instruction with the multi-modal motion data to obtain input data; track prediction is conducted on the input data through a target large language model, a predicted track of the target object is obtained, and the target large language model is obtained through training of a parameter efficient fine tuning technology. According to the embodiment of the invention, high-precision and high-reliability trajectory prediction of various objects in various complex scenes can be realized.

The present disclosure involves systems, software, and computer implemented methods for integrated data privacy services. An example method includes determining to initiate an integrated end of purpose protocol for an object of an object type. Target applications are determined that are allowed to process objects of the object type for at least one purpose, based on identified purpose information. An end-of-purpose query is provided to the target applications and an end-of-purpose status is received from each target application that indicates whether the application is able to block the object. The received statuses are evaluated to determine whether an aligned end of purpose has been reached for the object. In response to determining that the aligned end of purpose has been reached for the object, a block command is provided to each of the multiple applications that instructs a respective application to locally block the object.

The present disclosure provides an object positioning method, device, equipment, medium and program product, and relates to the technical field of image processing. The method converts scene data reflecting the geometric information of the surface of an object into a set of local geometric primitives rich in spatial position, geometric type and characteristic parameters, calculates the geometric affinity between the primitives to represent the possibility that they belong to the same object, and then uses a clustering algorithm to realize instance segmentation, thereby solving the technical problem of low precision and poor robustness of traditional object positioning methods caused by occlusion, background interference and a large number of object types in a complex environment, realizing automatic object instance segmentation and positioning based on geometric clues without a large amount of manual annotation, and achieving the effects of improving the recognition accuracy of objects with complex geometric structures, enhancing the generalization ability of the method in real scenes, and significantly reducing the dependence on pre-training data and manual annotation.

The application discloses a kind of training method, device, computer equipment and storage medium of object classification model, the method includes: obtaining source sample field and target sample field;First sample data in source sample field and second sample data in target sample field have the object characteristics of different modalities of training object;Call object classification model to carry out feature calculation between first sample data and the object summary characteristics corresponding to training object, and obtain feature summary deviation;Call object classification model to carry out feature matching to the object summary characteristics corresponding to training object for second sample data, and obtain the predicted object type of the object to which second sample data belongs;According to the predicted object type of the object to which second sample data belongs and the object type label carried by second sample data, generate type prediction deviation;According to feature summary deviation and type prediction deviation, update the model parameter of object classification model.Using the present application, the classification accuracy of the object classification model trained can be improved.

The invention relates to a method for identifying and / or sorting objects, in particular for recycling materials, comprising the steps of: Linking at least one first object type to object identity information via a reference object type property uniquely identifying the first object type; performing at least one learning phase for teaching at least one KI algorithm, the learning phase comprising analyzing at least one object having the reference object type property for an object property; establishing a correlation between the object identity information and the at least one object property, the correlation comprising associating the at least one analyzed object with the first object type; analyzing at least one object for at least one object property; and calculating an object identity of the object to the first object type using the at least one KI algorithm. The invention further relates to a system for identifying and / or sorting objects based on artificial intelligence technologies.

Strategic actions can be performed before and during runtime to reduce the cognitive load of an LLM during transformation of a natural language request into a refined structured request. During a development stage, object-type-specific examples are collected; a given example includes an example natural language request and a corresponding example structured request representing a desired LLM output for the example natural language request. The examples can include function calls to train the LLM to include function calls in its output to delegate certain sub-tasks. At runtime, a prompt generation module generates a prompt for a natural language request which includes the natural language request, one or more of the examples, and a data model. The prompt is input to the LLM which produces a preliminary structured output including one or more function calls. The post-processing module executes the function(s) and generates a refined structured output based on the execution results.

The invention provides a structured data processing method, and relates to the technical field of data processing, and the method comprises the following steps: obtaining task structured data of a target task, obtaining object structured data, and if the object type of a target object belongs to the task type of the target task, obtaining the object structured data of the target task; if the attribute data of the target object meets the sub-task condition corresponding to the intermediate task and the geographic area where the object is located belongs to the task application area of the target task, taking the target task as the intermediate task, and if the attribute data of the target object meets the sub-task condition corresponding to the intermediate task, taking the sub-score corresponding to the sub-task condition as the attribute score of the intermediate task; and taking the sum value of all attribute scores of the intermediate task as a task matching score of the target object and the intermediate task, and if the task matching score of the target object and the intermediate task is greater than a matching success threshold value corresponding to the intermediate task, judging that the target object and the intermediate task are successfully matched. Therefore, the accuracy of judging whether the target object and the target task are successfully matched or not is improved.

This invention provides a data processing system for object classification, comprising: a target geographic region, a processor, and a memory storing a computer program. When the computer program is executed by the processor, it performs the following steps: obtaining a list of object types corresponding to the target geographic region; obtaining a list of object identifiers corresponding to the list of object types; obtaining a list of object quantities corresponding to the list of object types; and obtaining a list of object information corresponding to the list of object types based on the list of object identifiers and the object quantities. It is understood that this invention sets multiple object types, processes data collected by all devices capable of collecting object information within the target region, and records the object information, obtaining object identifiers and corresponding object characteristics within the target region, and further obtaining object identifiers and object quantities corresponding to object types, which helps improve the accuracy of object classification.

The present disclosure relates to a method for recognizing an object. The method comprises the following steps: i) Comparing a detected object from a second frame with at least one detected object from a first frame using at least one of the following parameters: a) distance in terms of distance, in particular the Euclidean distance between the objects; b) Difference between the objects in their geometric dimensions, in particular the height and / or width of the objects; c) Difference between the objects in their preferably optical and / or acoustic appearance; d) Equality of the objects with respect to an object type; ii) Evaluating the results from step i) to determine whether the detected object of the second frame is the detected object of the first frame. The present disclosure further relates to a device (1) for recognizing an object, as well as a machine learning model and a computer program product.

Disclosed techniques provide improved relationship evaluation for computing objects, including to provide improved resource management. First metric values for object instances of a first set of a plurality of object instances of an object type are received. A graph data structure with nodes holding identifiers and metric values is instantiated. Directed edges are generated between nodes using the metric values, and edge weights are assigned based on these values. A graph can be rendered on a user interface, or code can be executed to identify a node pointed to by another node. When the code is executed, the identifier can be used to adjust resource allocation for the identified node, adjust prioritization of processing using the identified node, generate a notification that includes the identifier of the identified node, or trigger a computing process that uses the identifier of the identified node as an argument.

The present disclosure relates to a Python code static analysis method and device, the method comprising: obtaining source code of a Python project, and converting the source code into an abstract syntax tree; generating an abstract object, the abstract object being used to simulate an object created in a running process of the source code; binding the abstract object to a corresponding variable based on semantic information contained in the abstract syntax tree and Python syntax, to obtain a pointing analysis result of the Python project; obtaining a speculated variable according to a node type of a node in the abstract syntax tree, and obtaining a variable type speculation result of the Python project by speculating a type of each speculated variable; and judging whether the Python project violates a code programming rule and touches an object type error based on the pointing analysis result and the variable type speculation result, to obtain a static analysis result of the Python project. The present disclosure can supplement variable types that cannot be reasoned in the prior art based on a speculation method of syntax and semantics, and comprehensive Python code static analysis is achieved.

The embodiment of the invention provides a model generation method, a display method, equipment, a storage medium and a program product, and the method comprises the steps: providing selection prompt information of at least one part model corresponding to a plurality of part types for a user; the plurality of component types belong to target object types; in response to the component model selection request, determining target component models respectively selected for the plurality of component types; forming a first object model corresponding to the target object type by the target component models corresponding to the plurality of component types; in response to the map making request, determining a map material provided by a user, and based on the map material, generating a model map corresponding to the first object model; and mapping the model map into the first object model to obtain a second object model. According to the technical scheme provided by the embodiment of the invention, the flexibility of manufacturing the object model is improved.

The embodiment of the invention provides a task information generation method, an object processing method, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring classification demand information; classification dimension analysis is carried out according to the classification demand information, type labels under different classification dimensions and label description information of the type labels are generated, and the different classification dimensions correspond to different type labels and the label description information of the type labels; and for at least one classification dimension in different classification dimensions, type identification task information corresponding to the type label is generated based on the type label and the label description information under the classification dimension, and different types of identification task information are used for indicating object type identification under different classification dimensions. By means of the technical scheme, the accuracy of the generated type recognition task information can be improved, and then the accuracy of object type recognition based on the type recognition task information is improved.

To provide an object type specifying device, an object type specifying method, and an object type specifying program capable of correctly specifying the type of an object picked up by a person.SOLUTION: The object type specifying device is provided with: a position detecting unit for detecting the position of an object; a storing unit for storing information indicative of a type of the object at the position of each object; a determining unit for determining, based on information from a sensor, whether the object has been taken out or the object has been placed; an object specifying unit for specifying, when the determining unit determines that the object has been taken out, the type of the taken-out object based on the output of the position detecting unit and the information stored in the storage unit; and an updating unit for updating, when the determining unit determines that the object has been placed, the information stored in the storing unit by using images captured by a camera for capturing the arrangement of each object from the front side.SELECTED DRAWING: Figure 18

The application discloses a kind of full-system state persistence method and system suitable for library operating system.The application is first configured and initialized to Unikernel virtual machine system;Secondly, the state of Unikernel virtual machine system is abstracted as a versioned object type set;Then, based on the object type set, a one-way linked list structure of superblock anchoring is used to implement incremental log management, and the object metadata and disk location are tracked and persisted by fixed-size log entries;Then, through the resource partition model, the management tasks of object metadata and disk location are offloaded to processor execution by asynchronous decoupling architecture, to realize core decoupling;Finally, external synchronization queue intercepts and delays the release of outgoing data packets, ensures that the system state is persisted after sending, and uses adaptive strategy to optimize performance.The application is linked to Unikernel as a library, and full-system persistence can be achieved without modifying the source code of application programs or third-party libraries.

An object classification device generates region unit information indicating the region information of the object, classifies the object by determination of the object type or state based on the region unit feature value extracted from the region unit information, and the partial region unit feature value extracted from the partial region of the input image, and displays the object classification result for the user. This makes it possible to classify the object type or state accurately even in spite of co-existence of the objects different in size, length, shape, and the like in the image.

Some embodiments provide a non-transitory machine-readable medium that stores a program to generate an execution graph. The execution graph includes a semantic layer that is generated based on the processes involved in the system and the object types shared between the processes. The execution graph further includes an execution layer that is generated by modifying the semantic layer to include object instances that are involved in the processes. The execution layer can be presented on a display, thus allowing a user to visualize the process interactions taking place between the processes.

In an example embodiment, at the beginning of a session of a software application, the archiving plug-in determines whether there is any archived data for each possible archived object type. A flag can then be set indicating whether there are or are not any archived data for any possible archived object type. Then, when a call comes into the archiving plug-in (such as when a search is performed), rather than blindly checking the archive for matching data and determining whether the matching data can be accessed by the searching user, the flag is checked first. Only if the flag indicates that there are archived data for some possible archived object type will the call be serviced. Otherwise the call will be ignored / stopped.

Described are techniques for automated least privilege role definition. The techniques include enumerating access patterns including a subject, a verb, an object location, and an object type of respective Application Programming Interface (API) calls in source code associated with a binary program. For a first function call in the source code, the technique is configured to generate a first object type based on an API definition associated with the function call, a first verb based on an API-to-verb matching database, a first subject based on a program-to-subject mapping database and the binary program, and a first object location based on the API definition associated with the function call. The technique further includes generating a Role Based Access Control (RBAC) role definition based on the enumerated access patterns.

This invention belongs to the field of PLC automation technology, specifically relating to a PLC project description method and system. The method includes: describing a PLC project in a development environment based on the TOML markup language; creating corresponding program units, global variable sets, periodic tasks, and devices according to the description of the PLC project, and encapsulating them in the PLC project file, or modifying the attributes of program units, global variable sets, periodic tasks, or devices in the PLC project file according to the description of the PLC project; and exporting the PLC project file. The PLC project and its corresponding program units, global variable sets, periodic tasks, and devices are all edited and generated or modified according to the description format of metadata + object definition, and the object definition section at least defines fields representing the operation mode, fields representing the object path, and fields representing the object type.

Systems and methods include reception of an instruction to initiate an inquiry associated with a first instance of a first object type, querying of a system for object metadata defining a plurality of fields of the first object type, generation of a first user interface presenting one or more of the plurality of fields, reception of user input to the first user interface, the user input specifying a first one or more fields of the presented one or more of the plurality of fields, generation of the inquiry specifying the first one or more fields and the first instance, transmission of the inquiry, reception of a response to the inquiry including data associated with the first one or more fields and the first instance, and instructing of a system to update the data associated with the first instance of the first object type based on the received data associated with the first one or more fields and the first instance.

The invention provides an evaluation object extraction method and device, equipment, a medium and a product, and relates to the field of artificial intelligence. According to the method, a to-be-extracted text and a target evaluation object type are obtained, wherein the to-be-extracted text comprises multiple evaluation object types; inputting a to-be-extracted text into the self-coding convolution fusion model to obtain a target label sequence of the to-be-extracted text, the target label sequence being used for indicating an evaluation object type and an evaluation object type probability corresponding to each word in the to-be-extracted text; and based on the target label sequence and the target evaluation object type, extracting a target evaluation object in the to-be-extracted text. According to the method, the accuracy, pertinence and reliability of evaluation object extraction are effectively improved by defining the target and utilizing an advanced model and a reasonable extraction strategy.

Systems and methods for validations related to software are provided. In some embodiments, a method for building software with software action logging includes accessing a target object type. In some embodiments, the target object type includes one or more object properties. In some embodiments, the method further includes accessing an action type. In some embodiments, the action type includes one or more action parameters and is associated with one or more action rules. In some embodiments, the action type is associated with editing a target object of the target object type. In some embodiments, the method further includes generating an action log type that includes at least one of the one or more object properties and at least one of the one or more action parameters. In some embodiments, the action log type further includes an action context associated with an action of the action type.

The invention discloses a dexterous hand grabbing posture generation method based on object shapes. Firstly, a grabbing posture data set is constructed; then, constructing a de-noising diffusion network, and introducing a grabbing posture prompt text to train the de-noising diffusion network; the grabbing posture prompt text at least comprises an article type, a shape and grabbing posture description, and inputting the grabbing posture prompt text into the large language model to obtain grabbing posture text description; performing text coding on the grabbed posture text description to obtain grabbed posture text features; cascading the grabbed posture text features with the object point cloud features to obtain point cloud text fusion features; inputting the point cloud text fusion feature and the noise capture posture into a denoising diffusion network, and predicting noise; and finally, inputting the object point cloud features, the noise grabbing posture and the random time step into the trained de-noising diffusion network to generate a final grabbing posture. The grabbing posture better conforms to the shape of an object, and the grabbing stability and success rate of the dexterous hand are improved.

The invention provides a code generation method and device and a computing device cluster. In the embodiment, the method comprises the steps that a design draft of a page is obtained, the design draft is used for indicating M layers forming the page and the layout of the M layers, and the layers are basic units for drawing the page; based on the design manuscript, first objects and second objects which are similar in the design manuscript are determined, the first objects are a plurality of first graph layers in the M graph layers, the second objects are a plurality of second graph layers in the M graph layers, and the first objects and the second objects belong to the same object type; and performing code reuse on the first object and the second object to obtain a first page code of the first object and a second page code of the second object. According to the scheme, the similar objects can be automatically recognized, code reuse is carried out on the similar objects, and the code generation efficiency is improved.

In some examples, a system identifies an initial block input / output (I / O) operation that involves writing metadata to a data object, where the metadata of the initial block I / O operation includes initial object type information. Based on a header from a second block I / O operation that involves writing object contents to a target data object, the system generates secondary object type information relating to an object type of the target data object. The system compares the initial object type information in the metadata of the initial block I / O operation with the secondary object type information. Based on this comparison, the system determines whether a data anomaly has occurred.

In some examples, systems and methods for selecting object types and / or managing object models are provided. For example, a method includes: receiving a search query related to a use case; searching one or more ontologies based on the search query to generate a list of object types, the list of object types including one or more object type groups, each object type group of the one or more object type groups including a set of pre-selected object types; ranking the list of object types based on one or more ranking criteria; generating a presentation representing the list of object types based at least in part on the ranking; and causing a display of the presentation representing the list of object types.

A process of causing an event using an object associated with a use object type to occur each time a first input operation is performed once is performed. A counter associated with the use object type is changed each time the event occurs. If the counter becomes included in a reference range, the use object type is switched from a first object type to a second object type. Furthermore, when the first input operation is repeatedly performed a plurality of times, if the counter becomes included in the reference range, a process of causing an event using an object associated with the second object type is temporarily stopped and then restarted.

The application relates to an object completion method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring missing object information of a missing object in a current simulation scene; determining a target reference object type and a target relative spatial relationship matched with the missing object type based on a target matching rule, and acquiring a target reference object instance corresponding to the target reference object type from a current object instance of the current simulation scene; the target matching rule comprises a plurality of object types and relative spatial relationships between different object types; target pose information of the missing object is determined based on reference pose information of the target reference object instance in the current simulation scene and the target relative spatial relationship; and the target pose information is used for completing the missing object in the current simulation scene. The method can complete the missing object in the simulation environment and improve the integrity of the simulation environment.