6 results about "Feature class" patented technology

This invention relates to the field of computer vision and image processing, and proposes a method and system for automatic 3D modeling of 2D engineering drawings. The method includes the following steps: inputting 2D engineering drawings; parsing the context, generating a feature to-do list, and establishing a cross-view spatial mapping coordinate system; extracting the component types from the feature to-do list; obtaining parameterized topological constraint rules for the component types from a preset knowledge base; adopting goal-oriented active probing to search for geometric contours matching the features in the three views, obtaining the geometric primitives and their spatial poses corresponding to each feature; reading the dimension annotations, attaching the dimension values ​​as logical variables to the corresponding geometric primitives, and generating an enhanced structured feature list; constructing an instruction compiler, inputting the enhanced structured feature list, mapping each feature to a feature class calling function in the engineering knowledge base, and synthesizing a Python source code sequence to generate a parameterized 3D model.

The embodiment of the invention discloses a content pushing method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring an initial knowledge graph, wherein the initial knowledge graph comprises a first feature class entity, an application rule class entity and a content class entity which have a relationship in sequence; and respectively injecting the first feature description corresponding to the at least one first feature into the respective corresponding first feature entity in the initial knowledge graph to obtain an injection knowledge graph. In addition, the method further comprises the steps of obtaining application deviation data corresponding to each content entity in the injection knowledge graph based on the injection knowledge graph, and performing corresponding content pushing. Through the fine-grained connection between the entities in the knowledge graph, especially the fine-grained connection between the feature entities and the application rule entities, the applicability of the content under the corresponding feature condition is effectively judged.

Various embodiments of the present disclosure disclose machine-learning based evaluation techniques for detecting feature bias. An evaluation framework is provided that utilizes new evaluation data structures for comprehensibly evaluating feature bias in machine learning models. The evaluation framework includes receiving evaluation dataset for a machine learning model that includes one or more different feature classes of an evaluation feature. The evaluation framework includes generating, using an evaluation function, at least two performance metrics for the machine learning model and generating a unitless dissimilarity metric for the evaluation feature based at least in part on the first performance metric, the second performance metric, and an average between the first and second performance metrics. In this way, the unitless dissimilarity metric is based on a variability associated with the evaluation feature.

This application provides a method for accessing object attributes, an electronic device, and a storage medium. It relates to the field of data processing technology. The method includes: determining object attributes of a target object, where the target object is a feature class instance corresponding to a database table, and the object attributes are used to indicate member variables in the target object; generating a functional interface instance corresponding to the object attributes, where the functional interface instance is used to encapsulate access operations on the object attributes; storing the functional interface instance in a target cache; and, in response to an access operation on the object attributes of the target object, calling the functional interface instance from the target cache to perform the access operation on the object attributes. This application can improve the efficiency of object attribute access.

The application discloses a kind of spatio-temporal attention video behavior identification method based on time adaptation, belong to computer vision in behavior identification technical field.The method is distinguished to the importance of feature in time dimension by time adaptation, and filters out representative typical feature, to guide more attention is allocated on important feature to assist accurate identification.Spatio-temporal attention obtains spatio-temporal information from important feature class and secondary feature class based on energy function, selects the feature with representative information, and then fuses two kinds of features based on spatio-temporal dimension weight to obtain fusion feature, finally obtains video behavior identification result according to fusion feature.Spatio-temporal attention not only does not introduce parameter, but also can directly deduce three-dimensional weight, so it can improve the performance of video behavior identification network.The entire module has the nature of plug and play, can be easily embedded into mainstream 2DCNNs, to form a dedicated video behavior identification network.

The invention relates to the technical field of multi-platform data exchange and data standardization, in particular to a cross-platform geological data exchange method and system. The method comprises the following steps: S1, acquiring original geological data of different platforms; s2, analyzing the original geological data into a unified structured JSON protocol; the structured JSON protocol takes each geological object in the original geological data as a JSON object; s3, performing style mapping processing on the element class of each JSON object, and establishing a dynamic binding relationship between the element class and each platform rendering rule; s4, structured JSON data meeting the cross-platform standard is generated for each JSON object, and data exchange of the geological objects among different platforms is achieved. According to the method, data semantic differences between platforms are eliminated, standard storage and analysis of geological data in a multi-source environment are realized, a standard foundation is laid for cross-platform data exchange, and the problem of analysis errors caused by incompatibility of data formats is effectively solved.