31 results about "Relational encoding" patented technology

The invention discloses a data follow-up relationship analysis method, which is applied to an analysis server in a data analysis network. The data analysis network also includes a plurality of data acquisition devices, and all data acquisition devices in the data analysis network are bound in pairs. And generate a unique binding relationship code, obtain the binding relationship code corresponding to the data to be analyzed according to the data type and space following conditions of the data to be analyzed, group the data to be analyzed according to the binding relationship code corresponding to the data to be analyzed, and judge Whether the data to be analyzed in the group corresponding to each binding relationship code has time following, and output the judgment result of the data to be analyzed, which reduces the amount of calculation when analyzing the data following relationship, and improves the efficiency of data analysis.

The invention relates to a program source code coding method based on a code attribute tensor. The program source code coding method comprises the steps of generating a code attribute graph for a program source code; creating a symbol table, a node table and a code attribute tensor, and initializing; encoding the operation relationship between the data type of the AST node and the AST node into a code attribute tensor; encoding the father-child relationship between the AST nodes into a code attribute tensor; and encoding the adjacency relationship between the CFG nodes into the code attribute tensor, and outputting a final code attribute tensor. The invention provides a code attribute tensor concept and an algorithm for encoding a program source code into a code attribute tensor in order to solve the problem that an existing program source code encoding mode cannot fully embody program semantic characteristics. According to the method, under the condition that semantic information is prevented from being lost, program source codes are encoded into tensor-form data to serve as input of a machine learning model, and support is provided for subsequent program static analysis work.

The invention discloses an attention mechanism relationship comparison network model method, and constructs an attention relationship comparison network model for small-sample learning under a small amount of labeled sample data. Based on the relational network architecture, the model is divided into feature encoding, feature combination, and relational encoding. The function of the feature encoding module is to extract image feature information. The feature combination part is to combine the extracted query image feature information with the training image features of each group. The information is recombined to form a new combined feature map. The relationship encoding module performs nonlinear metric learning of the network. By introducing the attention mechanism and spectral normalization method into the end-to-end deep convolutional neural network model, the model has higher classification accuracy in the case of small sample learning. , and improve the stability of the final training results of the model, and improve the image classification accuracy of the existing model in small-sample learning.

The invention relates to a BIM model-based engineering budget estimation method, which creates a one-to-one mapping relationship between structure tree nodes and components, uses nodes to quickly find the quantity and estimated budget information of each project under the node or component, and creates a method that is separated from the model The project budget estimate database can quickly realize project quantity statistics and budget estimate preparation. The invention separates the BIM model from the project budget database by adding LID coding attributes to the components, and uses LID to associate nodes with components, thereby creating a one-to-one correspondence between each node and component. The coding structure is simple, and it is convenient to find and locate components And all project budget information under the component, using IFD code, it is convenient for computer to read and identify, and the efficiency is higher.

The present invention discloses a multi-event natural language description algorithm in video oriented to event relationship encoding, which includes the following steps: S1. Using a three-dimensional convolutional neural network to extract depth features from a given video sequence to obtain several depth feature vectors , forming a deep feature sequence; S2, based on the deep feature sequence, using a recurrent neural network as a time series analysis method to calculate the proposed start and end intervals of events in the video sequence; S3, selecting the events to be described in the video sequence Describe the event, and re-encode the corresponding subsequence of the event to be described in the deep feature sequence according to the proposed start and end interval of the event to obtain the descriptor of the event to be described; S4. Using attention-based The LSTM adaptive decoder of the model decodes the descriptor to obtain a natural language used to describe the event to be described.

The invention discloses an image paragraph description method based on relational coding and hierarchical attention mechanism. The method model is composed of a relational coding module and a hierarchical attention decoding module. The relational encoding module captures the encoded spatial relational information and semantic relational information through two encoders, where the prior knowledge of the semantical relation is learned by training a supervised semantic classifier during semantic relational encoding. The hierarchical attention of the hierarchical attention decoding module uses hierarchical attention with relational gates and visual gates to dynamically fuse relational information and object region features. The relational gates are used to switch between spatial relational information and semantic relational information. To decide whether to use visual information for embedding, the model adopts a strategy from coarse-grained regions to fine-grained spatial and semantic relations to fuse visual information during paragraph generation. Extensive experiments on the Stanford paragraph description dataset show that the method of the present invention is significantly better than the existing methods in multiple evaluation indicators in the field.

The embodiment of the invention provides an inquiry-based target positioning method and device and electronic equipment, and the method comprises the steps: carrying out the sub-region division of a to-be-inquired region, and building a mapping relation coding table through employing an incomplete randomness rule based on the sub-region division; Determining a question of the inquiry, and based onthe question of the inquiry and the mapping relation encoding table, asking for a sensor; And decoding a result output by the sensor based on the question, and determining a sub-interval corresponding to a decoded code word as an area where a to-be-positioned target is located based on the mapping relation coding table. According to the embodiment of the invention, the method can better adapt tothe target positioning under the sparse sensor distribution, and effectively reduces the cost of target positioning.