9 results about "Big graph" patented technology

The invention discloses a causal evaluation method for advertisement putting. The method comprises the following steps: acquiring a plurality of data sets of advertisements put on a take-out platform by merchants operating on the take-out platform; inputting the plurality of data sets into a causal graph mining model set to obtain a first candidate causal graph set; filtering the first candidate causal graph set based on an expert knowledge base to obtain a second candidate causal graph set; obtaining the weight of the path according to the relationship between the node and the path of each second candidate causal graph in the second candidate causal graph set, and aggregating the plurality of second candidate causal graphs based on the weight of the path to obtain a causal assessment large graph; and based on the causal evaluation large graph, evaluating the causal relationship between the advertising parameters and the effect indexes of the merchant advertising. According to the method, the accuracy of advertisement effect evaluation can be improved through accurate causal relationship analysis, the advertisement putting efficiency can be improved, and the putting strategy can be optimized.

The application relates to the technical field of engineering simulation, and discloses an industrial structure mechanics simulation prediction system based on an enhanced graph attention network, which comprises a finite element data mapping and enhancement module, a prediction module, an optimization module and a deployment module. The system reconstructs a finite element grid into a graph structure tensor, captures geometric mutation characteristics by using high-dimensional feature projection and adaptive feature recalibration, and transmits deep physical information through dense residual connection. In the training stage, a physical constraint loss function containing attention area weighting and structure smoothness is introduced; in the reasoning stage, a mixed precision mode is adaptively switched according to hardware attributes, and large graph blocking and fusion calculation based on overlapping boundaries are performed. The application effectively solves the contradiction between calculation efficiency and numerical accuracy in complex industrial structure mechanics response prediction, and realizes millisecond-level high-fidelity online simulation.

1. Name of the product in this design: Graphical User Interface for Viewing Activity Calendar on Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photo that best illustrates the design points: Interface change state diagram 1. 5. The rear view, top view, bottom view, left view, and right view are standard design elements and are omitted. 6. Purpose of the graphical user interface: The graphical user interface is used to view and subscribe to activities at different times in a calendar format. In the main view, click the bubble area under the date in the calendar widget to enter the interface change state shown in Figure 1. The top displays the activity calendar for the current month, and the bottom displays the activity content. In the interface change state diagram 1, swipe up on the activity content area to enter the interface change state diagram 2 and switch to the weekly calendar format. Scroll through the activity content area in Interface Change State Diagram 2, and sequentially enter Interface Change State Diagram 3 and Interface Change State Diagram 4 to display activities for future dates. The icons in the calendar area switch left and right according to the activity dates. In the interface change state diagram 4, scroll through the activity content area to enter the interface change state diagram 5, which displays future subscription coupons and other activities. The content in the calendar area switches according to the week of the date. In the interface change state diagram 5, continue scrolling through the activity content area to sequentially enter the interface change state diagrams 6, 7, and 8, displaying activity content with different numbers of images. In the interface change state diagram 8, click the "Remind Me" button in the lower right corner to subscribe to the event, and then enter the interface change state diagram 9. In the interface change state diagram 9, clicking on any image in the activity will lead to the interface change state diagram 10, where the image is displayed in a large format. Users can zoom in, zoom out, switch between images left and right by swiping the screen, or save the image by long-pressing.

The application discloses a method, system, device and medium for building a data visualization large screen based on an improved ResNet algorithm, and the method comprises the following steps: constructing a training data set, training an improved ResNet18 model by using the training data set; removing two full connection layers of the trained improved ResNet18 model to serve as a feature extractor; extracting features of the training data set by using the feature extractor, training an SVM classifier by using the extracted features; cutting a large design graph to obtain a plurality of small graphs, inputting features of the small graphs into the trained SVM classifier after applying the feature extractor to extract the features of the small graphs, classifying the small graphs, matching components from a component library according to the categories of the small graphs, and rendering the components to a large screen according to the information of the small graphs; and configuring and adjusting the components and page attributes of the large screen according to data, display and event requirements of the large screen, so as to obtain a complete data visualization large screen. The application has a higher chart image classification accuracy than existing models, and reduces the design complexity of the large screen through low-code components, thereby avoiding a repeated configuration process.

This invention belongs to the field of privacy computing technology and provides a method for constructing a multi-departmental government knowledge graph and making collaborative decisions with intelligent agents based on asynchronous federated learning. The method includes: CLIP joint embedding feature extraction, cross-departmental collaborative updating, relational reasoning and cross-departmental entity matching, comprehensive judgment and decision-making execution path matching, construction of a domain-level large graph and exception condition library, construction of an RPA rule base, and generation of automated processes. This invention achieves semantic-level fusion of heterogeneous data through seedless entity alignment and multimodal embedding technology, breaking down data silos; improves the rationality of human-machine collaboration through a two-factor threshold judgment mechanism; and enhances the automation level of processes by automatically mapping stable business rules to RPA action sequences.

The present application relates to a method for identifying color-changing standing trees based on phenology and multi-temporal remote sensing images, belonging to the field of remote sensing image processing and forestry information technology, and solving the deficiencies of existing technologies in terms of color-changing standing tree identification accuracy, efficiency, robustness, etc. The method comprises: classifying multi-temporal remote sensing images into different phenological subsets according to phenological periods; constructing a standardized sample library; training a window-level classification model; performing coarse screening with a large step size and then performing fine determination on the target remote sensing image to be detected; calculating the disease probability accumulation value of each pixel point to generate a disease mask matrix; and sequentially performing connected domain analysis and morphological operation on the disease mask matrix to obtain the final disease distribution mask matrix and output. The present application uses window-level samples for training and reasoning, and realizes an automatic identification process from large image discrimination to high-resolution mask generation through multi-step sliding windows and probability accumulation strategies, achieving efficient, accurate and automatic color-changing standing tree identification.

The application provides a power defect image simulation method using heterogeneous computing resource load balancing scheduling, wherein a defect simulation task of a large image is split into defect simulation subtasks of several small images through image segmentation; then a task scheduling simulator is designed according to a cluster state of the heterogeneous computing resources and a defect simulation subtask state, the task scheduling simulator is used to plan task scheduling so that resource consumption is minimum; according to the planned task scheduling, the defect simulation subtasks are distributed to corresponding heterogeneous computing resources, and after image simulation, the defect simulation subtasks are combined to obtain a final defect simulation image. Through the use of the heterogeneous resources to schedule the small image defect simulation subtasks obtained by division, the average completion time of the simulation tasks is minimum, the computing efficiency is improved, and the defect simulation effect of the high-resolution large image of the power grid is ensured.

The application relates to the technical field of data analysis, and provides a method and device for information correlation of offline videos, equipment and a storage medium. The method comprises the following steps: preprocessing single-set videos, obtaining a maximum face area graph and a maximum single-face original graph of each type of face in a key frame; performing global clustering based on the maximum face area graph and the maximum single-face original graph of each type of face extracted from all single-set videos; selecting a global target face-removing large graph and a global target maximum single-face original graph of each type of face from the global clustering result; and calling an external star face recognition service for identity recognition based on the global target face-removing large graph and / or the global target maximum single-face original graph, and associating each recognition object with real information and role information. The application performs identity recognition after hierarchical processing, key frame screening and representative image selection, can reduce the data volume of global processing, and improves the processing speed and accuracy.

A computing system can generate and train a machine-learning model for risk assessment. The machine-learning model can be trained on semi-labelled graph data that may contain one or more isolated nodes generated from a tabularized data set. The computing system can use graph embeddings to compare pairs of nodes of the graph to determine a similarity between each pair of nodes. The similarity may be used to determine whether to create a synthetic edge between the pair of nodes. An additional hyperparameter may be used to tune the number of generated edges based on a desired graph density. The generated graph data may then be used to train a machine-learning model capable of generating a risk indicator for a target entity. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.