55 results about "Big graph" patented technology

The invention discloses an intelligent bone marrow megakaryocyte morphology detection method which comprises the following steps: S1, data preparation: collecting and preprocessing a digital large map of a bone marrow smear, labeling megakaryocytes, and establishing a labeled sample; s2, generating and sorting a sample: extracting a small graph sample by taking megakaryocyte as a center; s3, constructing a deep learning model: constructing and training a deep convolutional neural network, and performing model optimization and performance improvement by using the generated small image sample set; s4, large image detection and reasoning: calling the trained model for reasoning by adopting a sliding window mechanism, and fusing detection results of a plurality of small windows back to an original large image through confidence weighting and a non-maximum suppression strategy; and S5, target cell segmentation: carrying out target segmentation on the detected megakaryocyte, and introducing a pyramid structure for cells with different sizes to obtain an accurate segmentation mask of each target cell. According to the method, the megakaryocyte detection and segmentation precision is improved through large image labeling, small image training and a sliding window reasoning strategy.

Provided are a distributed parallel computing based graph computing method, a distributed parallel computing system and a computer readable medium. A graph computing method comprises: obtaining data of a graph to be computed, the graph comprising a plurality of vertices and edges; storing the graph according to a compressed sparse row format, and storing the graph in a sparse matrix form, wherein all adjacent edges having a same vertex are continuously stored, and an offset array is used to support an index function thereof; and for subgraphs in which the degrees of both source vertices and target vertices are greater than a predetermined threshold, in forward graphs, performing segmentation according to target vertices, i.e., the column of a matrix, and in reverse graphs, performing segmentation according to source vertices, i.e., the row of the matrix, and further dividing the subgraph into a plurality of segmented subgraph (SSG) components, so that a target vertex in each SSG is limited within a preset range. According to the SSG method of the embodiment for a high-degree vertex, the conventional problem that the space-time locality of accessing data of an adjacent vertex is poor due to a large vertex range of a single large graph is avoided or relieved.

The present invention discloses a harmonic radar target recognition method based on subgraph fusion, which relates to the technical fields of data recognition and data representation, and includes the following steps: S1. The harmonic radar emits detection signals in three bands of L, S, and K, respectively performs frequency sweep irradiation on each type of non-linear electronic target, and collects the reflection signals corresponding to the three bands, and obtains a reflection signal set for each type of non-linear electronic target; preprocess each reflection signal set to obtain a characteristic curve; S2. Convert each characteristic curve into a characteristic subgraph, obtain a regularized characteristic subgraph through a graph convolutional network, and then fuse the regularized characteristic subgraphs in the form of subgraph fusion to generate a multi-head large graph; S3. Input the multi-head large graph into a graph neural network, obtain the embedded representation of the multi-head large graph, and splice the embedded representations of the multi-head large graph; S4. Input the spliced embedded representation of the multi-head large graph into a fully connected layer, and output the target category. The present invention also discloses an electronic device and a computer-readable storage medium.

The invention discloses a hyperspectral image classification method and device based on a subgraph adaptive neural network. The method comprises the following steps: S1, constructing a paired probability graph structure based on an SSAPGCN method; s2, reading a feature index and generating a feature matrix, performing PCA dimension reduction on the input data, and performing data preprocessing; s3, a global graph structure S is obtained based on dimension reduction preprocessing, a Metis graph segmentation algorithm is called for graph segmentation, and the global probability graph structure is divided into a plurality of sub-graphs; s4, calculating an intra-class distance intra (C) as a self-adaptive feedback threshold value, determining an optimal image convolution layer number k according to sub-image spectral difference, and avoiding over-smoothing or under-smoothing; and S5, training and testing the data by using the optimal image convolution layer number k to complete hyperspectral image classification. The method is mainly used for processing large image classification, the calculation cost can be reduced, spectrum and spatial features are fully mined, and the classification accuracy and efficiency are improved.

1. Name of the product in this design: Graphical User Interface (manufacturing) for Product Display and Production of Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: This graphical user interface is a user interaction interface for product display and product creation. 6. Human-computer interaction method of graphical user interface: The main view is the initial interface when entering the mini program. Slide the main view from left to right to display interface state diagram 1. After the slide ends, a new interface will appear, displaying interface state diagram 2. Slide from left to right in interface state diagram 2 to display interface state diagram 3. After the slide ends, a new interface will appear, displaying interface state diagram 4. Slide from left to right in interface state diagram 4 to display interface state diagram 5. After the slide ends, a new interface will appear, displaying interface state diagram 6. Click "Create" at the bottom of interface state diagram 6 to enter a new interface, displaying interface state diagram 7. Click any product in interface state diagram 7 to enter a new interface, displaying interface state diagram 8, where you can view a larger image of the product.

The invention relates to the technical field of spacecraft design, in particular to an aircraft information flow analysis, recheck and recalculation system, which comprises an information large graph design module used for automatically extracting design data such as an interface data list and a cable network contact table, generating a static information large graph through data check and analysis, and supporting signal management, layered display and export; the model library construction module is used for constructing a hierarchical information professional model library based on a Modelica language and covering multi-level models from a basic library, a universal library to a model customization library; and the information flow simulation verification analysis module is used for mapping the static information large graph into a simulation model, carrying out dynamic simulation verification on a system network, a communication network and a load network, and realizing multi-dimensional visual display of a result. According to the method, the information system design is rechecked and recalculated from static state to dynamic state and from manual operation to automatic operation, and the design efficiency, accuracy and verification sufficiency are effectively improved.

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.

A method is disclosed. The method includes generating a graph with nodes and edges, inputting the node data for the nodes into a first scoring algorithm to obtain node scores, and inputting the edge data into a second scoring algorithm to obtain edge scores. The method also includes forming one or more subgraphs from the graph. Each subgraph is formed by: determining an initial node with a node score that exceeds a threshold, determining an initial set of edges connected to the initial node, determining a set of nodes connected to the initial set of edges, and determining additional edges and additional nodes extending from the set of nodes, until a predetermined number of the additional nodes and edges have edge scores and node scores that do not meet the threshold, thereby forming the subgraph.

The invention discloses an improved mosaic data enhancement method, computer equipment and a storage medium. The method comprises the following steps: creating a background large image, and splicing a plurality of original images containing a target frame at the central point of the background large image to obtain a spliced large image; creating a window with the same size as the training image, and sliding on the spliced large image to obtain a plurality of candidate areas; calculating the score of each candidate region based on the score weight corresponding to the category and the area of each target frame in each candidate region; and performing soft sampling on the plurality of candidate areas with the highest scores, and cutting the optimal candidate area to generate a training image. Through the above mode, the category of the target frame can be considered as the score weight, the sampling probability of the tail category is improved, and the recognition performance in a long-tail scene is improved; and the area of the target frame is considered as a score weight, the integrity rate and the size of the target frame in the candidate region are evaluated, the contribution of an incomplete sample is inhibited, and the quality and the robustness of a training image are improved.

The invention relates to a charging station demand prediction method, terminal equipment and a storage medium. The method comprises the following steps: collecting charging station data, POI data and dynamic data in a to-be-researched area; taking the charging station as a center, selecting three areas with different sizes, and constructing corresponding sub-graphs based on POIs contained in the areas; selecting one sub-graph from the three sub-graphs according to the current dynamic data pair, and adjusting the edge of each node in combination with the road congestion condition; obtaining embedded features of the sub-graphs and taking the embedded features as spatial features of the corresponding charging stations; constructing a large graph corresponding to the to-be-studied area by combining the spatial features of all the charging stations; taking the spatial characteristics of the charging stations and the charging times of the previous day as the attributes of the nodes corresponding to the charging stations; and inputting the large graph into the STGCN to obtain the predicted charging times of each charging station in the next day. According to the invention, the accuracy and reliability of charging station demand prediction are improved.

The present invention discloses a method and system for detecting the opening and closing state of a four-corner lock of a motor vehicle. The method comprises: collecting an image of a four-corner lock of a motor vehicle and dividing it into a plurality of sub-images; determining the position of a four-corner lock area in each sub-image; detecting the position of each four-corner lock opening and closing state mark and the direction of the four-corner lock; judging the switch status of the four-corner lock according to the position of the four-corner lock opening and closing state mark and the direction of the four-corner lock, if the direction of the four-corner lock is consistent with the open state mark, it indicates that the four-corner lock is open, and if the direction of the four-corner lock is consistent with the closed state mark, it indicates that the four-corner lock is closed. High-resolution two-dimensional image data of the four-corner lock is obtained by a non-contact optical imaging method, and the position of the four-corner lock area is extracted from the large image of the entire carriage by a deep learning positioning algorithm, and then the direction of the four-corner lock and the open and close characters are extracted by a deep learning algorithm in the sub-image, and the opening and closing state of the four-corner lock is judged by determining the relative position relationship between the direction of the four-corner lock and the character switch.

1. The name of the design product: software personal center graphical user interface for electronic device. 2. The use of the design product: for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the graphical user interface is used for users to view and edit account information. 6. The human-computer interaction mode of the graphical user interface: the front view is the main interface of the personal center, clicking the "log out" option button in the upper right corner of the front view switches the interface to the interface change state diagram 1; selecting the avatar area after clicking the "cancel" option button in the interface change state diagram 1 pop-up window switches the interface to the interface change state diagram 2; clicking the "view large image" option button in the avatar pop-up window of the interface change state diagram 2 with the right mouse button switches the interface to the interface change state diagram 3; clicking the "username" text box after clicking the close icon in the upper right corner of the interface change state diagram 3 pop-up window switches the interface to the interface change state diagram 4; clicking the "save" button on the right side of the interface change state diagram 4 and then clicking the "last 7 days" drop-down menu icon on the right side of the data panel switches the interface to the interface change state diagram 5. 7. Other circumstances that need to be explained: the single gray filled area in the interface is the avatar content.

The invention discloses a data warehouse system based on a graph database and a construction method, the system comprises a graph data storage layer and a platform service layer, the graph data storage layer comprises a physical large graph storage module and a business sub-graph storage module; the physical large graph storage module adopts a distributed graph storage engine and is responsible for storing enterprise core data elements and association relationships thereof; a graph database is adopted as a bottom storage facility; the service sub-graph storage module provides an independent sub-graph storage space for each service application based on the data of the physical large graph storage module; dynamic graph mode expansion is supported, business departments are allowed to add specific attributes and relationships, and customized business sub-graphs are formed; the platform service layer extracts data from each business system through a high-performance ETL engine provided by a graph database, and stores the data to the physical large graph storage module after data governance service cleaning and conversion; the method has the beneficial effect that the flexibility and adaptability of business application are remarkably improved.

The embodiment of the invention discloses an image rendering method for structural, hydrodynamic and thermal simulation scenes. The method comprises the following steps: acquiring a plurality of pictures in a structure, fluid dynamics and thermal simulation scene, splicing the pictures into a large picture through a puzzle algorithm, and recording texture coordinates of each picture corresponding to the whole large picture; updating vertex coordinates and texture coordinate data of each original pit into vertex coordinates and texture coordinates of the corresponding whole large image, and storing the vertex coordinates and the texture coordinates into a CPU memory; according to the method, all pit position data are combined, then a CPU uploads stored coordinate data of vertexes and textures of all pit positions to a GPU at a time, DrawCall is executed once, drawing and rendering of all pit positions are completed, texture pictures of objects made of different materials can be assembled into a large texture picture, and the texture pictures are displayed on the GPU. And each pit is drawn by using one sub-region of the large texture picture, so that each pit has the same texture data, and the requirement of merging into a batch is met.

The invention provides a constellation satellite grounding large graph automatic generation and impedance automatic labeling method and system, and the method comprises the steps: automatically recognizing circuit elements and a connection relation thereof through software, converting the circuit elements into a format compatible with simulation software, and carrying out the preliminary current backflow analysis and detailed current density and heat distribution simulation. Impedance characteristics are measured through a network analyzer, an ideal impedance matching network is calculated, environmental factors are added, and the identified impedance matching network and the calculated impedance matching network are compared and adjusted. And finally, converting the 2D grounding large graph into a 3D grounding structure, and designing a multi-layer and multi-dimensional grounding network through three-dimensional printing and material parameter simulation. According to the method, the satellite grounding large graph can be automatically drawn through software, the comparison impedance is automatically marked, optimization is carried out through simulation data, and the drawing efficiency and reliability can be obviously improved.

The application provides a satellite power supply large map automatic generation method and system, comprising: standardizing satellite power supply large map elements according to power supply large map content drawing requirements; establishing a mapping relationship between the digital model and the power supply large map elements; establishing a digital model of the satellite power supply large map according to the power supply large map design specification standard; automatically identifying each power supply large map element from the interface data sheet according to the naming rules of each power supply large map element; and automatically drawing and generating the satellite power supply large map according to the mapping relationship between the digital model and each power supply large map element. The application realizes digital conversion from the interface data sheet to the graphic expression by establishing an automatic mapping mechanism between the satellite power supply large map digital model and the elements, and ensures the high standardization and professional readability of the drawing in the symbol, line type and spatial layout by algorithm logic enforcement design specification.

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.

According to the phishing account detection model training method, the phishing account detection method and the phishing account detection device, a second-order transaction network is independently constructed for each account, a local transaction structure of each account is completely reserved, and the bottleneck of feature dilution and excessive smoothness caused by traditional global large graph training is broken through; address similarity features are introduced, high-frequency transaction object tail number similarity deliberately constructed by an attacker is accurately captured, and the visual confusion type fishing behavior is effectively recognized. A random walk restart algorithm is adopted to generate double local subgraphs, center node self-pairing is used as a positive sample, a cross-subgraph non-center node is used as a negative sample, node-level comparison loss is constructed, and the discrimination capability of a model on phishing nodes and normal neighbors is enhanced; through joint classification loss end-to-end training, the graph neural network can be incrementally updated without re-training a global graph, and lightweight deployment is realized. According to the method, in a block chain transaction scene with extremely unbalanced data, the risk of misjudgment of the phishing account can be remarkably reduced, and high sensitivity and real-time performance are both achieved.

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.

1. The name of the product of this design: Intelligent system quotation graphical user interface for electronic equipment. 2. Purpose of the product of this design: an electronic device. 3. The design highlights of this appearance design product are the graphical user interface displayed on the screen. 4. The picture or photo that best illustrates the key points of the design: main view. 5. The product carrier is of conventional design, and the rear view, left view, right view, top view and bottom view of the product carrier are omitted. 6.Purpose of the graphical user interface: It is used for users to complete configuration quotation and order online. 7. Human-computer interaction mode of graphical user interface: The main view is the full interface; Interface change state diagram 1 is the main interface introduction page, by moving the cursor to the main view menu bar, the menu bar is displayed as black text on a white background to enter; Interface change state diagram 2 is the main interface introduction page, by clicking the interface change state diagram 1 Enter through the "Products and Services" drop-down window; Interface Change Status Figure 3 is the list page of intelligent AI quotations, which can be entered by clicking the "Yisu Intelligent AI Quotations" button on the right side of Interface Change Status Figure 2; Interface Change Status Figure 4 is the list page of intelligent AI quotations, which can be entered by clicking "New Folder" on the left side of Interface Change Status Figure 3, and the left column enters the folder creation state. After entering the name, the folder creation is completed, and a prompt is prompted to enter successfully; Interface Change Status Figure 5 is the upload page of the intelligent AI quotation, which can be entered by clicking the current folder on the left side of Interface Change Status Figure 4, adding subfolders, and finally entering the third level; Interface Change Status Figure 6 is the page in the parsing process, which can be entered by clicking the "Click to Upload" icon in Interface Change Status Figure 5; Interface Change Status Figure 7 is the list display page, which can be entered by clicking the interface change status figure 6 after the parsing is completed; Interface Change Status Figure 8 is the detailed large picture display page, which can be entered by clicking the upper right corner of Interface Change Status Figure 7 The "icon display button" in the corner is exchanged for the picture detail list page to enter; interface change state Figure 9 is the processing information configuration page, which is entered by clicking "Set parameters" in the lower right corner of the third picture of interface change state Figure 8; interface change state Figure 10 is the processing information configuration page, by clicking the "material selection box" on the right side of interface change state Figure 9 to pop up the material options, click the required material options to enter; interface change state Figure 11 is the processing information configuration page, by clicking the "surface treatment selection box" on the right side of interface change state Figure 10 to pop up the surface treatment options, click the required surface treatment options to enter; interface change state Figure 12 is the processing information configuration page, by clicking the "quantity input box" on the right side of interface change state Figure 11 to enter the required quantity, complete the quantity modification to enter; interface change state Figure 13 is the processing information configuration page, by clicking the interface change state Figure 12 and clicking "Generate Price" to enter; interface change state Figure 14 is the successful quotation page completed.

The invention discloses a method for realizing NMS (Network Management System) calculation in large graph slice reasoning post-processing based on Transform, and in order to realize NMS filtering operation, the method comprises the following steps: inputting coordinates, categories and confidence of a target frame detected by a deep learning network as semantic information into a Transform model, and learning to obtain filtered coordinates and other information; secondly, in order to improve the model reasoning speed, the model structure of transformer is simplified; therefore, end-to-end reasoning of the target detection network is realized in a real sense, and the process of post-processing of the model on a CPU is omitted.

The invention relates to a test case management method and device, electronic equipment and a storage medium. The test case management method comprises the following steps: determining whether to-be-tested functions of the business large graph exist in tested system functions corresponding to a test requirement, and positioning a function node; obtaining a function node of a stock function corresponding to the function of the tested system, obtaining an existing test case ID associated with the function node ID, searching the existing test case associated with the function of the tested system, determining whether the test case needs to be updated, and if the test case does not need to be updated, judging whether the test case needs to be newly added; and after newly adding the test cases, selecting the test cases corresponding to the functions and the associated functions from the business large graph according to the tested functions, and creating a task plan. According to the test case management method, the associated test case can be maintained according to the tested function, generation of repeated cases is reduced, accuracy and effectiveness of the test case are guaranteed, and research and development test efficiency is improved.

The application relates to a dense subgraph-based graph compression method and system, and belongs to the technical field of computer software. The application reorders and reencodes nodes by using dense subgraphs, so that node ordering is not performed on a large graph, time for node ordering is reduced, and redundancy consumption of each node in the memory is reduced; each node in a K-edge-connected subgraph is re-encoded, a new edge storage representation is constructed, and space occupied by nodes with high occurrence frequencies in the graph is reduced; for a sparse space of the graph, a compressed adjacency list is used to reduce storage consumption, and a two-level index is used to improve the search efficiency of node neighbors. The application is simple and easy to use, reduces the gap distance between nodes, and effectively reduces the space required for storing the graph in the memory.

In order to solve the problems that in the prior art, a social network and a knowledge graph have inherent contradictions in the aspects of dynamics, heterogeneity, time sequence and multi-source data fusion, and real-time modeling and analysis of risk knowledge flow in a financial scene cannot be met, the invention discloses a social knowledge large graph construction method facing financial risk knowledge flow. According to the method, multi-source data such as financial markets, financial news and financial platform user social relations can be collected, point sets are extracted, edge sets and three-party hyperedge sets related to enterprises, social users and financial news are constructed, and after an initial social knowledge large graph is formed through integration, a graph structure is dynamically updated based on time range changes; according to the method, deep fusion of social relations and financial field knowledge is realized, propagation paths and intensity changes of financial risk knowledge along with time are accurately captured, the problem of multi-entity association analysis sparsity of a traditional model is solved, and financial risk monitoring and early warning requirements are met.

This invention provides a method and system for subgraph matching in large graphs based on multiple GPUs. The method includes dividing a large graph into subgraphs to obtain several subgraphs, including intra-partition subgraphs and cross-partition subgraphs; performing sequential queries on the intra-partition subgraphs to obtain a matching order, and determining a first matching strategy for the intra-partition subgraphs based on the matching order; identifying the hop count of the cross-partition subgraphs and determining a second matching strategy for the cross-partition subgraphs based on the hop count; and integrating the first and second matching strategies through GPUs to complete the matching of the intra-partition subgraphs and the cross-partition subgraphs. This invention can process the subgraph matching problem in parallel on multiple GPUs, overcoming the limitation of graph size on GPU subgraph matching in large graphs.

The invention provides a perspective line accurate concentric plotter, which relates to the field of plotters and is characterized by comprising an L-shaped ruler body, a sliding ruler and a knob. The method has the advantages that the design principle is visual, operation is convenient and fast, a complex small graph amplification process is not needed, dependence on a limited fixed template is also eliminated, and drawing can be directly conducted on a target drawing; through the precise arc track and the sliding ruler structure pointing to the circle center, it is ensured that all perspective lines can be precisely intersected at the same point, and high-precision drawing is achieved; meanwhile, the device is high in flexibility, is not restrained by a preset angle, can adapt to any visual angle and vanishing point position, and greatly improves the freedom degree and efficiency of hand drawing creation; besides, the tool is wide in application range, supports drawings of different specifications, is convenient for artists to directly draw full-size perspective large drawings and add details, and has good practicability and expansibility.

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.

The present invention discloses a method for repairing content truncation during the conversion of a web page to a PDF. The method includes obtaining a basic data set of the DOM structure tree for the web page to be processed; using browser screenshot technology to perform full-screen screenshot processing on the web page to be processed, obtaining multiple web page screenshot images; merging all the web page screenshot images in order from top to bottom to form a large image, and determining the height of the large image based on the sum of the heights of all the web page screenshot images; performing DOM element definition analysis on the large image through the generated DOM structure tree data set to form a first truncation processing rule; truncating the large image within the A4 size according to the first truncation processing rule to form a plurality of segmented image data sets; performing PDF conversion on the generated image data sets to form a complete PDF. The present invention realizes the repair of the element truncation phenomenon during the conversion of a web page to a PDF based on the combination of html structured data and image processing technology.

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.

1. The name of the design product: graphical user interface for personal data saving and management of electronic equipment. 2. The use of the design product: the design product is used in the operation and display of the interface of electronic equipment. 3. The design points of the design product: the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: information display and human-computer interaction for users to save and manage personal data. 6. The human-computer interaction mode of the graphical user interface: the front view displays the data saved by the user, the "go to save" button is clicked, multiple data are saved, the interface change state diagram 1 is displayed, the "save more" button is clicked, the interface change state diagram 2 is jumped to, the first item "data name" is clicked, the interface change state diagram 3 is jumped to, the "xx system" is clicked, the interface change state diagram 4 is jumped to, when there is a result, the interface change state diagram 5 is jumped to, the "save to digital business card" is clicked, the interface change state diagram 6 is jumped to, the "click to view large image" is clicked, and the interface change state diagram 7 is jumped to.