1234results about "Other databases browsing/visualisation" patented technology

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention discloses a port container transportation full-process simulation and space planning quantification system and method under random event deduction. The system comprises a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module. The data acquisition module is used for acquiring basic data in port operation; the data processing module is used for processing the basic data; the process simulation module is used for constructing port container transportation process simulation covering the whole process of ship navigation, ship berthing, quay crane loading and unloading, storage yard storage and container truck outward transportation based on the processed basic data, and obtaining a simulation result; the efficiency quantitative index generation module is used for obtaining an efficiency quantitative index according to the container transportation characteristics and the port operation experience; and the efficiency quantification module is used for quantifying the simulation result by utilizing the efficiency quantification index. According to the invention, the current operation efficiency level of the port can be measured comprehensively and objectively.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

The invention discloses a three-dimensional energy carbon data visualization terminal and a use method thereof. The visualization terminal comprises a data acquisition module, a data processing module, a visualization display module, an interaction control module and a communication transmission module. The data acquisition module acquires a voltage signal, a current signal and a carbon emission signal of the park power equipment in real time; the data processing module performs energy-carbon coupling analysis on the original data to generate an analysis result; the visual display module dynamically displays the energy and carbon monitoring data, the electric power parameter waveform change trend, the incidence relation between energy consumption and carbon emission and abnormal event information; the interaction control module receives a user instruction and adjusts display content; and the communication transmission module performs data interaction with a remote management platform. Through multi-module cooperation, real-time acquisition, coupling analysis and three-dimensional dynamic visualization of energy and carbon data are realized, comprehensive energy efficiency and carbon efficiency management support is provided for an industrial park, and the decision efficiency of energy optimization and carbon emission regulation is improved.

The invention relates to the technical field of teaching management, discloses an intelligent teaching system based on a knowledge graph and virtual simulation and a construction method, and aims to effectively solve the difficulty in integration and management of teaching resources and provide personalized teaching experience. The system comprises a knowledge graph construction module which is used for integrating and standardizing teaching resources and constructing a knowledge graph containing knowledge points and association relationships thereof; the virtual simulation teaching module is used for displaying knowledge points in a virtual simulation form by constructing a knowledge presentation model and an interactive learning model, and evaluating learning conditions of students; the intelligent teaching control module is used for constructing a teaching strategy model by adopting a reinforcement learning algorithm and dynamically adjusting a teaching instruction according to the learning progress and the interaction effect of the students; and the execution presentation module is responsible for receiving and executing the teaching instruction and presenting the teaching content in the virtual teaching environment. The system improves the pertinence and effectiveness of teaching, and stimulates the learning interest of students.

The invention relates to the field of data visualization, in particular to a data visualization method and system for an intelligent automobile data recorder of an electric vehicle. The method comprises the following steps: acquiring a real-time video stream of a driving scene based on an intelligent automobile data recorder, performing motion visual jitter elimination compensation and multi-level scene target feature perception, and constructing a multi-level scene target information sequence; performing space-time connection rendering according to the multi-level scene target information sequence, and performing scene evolution smoothing processing so as to construct a dynamic driving scene topology rendering model; vehicle running state parameters are obtained, multi-parameter correlation analysis and vehicle transient response analysis are carried out, and a real-time vehicle state evaluation report is constructed; and performing multi-target constraint calculation based on the dynamic driving scene topology rendering model, and performing optimal vehicle speed prediction to obtain the optimal vehicle speed. According to the invention, through multi-dimensional driving visual rendering, good real-time feedback and behavior early warning are provided for the user.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to the technical field of supply chain management, in particular to a digital visual management system and method applied to a building material supply chain. The method comprises the following steps: acquiring real-time position data, material attribute data and time sequence node data; performing data preprocessing and integration on the real-time position data, the material attribute data and the time sequence node data to obtain standard building material supply chain data; obtaining a construction drawing; basic building materials of the standard building material supply chain data are extracted based on the construction drawing, wherein the basic building materials comprise concrete, reinforcing steel bars and building blocks; building pouring abnormity monitoring is carried out based on the concrete, the steel bars and the building blocks, and pouring abnormity monitoring data is generated. Through multi-source data integration, ternary coupling analysis, scheduling optimization and three-dimensional dynamic visualization, the problems of information island, scheduling conflict and low management efficiency of the building material supply chain are solved, and intelligent, efficient and transparent management of the supply chain is realized.

The invention relates to an optimization method and device for large-scale graph data visual rendering and a storage medium, and the method comprises the steps: obtaining to-be-rendered graph data, constructing an R-tree spatial index according to the to-be-rendered graph data, and constructing a self-adaptive fragmentation model based on an R-tree data structure; monitoring interaction data of a user in real time, performing viewport prediction according to the interaction data, and determining a to-be-loaded sub-graph according to a prediction result and the adaptive fragmentation model; the method comprises the following steps: acquiring system data of client equipment, analyzing the performance of the client equipment according to the system data, and determining a rendering strategy according to a performance analysis result; and performing off-screen rendering on the to-be-loaded sub-graph according to the rendering strategy, and displaying an off-screen rendering result on the client device after detecting that the user completes the corresponding operation. According to the method, the rendering granularity is dynamically adjusted through the adaptive fragmentation model, and the rendering quality and performance consumption are balanced; the user interaction intention is pre-judged through a viewport prediction algorithm, and window smooth switching is achieved in combination with off-screen rendering.

In an embodiment, a method comprises: generating a user interface configured to allow selecting a product from a plurality of products that are customizable and displaying the user interface on a display device of a user computer; receiving, via the user interface, a selection of a particular product, from the plurality of products, that require customization; in response to receiving the selection of the particular product, automatically generating a plurality of customization options available for customizing the particular product; receiving one or more triggers of a plurality of triggers, generated based on customization options selected from the plurality of customization options; automatically generating one or more corresponding customization attribute values for the particular product; automatically generating one or more digital representations of the particular product, and displaying the digital representations of the particular product on the display device of the user computer.

The invention relates to the technical field of Internet of Things perception, in particular to an intelligent storage digital visual management system and method based on the Internet of Things. The method comprises the following steps: acquiring intelligent storage structure data; constructing a storage three-dimensional model according to the intelligent storage structure data; carrying out material carrying simulation based on the storage three-dimensional model to obtain material carrying data; identifying a high-frequency congestion area based on the material handling data; carrying out cargo placement analysis according to the high-frequency congestion area to obtain cargo placement data; identifying a cargo spatial form based on the cargo placement data to obtain cargo spatial form data; carrying out cargo gravity center shift detection according to the cargo spatial form data to obtain cargo gravity center shift data; and according to the cargo gravity center shift data, acquiring a cargo unique label to obtain cargo label data. The goods stacking safety rate and the goods shelf structure operation stability rate in intelligent storage are improved based on the Internet of Things sensing technology.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention belongs to the technical field of public safety, and particularly relates to a map-based focused person management and control large model visualization system and method, and the system comprises a multi-source data collection layer which is used for constructing a multi-modal data set comprising identities, tracks and biological characteristics; the large model analysis layer comprises a space-time trajectory prediction model, a risk integral calculation engine, a group event early warning model and data processing; the visual interaction layer dynamically renders personnel tracks, risk thermodynamic diagrams and police resource distribution based on a three-dimensional map engine, and supports hierarchical early warning display and emergency plan linkage; the monitoring and tracking layer is matched with the tracking and early warning layer, frequently-going / non-frequently-going areas are divided through an improved space-time ST-DBSCAN algorithm, dangerous behaviors of management and control personnel are monitored and tracked, and a differential early warning mechanism is triggered. The multi-modal data fusion technology is combined, real-time loading of ten thousand people-level track points is achieved, data collaborative training of public security organizations in multiple places is supported, and dangerous behaviors of management and control personnel can be monitored and tracked.

The invention discloses a mass data display method, device and equipment based on a browser client side and a medium, relates to the technical field of computers, is applied to a server side and comprises the steps that a data obtaining request sent by the browser client side is obtained; the data acquisition request carries a request time range and a target sampling frequency; obtaining original time sequence data in the request time range from a preset time sequence database, and determining the size of a sampling window based on the data frequency of the original time sequence data and the target sampling frequency; the data frequency is greater than the target sampling frequency; grouping the original time series data based on the size of the sampling window so as to obtain a plurality of data windows, and extracting a target feature point for representing data trend change in each data window; and merging the target feature points of the data windows to obtain a downsampled data sequence, and sending the data sequence to a browser client for display. According to the method and the device, effective visual display of mass data on the browser client can be realized.

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional data visualization supply chain process optimization method and system based on big data, and provides a supply chain dynamic optimization thought based on multi-source heterogeneous data fusion and intelligent decision closed loop. By constructing an end-to-end multi-source heterogeneous data real-time cooperative processing and intelligent decision closed-loop architecture, dynamic integration, intelligent analysis and automatic response and update optimization of full-link data of the supply chain are realized, so that the real-time analysis capability and dynamic adjustment capability of the supply chain are remarkably improved.

The invention discloses a construction method and system of a power distribution network visualization platform based on SG-CIM, and relates to the technical field of construction of power distribution network visualization platforms. According to the construction method of the power distribution network visualization platform based on the SG-CIM, power text information of a power distribution network is obtained and input into a pre-trained semantic recognition model for analysis, an equipment attribute triple and an equipment relation triple of each piece of power equipment are obtained, topology reconstruction processing is carried out on the equipment relation triple, and the power distribution network visualization platform based on the SG-CIM is constructed. According to the method, a power equipment topological structure diagram is generated, graph modeling processing is carried out, a semantic knowledge graph is generated, then quality verification analysis is carried out, and a semantic structure coordination index is obtained. Therefore, the uniformity of semantic expression and topological connection logic of each power device in the power distribution network is guaranteed from the source, and the intelligent analysis capability, the operation visualization capability and the automatic verification capability of the power distribution network structure are improved.

Disclosed herein are computing system, apparatus, article of manufacture, method and / or computer program product embodiments, and / or combinations and sub-combinations and sub-combinations thereof, for generating customized / personalized content browser UIs and browsing experiences. For example, a computing system may be configured to obtain data about one or more user interactions with a content browser user interface (UI). In some cases, the content browser UI displays a plurality of groups of tiles representing different content items. Additionally, the computing system may be configured to identify, based on the data about the one or more user interactions, a first template specifying a layout of the content browser UI and a second template specifying a configuration of one or more groups of tiles of the plurality of groups of tiles. Further, the computing system may be configured to update the content browser UI based on the second template.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and / or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

The present application belongs to the technical field of visualization, and provides a method and system for tracking and visualizing scientific research hotspots, which comprises the following steps: obtaining scientific research literature data in multiple dimensions; performing cluster analysis and heat mining on the scientific research literature data to obtain target scientific research literature data with potential hotspots; tracking and analyzing the heat persistence and heat stability of the target scientific research literature data, and screening to obtain hot scientific research literature data; and performing visual analysis on the hot scientific research literature data to obtain hot visual analysis results. The present application can improve the accuracy of obtaining scientific research hotspots and the visual effect by obtaining scientific research literature data in multiple dimensions, tracking and analyzing the heat persistence and heat stability of target scientific research literature data with potential hotspots, screening to obtain hot scientific research literature data, and finally performing visual analysis.

The invention relates to the field of tuna culture management, and discloses a management method and system for a tuna intelligent pond culture platform, and the method comprises the following steps: collecting the culture associated data of tunas in combination with underwater monitoring equipment, overwater monitoring equipment and water quality monitoring equipment, and constructing a tuna culture evaluation model; carrying out risk assessment on the tuna culture state of the pond based on the tuna culture assessment model; and performing water quality parameter adjustment on the target pond area with the culture risk, and performing visual data display processing through the tuna intelligent pond culture platform. According to the invention, a data monitoring platform can be provided for intelligent culture of tunas, help can be provided for tuna culture more efficiently, the purpose of system automation is achieved, and meanwhile, the harm in the tuna culture process is reduced.

The invention discloses an automatic settlement path generation method fusing a knowledge graph and a tax rule, and relates to the technical field of financial science and technology and intelligent finance and tax compliance, and the method comprises the steps: S1, obtaining initial decision basis data and execution process details from a settlement path through a block chain network, employing a Hash chain structure to link each link record, s2, according to the obtained path chain, a graph database is adopted to store dynamic association attributes, and the dependency relationship of each node in the path is determined through knowledge graph reasoning, and S3, if an information fault is detected in the path chain, the matching degree of an execution result and a decision basis is compared through a timestamp verification mechanism, and if the information fault is detected in the path chain, the decision basis is determined. Judging a deviation position and marking a deviation point; according to the automatic settlement path generation method fusing the knowledge graph and the tax rules, the security and reliability of financial transactions and the tax management efficiency are effectively improved.

The invention relates to the technical field of circuit board detection, and provides a circuit board damage detection optimization method and system, and the method comprises the following steps: S1, synchronously collecting a contact resistance signal, a probe pressure signal and environment data of a circuit board test point through a probe array, and carrying out the optimization processing of the collected data parameters; s2, establishing a contact impedance dynamic model, depicting the dynamic change of the contact impedance of the circuit board, extracting a characteristic signal for analysis, and mining the mode characteristics of an abnormal signal; and S3, fusing the abnormal multi-source evidences of the circuit board, processing the uncertainty and relevance among the evidences, and reversely deducing the positions and reasons of the potential defects on the circuit board. Through a multi-dimensional verification and error self-correction mechanism, the error between a defect solution and a real defect is quantified and reversely deduced by using posterior error estimation, so that the reliability and consistency of a detection result are improved, a reliable result support is provided for engineering application of circuit board damage detection, and meanwhile, the dependence on detection experience of detection personnel is avoided.

The invention belongs to the technical field of engine fault diagnosis, and provides an engine test fault diagnosis method, system and device and a storage medium, and the method comprises the steps: collecting and analyzing engine data, and obtaining a fault data set; converting the fault data set into a target format; dynamically updating the target knowledge graph based on the fault data set in the target format; and inputting the query elements into the target knowledge graph, obtaining fault data of the corresponding engine, and obtaining a fault reason. According to the method, the dynamic target knowledge graph is adopted, after fault detection, the target knowledge graph can be updated according to the data of the engine, the node relation can be automatically expanded in real time, manual intervention is reduced, and more accurate diagnosis is provided for subsequent fault query; according to the method, nodes and fault data can be visualized during query, and an interactive fault diagnosis and learning interface can help a user to quickly position a fault source and accumulate fault detection experience.

The invention relates to an artificial intelligence technology, can be applied to the technical field of finance, and discloses an artificial intelligence-based abnormal data identification method, device, equipment and medium, and the method comprises the steps: collecting investment data in real time, and inputting the investment data into an investment data semantic large model; performing anomaly identification on the investment data based on the investment data semantic large model to generate an abnormal data identification result; obtaining abnormal investment data in the abnormal data identification result, analyzing an abnormal condition of the abnormal investment data based on a graph neural network algorithm, and generating an abnormal reason and abnormal positioning information; and obtaining a repair suggestion based on the exception reason, and pushing the exception reason, the exception positioning information and the repair suggestion to a visual interface. According to the invention, investment data is scanned in real time through the AI algorithm, abnormal values are rapidly identified, and human errors are reduced; abnormal reasons can be automatically positioned, repair suggestions can be provided, real-time monitoring is achieved, and abnormal data recognition efficiency is improved.

The invention discloses a teaching optimization method, system and device based on knowledge space and a medium. The method specifically comprises the steps that a secondary node platform is analyzed based on national industrial internet identification, official teaching materials are analyzed to generate knowledge point entities with identification codes, and a relation network is established; based on the relational network, collecting a multi-platform question bank problem solving step, performing authentication in combination with an identification code matching mechanism, synchronizing to a basic knowledge space after authentication, marking as an extended knowledge point library, and calculating a difficulty threshold value of a knowledge point association combination in combination with wrong question statistics; and on the basis of the relational network, cross-platform problem solving steps of the learner are captured in real time, authentication is carried out in combination with an identification code matching mechanism, if authentication is not passed, a preposed knowledge point unmastered state is marked, and a learner problem solving knowledge space with a state mark is generated. According to the invention, comprehensive analysis of the knowledge point mastering condition of the learner, difficult point positioning and personalized teaching optimization suggestion generation are realized, and the teaching quality and the learning effect are improved.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The present application provides a method for generating an environmental model, a display method, an apparatus, an electronic device, and a storage medium, and relates to the field of Internet of Things technology. The generation method includes: displaying a spatial layout diagram corresponding to a target scene; the spatial layout diagram is generated based on the relationship between the trajectory of a first target object in the target scene and each space in the target scene; identifying each second target object deployed in the target scene, and obtaining the position of each identified second target object in the target scene; generating an environmental model of the target scene based on the position of each second target object in the target scene and the spatial layout diagram corresponding to the target scene, for describing the status of the first target object and / or the second target object in the target scene. The present application associates the second target object with the spatial layout diagram, and provides a device monitoring method based on spatial positioning, which can intuitively monitor various equipment and facilities in the target scene, thereby helping to improve the effectiveness of the device control service.

A computing device displays, in a graphical user interface, a canvas region that includes one or more scenes. The one or more scenes include a first scene having a first visualization card. The device displays, in the graphical user interface, a recommendation region that includes one or more data visualizations. Each of the data visualizations corresponds to a respective metric of a data source. The respective metric has a respective temporal attribute. The device also displays, in the graphical user interface, a scene summary that includes, for each scene, a respective count of visualization cards corresponding to the scene, a respective count of text cards corresponding to the scene, a respective count of unique metrics corresponding to the scene, and a respective cumulative temporal span corresponding to the scene.