12 results about "Web visualization" patented technology

The application relates to a network visualization method based on hierarchical layout and a clustering algorithm, and belongs to the field of data visualization.The method comprises the following steps: calling an API interface to obtain circuit data and processing the circuit data, using a dynamic clustering algorithm to perform clustering processing on the data, layering the data processed by the precondition clustering algorithm, each cluster serving as a layer, determining the sequence of each layer according to the structure of the clustering result, mapping the clustering result to hierarchical levels to form node sets of different layers, optimizing the node sequence of each layer to reduce the crossing of edges, calculating the coordinates of each node according to the layering and crossing reduction result, performing drawing according to the calculated node coordinates to convert the processed data into a topology diagram, and acquiring alarm data in real time, performing correlation analysis on the alarm data, and identifying potential fault causes.The method can efficiently process various complex network data and realize intuitive display and dynamic analysis of network structures.

The invention discloses a multilevel eigenvalue layered rendering method for massive time series data of the Internet of Things. The method comprises the following steps: a back end predefines multi-stage time granularity, regularly calculates the maximum value and the minimum value of original data in each time window, and persistently stores the maximum value and the minimum value as a multi-layer characteristic value data set; the front end intelligently matches the optimal hierarchy according to the current view time span and requests the feature value of the hierarchy; and the front end draws a vertical line segment or an envelope line for rendering by using the maximum value and the minimum value of each window. And during user interaction, data hierarchies are dynamically switched to realize smooth drilling. Through the core technology of extreme value pre-calculation, on-demand matching and envelope line rendering, the fluctuation range and key event characteristics of data are completely reserved while front-end single-time rendering data points are strictly controlled within thousand levels, high-performance and high-fidelity Web visualization of ten-billion-level time series data is achieved, and the real-time performance of the system is improved. The technical problem that performance, precision and interactive experience cannot be achieved at the same time in a traditional scheme is solved.

The application relates to the technical field of fault prediction and health management, and particularly discloses a business-level network visual monitoring method for fault prediction and health management, which restores the round trip process of a business session through a bypass probe, splits network access time consumption into three parts of client transmission, server transmission and application processing, and identifies an abnormal delay position by using a historical baseline. For an abnormal session, instead of directly taking the CPU rise collected during diagnosis as fault evidence, a diagnosis disturbance instruction with calibrated resource consumption is first sent, and then the processor utilization deviation amplitude and recovery time length of the target device are observed to judge the pressure margin. Meanwhile, the system determines the fault direction in combination with indexes such as retransmission, zero window, packet loss and same-type business delay, outputs a unique root cause and confidence, and is visually displayed in a business topology.

The application discloses a three-dimensional visualization system and construction method based on Ethereum big data, which classifies Ethereum blockchain big data according to entities, and constructs an Ethereum head ecological network model by using a Spark computing framework. The whole Ethereum network model is laid out by using a three-dimensional force guiding model, and the Barnes-Hut optimization algorithm extended to three dimensions is introduced in the graph layout process. The local part is laid out on the surface by using a spherical layout method. Finally, the WebGL, ThreeJS and other WEB three-dimensional graphics rendering technologies are used to realize the visualization display of the Ethereum head ecological network on the WEB side. The user can view the three-dimensional visualization model of the Ethereum transaction network in different ways, so that the user interaction process becomes more smooth, the details of the three-dimensional visualization model are controlled more deeply, and the search function in the model is provided, so that the user can quickly locate the node to be concerned.

The geological iterative matching technology based on machine learning assistance is characterized in that a convolutional neural network is used for capturing feature data of a geological image for extraction. Data cleaning standardization is carried out on the extracted data, so that the quality and consistency of the data are ensured, and the accuracy of a subsequent machine learning model is improved. A vector machine (SVM) learning algorithm is utilized to establish an association between the geological data and the model to match the geological model and the actual data. Iterative optimization processing is carried out by using a gradient descent method, and the accuracy and performance of the model are improved step by step. Finally, a neural network visualization algorithm is utilized, the geological data and the result of the model are presented in a visual and understandable mode, and the combination of the geological iteration matching technology based on the machine learning auxiliary algorithm technology helps experts in the geological field to better understand the geological data, recognize the geological mode and improve the geological performance. And optimizing the geologic model to match the actual data.

The invention discloses a zoo monitoring method and system based on lightweight multi-target tracking and self-adaption, and the method comprises the steps: setting a detector and a tracker, and carrying out the real-time multi-target detection and tracking in a zoo; light-weight trajectory prediction is carried out by using a light-weight gating cycle unit GRU network as a prediction model; constructing a real-time 3D semantic map containing a static environment and dynamic obstacles; an online Testtime-Training self-adaptive mechanism is introduced, so that continuous online self-adaptive learning of the model is realized; and setting an early warning rule, performing early warning on animal abnormal behaviors, and configuring a visual interface for real-time display. According to the scheme, the functions of multi-target tracking, trajectory prediction, 3D semantic map construction, abnormal behavior early warning (including electronic fence) and Web visualization are integrated, the application scene is wide, and deployment and maintenance are easy.

The invention discloses a supply chain network visualization method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting node data and edge data of a supply chain network, and forming an initial real-time data flow; performing optimization processing on the initial real-time data stream, dynamically adjusting a data updating strategy to adapt to a data change rate and a system load, and outputting a structured data stream; mapping nodes and edges of the supply chain network into a three-dimensional space for real-time visual rendering based on a graphic hardware acceleration rendering technology and the structured data stream; adopting an intelligent layout algorithm based on a service scene to optimize the node space positions mapped into the three-dimensional space; and carrying out anomaly detection on the running states of the nodes and the edges of the supply chain, and carrying out visual prompt in a three-dimensional space. The basic defects of a traditional supply chain management system in the aspects of real-time performance, intuition and intelligence are thoroughly overcome, and the technical span from passive response to active management and control is achieved.

Various embodiments for systems and methods of providing an artificial intelligence (AI) workflow assistant for single cell RNA sequencing (scRNA-seq) and related "omics" analyses within an R / Seurat environment is disclosed herein. Input data including a Seurat object and associated outputs are processed to compute dataset metrics that are combined with pre-defined prompt templates to internally construct augmented prompts, without requiring users to manually craft prompts. Augmented prompts are submitted to a large language model (LLM), and responses are parsed and integrated into workflow outputs to provide data-driven recommendations and interpretations across analysis stages. In some implementations, an integration and analysis network (IAN) uses a multi-agent architecture in which multiple agents process pathway and network-based enrichment results, aggregate agent responses with an experimental description to form a comprehensive augmented prompt, and generate an integrated LLM response, an LLM-derived system model, and a comprehensive HTML report, optionally including interactive network visualizations summarizing enriched pathways and relationships.

The invention relates to the technical field of industrial equipment monitoring, in particular to a cross-system integrated dedusting data monitoring and layered linkage visualization device which comprises a plurality of dedusting systems and a server, and monitoring points of the dedusting systems are in electric signal connection with the server. An HD-SDK acquisition unit, a SpringBoot synchronization unit, a background control unit, an MyBatis-Plus storage unit, a BaseMapper data operation module and a Web visualization unit are installed on the server; the HD-SDK acquisition unit comprises an HDDataConsection connection module, an HDTagManager label management module and an HDDataProvider data query module, and the HDDataConsection module establishes an encrypted communication link through a triple verification interface of an IP (Internet Protocol), a port, an account number and a password, so that the data transmission security is guaranteed.

The invention relates to the technical field of cell inference of a ligand-receptor co-expression spectrum, and discloses an intercellular communication network inference system based on the ligand-receptor co-expression spectrum. Comprising the following modules: a multi-modal data collaborative acquisition and standardization module, a ligand-receptor candidate set screening and data fusion module, a dual-path feature extraction and fusion module, a dynamic weight distribution and communication network inference module, a closed-loop iterative optimization and parameter updating module, and a communication network visualization and standardization report generation module. All the modules are linked in sequence to realize inference and presentation of an intercellular communication network; the invention aims to solve the problems that the existing communication network inference system generally has single data dimension, inference is carried out by depending on single type of gene expression data, integration and utilization of multi-level information such as spatial position, protein expression, metabolic activity and the like are lacked, complex characteristics of ligand-receptor co-expression cannot be comprehensively captured, and inference accuracy is high. And the inference basis is one-sided and the reliability is insufficient.

The invention discloses a water conservancy project accident cause association analysis method based on an improved Apriori algorithm, and the method comprises the steps: collecting production safety accident case data in the field of water conservancy project construction, constructing a standardized accident cause factor set, and completing cause text normalization processing through synonym replacement and semantic mapping; then, constructing a cause factor causal relationship graph, mining association rules based on an improved Apriori algorithm, introducing accident severity and influence range to construct a weighted evaluation model, and dynamically setting minimum weighted support degree, confidence coefficient and improvement degree; and finally realizing cause network visualization in combination with Gephi software. According to the method, the problems that a traditional Apriori algorithm does not consider accident differentiation characteristics and cause analysis is low in accuracy are solved, the scientificity of hydraulic engineering accident cause association mining is remarkably improved, and accurate data support is provided for safety management.

The invention relates to the technical field of Web visualization, in particular to a forbidden area drawing and path avoidance algorithm based on CANVAS. Through an innovative mixed spatial index architecture and a multi-target path optimization technology, the problems of efficiency, precision and flexibility in traditional Canvas path planning are thoroughly solved, through cooperative work of six core technology modules, a complete multi-target evaluation system and a self-adaptive weight adjustment mechanism are established, and the multi-target path planning method has the advantages that the multi-target path planning efficiency is improved. An incremental updating algorithm based on influence range analysis is developed, local re-calculation during change of a forbidden area is realized, the performance bottleneck of global re-planning is avoided, and an intelligent path planning system with high performance, high quality and high reliability is realized; and a complete technical solution is provided for the fields of Web geographic information systems, game development, intelligent navigation and the like. The technology has a wide application prospect and a remarkable commercial value.