Dynamic data visualization presentation method based on computing power host management operation platform

Abstract

The invention provides a dynamic data visualization presentation method based on a computing power host management operation platform, and belongs to the field of cloud computing / edge computing technology and management operation support, and the method comprises a dynamic rendering engine which realizes high-performance graphic rendering based on WebGL, synchronizes computing power node states in real time in combination with WebSocket, and reduces data transmission delay; data fragmentation compression transmission: carrying out fragmentation compression processing on large-scale concurrent data to reduce network bandwidth occupation; according to the self-adaptive layout strategy, chart layout is dynamically adjusted according to the data size, and the information overload problem is relieved; gPU accelerated rendering: the rendering efficiency is improved by utilizing the parallel computing capability of the GPU; and multi-dimensional chart linkage: supporting multi-dimensional data linkage analysis, and improving operation and maintenance decision efficiency. The method solves the problems of pause, delay and other pain points of a traditional visualization technology in a high-concurrency scene, and is suitable for large-scale data processing and visualization in the fields of cloud computing / edge computing and management operation support.

Description

Technical Field

[0001] This invention relates to the fields of cloud computing / edge computing technology and management and operation support, and in particular to a method for dynamic data visualization based on a computing power host management and operation platform. Background Technology

[0002] In the current field of computing power server management and operation, with the rapid development of information technology, the scale of data is exploding, and high-concurrency scenarios are becoming increasingly common. Traditional data visualization methods have exposed many obvious shortcomings when dealing with high-concurrency data.

[0003] The latency issue is prominent: traditional technologies struggle to efficiently handle the real-time transmission and rendering of large-scale concurrent data, resulting in significant delays in data visualization and hindering the timely provision of accurate information for operational decisions.

[0004] It consumes a lot of resources: It consumes a lot of computing and bandwidth resources, and under high concurrency, it can easily cause system resource shortages or even system crashes.

[0005] Poor user experience: Under large-scale concurrent data, traditional methods often experience lag and information display is chaotic, leading to information overload and making it difficult for operation and maintenance personnel to quickly and accurately obtain key information, which seriously affects the efficiency of operation and maintenance decision-making.

[0006] Currently, while some technologies attempt to address these issues by optimizing rendering algorithms or improving data transmission methods, most fail to achieve holistic optimization of rendering, transmission, and layout, making it difficult to achieve ideal visualization results in high-concurrency scenarios. Therefore, a new technological solution is urgently needed to effectively address data visualization problems in high-concurrency scenarios. Summary of the Invention

[0007] This invention provides a dynamic data visualization method based on a computing power host management and operation platform, which solves the problems of high latency, high resource consumption, and information overload in traditional data visualization under high concurrency scenarios.

[0008] The technical solution of this invention is:

[0009] A method for dynamic data visualization based on a computing power host management and operation platform includes the following steps:

[0010] Build a WebGL dynamic rendering engine and use GPU to accelerate the rendering of computing node data;

[0011] WebSocket technology is used to synchronize the status of computing nodes in real time;

[0012] Data is fragmented, compressed, and transmitted.

[0013] Implement an adaptive layout strategy to dynamically adjust the layout of the visual interface according to the display environment;

[0014] Enable GPU-accelerated rendering and multi-dimensional chart linkage to display computing power node data from multiple dimensions.

[0015] Furthermore,

[0016] The steps for building a WebGL dynamic rendering engine include:

[0017] Initialize the WebGL rendering environment and configure the rendering context and parameters;

[0018] Construct a data model suitable for computing power nodes.

[0019] in,

[0020] WebGL rendering: It uses WebGL to replace traditional technologies and leverages the GPU to accelerate graphics rendering;

[0021] WebSocket Synchronization: Establishes a persistent connection via WebSocket and pushes real-time updates on the status of computing nodes;

[0022] Data-driven rendering: The chart is dynamically updated based on data changes, rather than being refreshed globally.

[0023] Furthermore,

[0024] The steps for real-time synchronization of computing node status using WebSocket technology include:

[0025] Set up a WebSocket service on the server side and configure the listening port;

[0026] The client establishes a persistent WebSocket connection with the server;

[0027] The server monitors the status of computing nodes in real time and pushes the data to the client via WebSocket, allowing the client to update its local status model.

[0028] Furthermore,

[0029] The steps of fragmenting and compressing the data for transmission include:

[0030] Divide large-scale data into several data slices according to rules;

[0031] Each data slice is compressed;

[0032] The compressed data fragments are transmitted to the client, and the client decompresses and restores the data.

[0033] in,

[0034] Data sharding: When it is necessary to transmit large-scale computing node data, the data is divided into several data shards according to rules, and each data shard contains some related data;

[0035] Compression processing: Each data segment is compressed using a compression algorithm;

[0036] Transmission and decompression: The compressed data fragments are transmitted to the client via the network; after receiving the data fragments, the client decompresses them to restore the original data.

[0037] Furthermore,

[0038] The steps for implementing the adaptive layout strategy include:

[0039] Real-time detection of display environment parameters, such as screen size, resolution, and aspect ratio;

[0040] Calculate the optimal layout scheme based on display environment parameters and data characteristics;

[0041] The layout is dynamically adjusted when the display environment or the amount of data changes.

[0042] in,

[0043] Environmental monitoring: The client monitors the parameters of the display environment in real time, including screen size, resolution, and aspect ratio;

[0044] Layout calculation: Based on the detected display environment parameters and the characteristics of the computing power node data that needs to be displayed, the optimal layout scheme is calculated, including the size, position, and arrangement of the charts.

[0045] Dynamic adjustment: When the display environment or the amount of data changes, the layout scheme is automatically recalculated and the visualization interface is dynamically adjusted to ensure the clarity and readability of the data display.

[0046] Furthermore,

[0047] The steps for achieving GPU-accelerated rendering and multi-dimensional chart linkage include:

[0048] The WebGL engine is used to distribute rendering tasks to the GPU for parallel computation.

[0049] Create various types of charts based on data from different dimensions;

[0050] Establish interconnected relationships between multi-dimensional charts to enable operation response and synchronous updates.

[0051] in,

[0052] Multidimensional chart creation: Create several types of charts based on different dimensions of data from computing power nodes, including line charts, bar charts, pie charts, and heatmaps;

[0053] Chart linkage settings: Establish linkage relationships between multi-dimensional charts. When a user performs an operation on a certain chart, other related charts will respond in real time and update their display content synchronously, so that users can have a comprehensive understanding of computing power node data from multiple dimensions.

[0054] The beneficial effects of this invention are

[0055] Significantly improved real-time performance: By synchronizing the status of computing nodes in real time through WebSocket and combining the efficient rendering capabilities of the WebGL dynamic rendering engine, real-time visualization of data is achieved, greatly reducing data latency and enabling operations and maintenance personnel to understand the operating status of computing nodes in a timely manner.

[0056] Significantly reduced resource consumption: Data fragmentation and compression transmission technology effectively reduces the amount of data transmitted and lowers the consumption of bandwidth resources; at the same time, GPU accelerated rendering makes full use of hardware resources, improves rendering efficiency, reduces CPU usage, and lowers the overall resource consumption of the system.

[0057] Solving lag and information overload: The WebGL dynamic rendering engine and GPU accelerated rendering technology solve the lag problem when rendering large-scale concurrent data, making the visualization interface smoother; the adaptive layout strategy and multi-dimensional chart linkage mechanism can reasonably lay out and display data according to the display environment and data characteristics, avoiding information overload and enabling operation and maintenance personnel to quickly and accurately obtain key information.

[0058] Improve operational decision-making efficiency: Real-time, accurate, and clear data visualization provides operations and maintenance personnel with comprehensive and accurate information on the operating status of computing nodes, helping them to promptly identify, analyze, and make decisions, thus significantly improving operational decision-making efficiency. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the workflow of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] This invention provides a method for dynamic data visualization based on a computing power host management and operation platform, which mainly implements the following key steps:

[0062] 1) Dynamic rendering engine

[0063] 2) Data fragmentation and compression transmission

[0064] 3) Adaptive layout strategy

[0065] 4) GPU-accelerated rendering

[0066] 5) Multi-dimensional chart linkage

[0067] The detailed implementation is as follows:

[0068] Step 1: Dynamic Rendering Engine

[0069] WebGL rendering: It uses WebGL to replace traditional technologies and leverages the GPU to accelerate graphics rendering;

[0070] WebSocket Synchronization: Establishes persistent connections via WebSocket to push real-time updates on the status of computing nodes (such as CPU, memory, network, etc.);

[0071] Data-driven rendering: The chart is dynamically updated based on data changes, rather than being refreshed globally.

[0072] Step 2: Data fragmentation, compression, and transmission

[0073] Data sharding: When it is necessary to transmit large-scale computing node data, the data is divided into several data shards according to certain rules, and each data shard contains some related data.

[0074] Compression processing: Each data segment is compressed using an efficient compression algorithm to reduce the amount of data transmitted and reduce bandwidth consumption.

[0075] Transmission and decompression: The compressed data fragments are transmitted to the client over the network. After receiving the data fragments, the client decompresses them to restore the original data.

[0076] Step 3: Adaptive Layout Strategy

[0077] Environment detection: The client monitors the parameters of the display environment in real time, such as screen size, resolution, and aspect ratio.

[0078] Layout calculation: Based on the detected display environment parameters and the characteristics of the computing power node data that needs to be displayed, the optimal layout scheme is calculated, including the size, position, and arrangement of the charts.

[0079] Dynamic adjustment: When the display environment changes (such as when the user adjusts the window size) or the amount of data changes, the layout scheme is automatically recalculated and the visualization interface is dynamically adjusted to ensure the clarity and readability of the data display.

[0080] Step 4: GPU-accelerated rendering

[0081] GPU-accelerated rendering: By utilizing the WebGL rendering engine, rendering tasks are distributed to the GPU for parallel computation, accelerating the model rendering process, improving rendering efficiency, and reducing stuttering.

[0082] Step 5: Multi-dimensional chart linkage

[0083] Multidimensional chart creation: Create various types of charts, such as line charts, bar charts, pie charts, and heatmaps, based on different dimensions of data from computing nodes (such as time dimension, spatial dimension, performance dimension, etc.).

[0084] Chart linkage settings: Establish linkage relationships between multi-dimensional charts. When a user performs an operation on a chart (such as selecting a data range or clicking a data point), other related charts will respond in real time and update their display content synchronously, so that users can have a comprehensive understanding of computing power node data from multiple dimensions.

[0085] The above description is merely a preferred embodiment of the present invention and is used only to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for dynamic data visualization based on a computing power host management and operation platform, characterized in that, Includes the following steps: Build a WebGL dynamic rendering engine and use GPU to accelerate the rendering of computing node data; WebSocket technology is used to synchronize the status of computing nodes in real time; Data is fragmented, compressed, and transmitted. Implement an adaptive layout strategy to dynamically adjust the layout of the visual interface according to the display environment; Enable GPU-accelerated rendering and multi-dimensional chart linkage to display computing power node data from multiple dimensions.

2. The method according to claim 1, characterized in that, The steps for building a WebGL dynamic rendering engine include: Initialize the WebGL rendering environment and configure the rendering context and parameters; Construct a data model suitable for computing power nodes.

3. The method according to claim 2, characterized in that, WebGL rendering: It uses WebGL to replace traditional technologies and leverages the GPU to accelerate graphics rendering; WebSocket Synchronization: Establishes a persistent connection via WebSocket and pushes real-time updates on the status of computing nodes; Data-driven rendering: The chart is dynamically updated based on data changes, rather than being refreshed globally.

4. The method according to claim 1, characterized in that, The steps for real-time synchronization of computing node status using WebSocket technology include: Set up a WebSocket service on the server side and configure the listening port; The client establishes a persistent WebSocket connection with the server; The server monitors the status of computing nodes in real time and pushes the data to the client via WebSocket, allowing the client to update its local status model.

5. The method according to claim 1, characterized in that, The steps of fragmenting and compressing the data for transmission include: Divide large-scale data into several data slices according to rules; Each data slice is compressed; The compressed data fragments are transmitted to the client, and the client decompresses and restores the data.

6. The method according to claim 5, characterized in that, Data sharding: When it is necessary to transmit large-scale computing node data, the data is divided into several data shards according to rules, and each data shard contains some related data; Compression processing: Each data segment is compressed using a compression algorithm; Transmission and decompression: The compressed data fragments are transmitted to the client via the network; after receiving the data fragments, the client decompresses them to restore the original data.

7. The method according to claim 1, characterized in that, The steps for implementing the adaptive layout strategy include: Real-time detection of display environment parameters, such as screen size, resolution, and aspect ratio; Calculate the optimal layout scheme based on display environment parameters and data characteristics; The layout is dynamically adjusted when the display environment or the amount of data changes.

8. The method according to claim 7, characterized in that, Environmental monitoring: The client monitors the parameters of the display environment in real time, including screen size, resolution, and aspect ratio; Layout calculation: Based on the detected display environment parameters and the characteristics of the computing power node data that needs to be displayed, the optimal layout scheme is calculated, including the size, position, and arrangement of the charts. Dynamic adjustment: When the display environment or the amount of data changes, the layout scheme is automatically recalculated and the visualization interface is dynamically adjusted to ensure the clarity and readability of the data display.

9. The method according to claim 1, characterized in that, The steps for achieving GPU-accelerated rendering and multi-dimensional chart linkage include: The WebGL engine is used to distribute rendering tasks to the GPU for parallel computation. Create various types of charts based on data from different dimensions; Establish interconnected relationships between multi-dimensional charts to enable operation response and synchronous updates.

10. The method according to claim 9, characterized in that, Multidimensional chart creation: Create several types of charts based on different dimensions of data from computing power nodes, including line charts, bar charts, pie charts, and heatmaps; Chart Linkage Settings: Establish linkage relationships between multi-dimensional charts. When a user performs an action on a certain chart, other related charts will respond in real time and update their displayed content synchronously.

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