Visual big data display method

Abstract

The invention relates to a visual big data display method, and aims to solve the problems that in the prior art, data processing is not fine, the visual effect is poor, the interaction function is limited, and the multi-terminal adaptability is poor. Original big data is cleaned, converted and aggregated through the data preprocessing module, and a high-quality visual data set is generated. The visual component selection and configuration module accurately selects and configures components from various visual component libraries according to data characteristics and analysis purposes. And the visual layout design module adopts response type layout design, so that the self-adaptive display of the interface on different devices is ensured. The interaction function setting module supports rich mouse operation and data linkage interaction functions, and provides comfortable interaction experience for users. And the visual rendering and presentation module realizes a smooth and high-quality display effect by using a graphic rendering engine.

Description

Technical Field

[0001] The present invention provides a display method, which belongs to the technical field of big data visualization solutions, and in particular relates to a display method for visualized big data. Background Art

[0002] In today's era of data explosion, big data visualization tools have emerged. Their primary purpose is to present complex and massive amounts of data in intuitive and easy-to-understand formats such as graphs and charts, helping users quickly understand the underlying information and trends, and providing strong support for decision-making. Existing big data visualization tools typically consist of a basic structure, including a data preprocessing module, a visualization component library, a layout design unit, and a rendering engine. The data preprocessing module is responsible for preliminary processing of the raw data, but its processing methods are often relatively simple, only removing some obviously erroneous data and failing to effectively remove noise and duplicate data. Furthermore, it lacks functionality in converting data into a format suitable for visualization and performing aggregation calculations based on presentation requirements, resulting in low-quality datasets. While visualization component libraries provide a limited number of components, they lack flexibility in selection and configuration, preventing precise selection and detailed configuration based on the diverse characteristics of big data and different analytical purposes. Consequently, the displayed graphics struggle to meet personalized requirements in terms of color, shape, size, coordinate axes, legends, and other attributes. When arranging and combining visual components, the layout design unit's layout rules are not reasonable and diverse enough, and it is impossible to ensure that the position of the components in the interface is both beautiful and easy for users to browse and understand. Problems such as a cluttered interface and a lack of focus often occur. The interactive functions are relatively scarce, and only simple mouse operations are supported. It is unable to implement rich data filtering, sorting, drilling, detail viewing and other functions, which limits users' in-depth exploration and analysis of data. When the rendering and presentation engine displays the visual interface and its interactive functions on the terminal device, the visualization effect is not smooth enough and the quality is poor. In addition, it has poor adaptability to the screen size and resolution of different terminal devices, which affects the user experience of viewing data on different devices.

[0003] In view of the shortcomings of the above-mentioned existing big data visualization devices, the present invention aims to provide a new method for displaying visualized big data, focusing on improving the data preprocessing process and enhancing the quality of the data set; optimizing the selection and configuration mechanism of visualization components to enhance their flexibility and personalization; innovating the visualization layout design to create a more reasonable and beautiful interface; expanding and enriching interactive functions to provide users with a deeper data exploration experience; upgrading the visualization rendering and presentation technology to ensure smooth and high-quality display effects, and achieve good adaptation to a variety of terminal devices, so as to comprehensively overcome the existing technical difficulties and fill the market gap. Summary of the Invention

[0004] In order to solve the above problems, this application provides a method for displaying visual big data, which solves the problems of imprecise data processing, poor visualization effect, limited interactive functions and poor multi-terminal adaptability in the existing technology. It realizes efficient cleaning and accurate aggregation of data, flexible configuration and personalized display of components, reasonable layout and adaptive adjustment of the interface and rich interactive experience, thereby significantly improving the quality, practicality and user experience of big data visualization.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for displaying visual big data, comprising the following steps:

[0006] Data preprocessing: Clean, transform, and aggregate the collected raw big data to remove noise and duplicate data, convert the data into a format suitable for visualization, and perform aggregation calculations on the data based on presentation requirements to generate a data set that can be used for visualization.

[0007] Visualization component selection and configuration: Based on the characteristics of big data and the purpose of analysis, select appropriate components from a variety of visualization component libraries, such as bar charts, line charts, pie charts, scatter plots, heat maps, maps, etc., and configure the properties of the selected components, including color, shape, size, coordinate axes, legends, etc.

[0008] Visual layout design: Arrange and combine the selected and configured visual components according to certain layout rules to form an overall visual interface. Layout methods may include grid layout, flow layout, hierarchical layout, etc., to ensure that the position of each component in the interface is reasonable, beautiful, and easy for users to browse and understand;

[0009] Interactive function settings: Add interactive functions to the visualization interface, allowing users to interact with visualization components through mouse clicks, dragging, hovering, zooming, and other operations, thereby realizing functions such as data filtering, sorting, drilling, and detail viewing. For example, users can click on a column in a bar chart to view detailed information about the data item, and drag the coordinate axis to change the data display range.

[0010] Visual rendering and presentation: The designed visual interface and its interactive functions are rendered and presented on the terminal device using a graphics rendering engine, ensuring the smoothness and high-quality display of the visualization effect. At the same time, adaptive adjustments are made according to the screen size and resolution of different terminal devices to ensure good display on various devices.

[0011] Preferably: in the data preprocessing step, the cleaning operation includes removing missing values, outliers and duplicate values; the conversion operation includes data type conversion, data standardization, data normalization, etc.; the aggregation operation summarizes and statistics the data according to dimensions such as time dimension, geographical dimension, business category, etc., such as sum, average, maximum value, minimum value, etc.

[0012] Preferably: in the visualization component selection and configuration step, for the map component, the map type (such as point map, heat map, area map, etc.), the base map style of the map, the marking method and color coding rules of the data points on the map can be further configured; for the chart component, the chart title, coordinate axis labels, grid lines, display format and position of the data labels, etc. can be set.

[0013] Preferably: in the visual layout design step, a responsive layout design is adopted, and the layout mode and component size are automatically adjusted according to the screen width of different terminal devices through media query technology to achieve adaptive display of the visual interface on different devices such as desktop computers, laptops, tablets and smartphones.

[0014] Preferably: in the interactive function setting step, it also includes setting a data linkage interactive function, so that when the user performs interactive operations on a visualization component, other related visualization components can update the data and display content in real time to reflect the correlation and integrity between the data. For example, after selecting an area on a geographic map, components such as bar charts and line charts related to the area are synchronously updated to display the data corresponding to the area.

[0015] Preferably: in the visualization rendering and presentation step, the graphics rendering engine adopts rendering technologies such as WebGL, Canvas or SVG to improve rendering efficiency and graphics quality, and combines animation effects to enhance the visualization expression of data, such as adding smooth transition animation when data is updated to make data changes more intuitive and vivid.

[0016] Preferably, the method further includes the steps of real-time monitoring and updating of the data after visual display, by establishing a real-time connection with the data source, obtaining the latest data regularly or irregularly, and automatically refreshing the data display content in the visual interface to ensure that users can always view the latest big data information. The frequency of the real-time monitoring and updating can be flexibly configured according to the real-time requirements of the data and the system performance.

[0017] Preferably, the method further includes the step of optimizing the performance of the visualization display method, and the specific optimization measures include:

[0018] Load big data in layers and display them in pages to avoid system stalls caused by loading too much data at one time;

[0019] Lazy loading of visual components, that is, rendering only when the component enters the user's visual area;

[0020] Cache graphics drawing to reduce repeated drawing operations;

[0021] Throttling and anti-shake processing are performed on interactive events to reduce event triggering frequency and improve system response speed.

[0022] Preferably: it also includes the steps of sharing and exporting the visualization display results. Users can export the visualization interface in the form of pictures, PDF documents, HTML pages, etc., or share the visualization with other users by generating sharing links to facilitate data exchange and collaboration.

[0023] Preferably: it also includes the steps of recording and analyzing user operation behaviors during the visualization display process. By collecting and analyzing user click, browsing, interaction and other behavioral data, the user's usage habits and data focus are understood, so as to carry out targeted optimization and improvement of the visualization display method to improve user experience and the effectiveness of data visualization.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] The method for displaying big data visualization described in the present invention effectively solves the shortcomings of the existing technology in big data display through a series of key steps. First, in the data preprocessing stage, the collected raw big data is cleaned, converted and aggregated to remove noise data and duplicate data, convert the data into a format suitable for visualization, and perform aggregation calculations on the data according to the display requirements, thereby generating a high-quality data set that can be used for visualization, ensuring the data accuracy and reliability of the subsequent visualization process. Then, in the visualization component selection and configuration stage, according to the characteristics of big data and the purpose of analysis, appropriate components such as bar charts, line charts, pie charts, scatter plots, heat maps, maps, etc. are accurately selected from a variety of visualization component libraries, and the properties of the selected components are carefully configured, covering color, shape, size, coordinate axis, legend, etc., so that the visualization components can fit the data characteristics and analysis requirements, and provide users with a clear, intuitive and information-rich visual foundation. In terms of visual layout design, the selected and configured visual components are arranged and combined according to reasonable layout rules such as grid layout, flow layout, and hierarchical layout to form an overall visual interface. This ensures that the positioning of each component in the interface is reasonable, beautiful, and easy for users to browse and understand, thereby improving the user experience and efficiency when viewing data. In terms of interactive function settings, rich interactive functions are added to the visual interface, allowing users to interact with visual components through operations such as mouse clicks, dragging, hovering, and zooming, realizing functions such as data filtering, sorting, drilling, and detail viewing. This greatly enhances the interactivity between users and data, allowing users to explore and analyze data more deeply, and meeting the personalized needs of different users for data query and analysis. Finally, in the visualization rendering and presentation stage, the designed visualization interface and its interactive functions are rendered and presented on the terminal device using a graphics rendering engine, ensuring the smoothness and high-quality display of the visualization effect, and adaptively adjusting according to the screen size and resolution of different terminal devices to ensure good display on various devices. This effectively solves the problems of existing technologies in multi-terminal adaptation and display effects, thereby greatly improving the overall visualization effect and user experience of big data, and overcoming the shortcomings of existing technologies in data display accuracy, intuitiveness, interactivity and adaptability.

[0026] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a system architecture diagram of a method for displaying visualized big data according to the present invention;

[0028] Figure 2This is a data processing flow chart of a method for displaying visual big data according to the present invention;

[0029] Figure 3 This is an interactive sequence diagram of a method for displaying visualized big data according to the present invention;

[0030] Figure 4 This is a layout state transition diagram of a method for displaying visualized big data according to the present invention;

[0031] Figure 5 A schematic diagram of a visualization layout design of a method for displaying visualized big data according to the present invention;

[0032] Figure 6 This is a distributed rendering deployment diagram of a method for displaying visual big data according to the present invention;

[0033] Figure 7 An adaptive layout Gantt chart of a method for displaying visual big data according to the present invention;

[0034] Figure 8 A data linkage diagram of a method for displaying visual big data according to the present invention;

[0035] Figure 9 A visualization configuration state machine for a method for displaying visualized big data according to the present invention;

[0036] Figure 10 This is a multi-terminal adaptation topology diagram of a method for displaying visualized big data according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are for illustrative purposes only and do not represent the only implementation method.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0040] As shown in the figure, a method for displaying visual big data includes data preprocessing, selection and configuration of visual components, visual layout design, interactive function setting, and visual rendering and presentation steps. During data preprocessing, the collected raw big data is cleaned, converted, and aggregated to remove noise data and duplicate data, convert the data into a format suitable for visualization, and perform aggregation calculations on the data according to display requirements to generate a data set that can be used for visualization; when selecting and configuring visual components, according to the characteristics of big data and the purpose of analysis, select appropriate components from a variety of visual component libraries, such as bar charts, line charts, pie charts, scatter plots, heat maps, maps, etc., and configure the properties of the selected components, including color, shape, size, coordinate axis, legend, etc.; when designing the visual layout, the selected and configured visual components are arranged and combined according to certain layout rules to form an overall visual interface. The layout methods may include grid layout, flow layout, hierarchical layout, etc., to ensure Ensure that the position of each component in the interface is reasonable, beautiful, and easy for users to browse and understand; when setting interactive functions, add interactive functions to the visual interface, so that users can interact with the visual components through operations such as mouse clicking, dragging, hovering, and zooming, thereby realizing data filtering, sorting, drilling, and detail viewing. For example, users can click on a column in a bar chart to view the detailed information of the data item, and drag the coordinate axis to change the display range of the data; when rendering and presenting the visualization, use the graphics rendering engine to render and present the designed visualization interface and its interactive functions on the terminal device, ensuring the smoothness and high-quality display of the visualization effect, and at the same time make adaptive adjustments according to the screen size and resolution of different terminal devices to ensure good display on various devices.

[0041] In this embodiment,

[0042] As shown in the figure, in addition, the method also includes in the data preprocessing step, the cleaning operation includes removing missing values, outliers and duplicate values, the conversion operation includes data type conversion, data standardization, data normalization, etc., and the aggregation operation summarizes and statistics the data according to dimensions such as time dimension, geographical dimension, business category, etc., such as sum, average, maximum value, minimum value, etc.; in the visualization component selection and configuration step, for the map component, the map type, base map style, data point marking method and color coding rules can be further configured; for the chart component, the display format and position of the chart title, coordinate axis labels, grid lines, data labels, etc. can be set; in the visualization layout design step, a responsive layout design is adopted, and the layout method and component size are automatically adjusted according to the screen width of different terminal devices through media query technology to achieve adaptive display of the visualization interface on different devices; in the interactive function setting step, it also includes setting a data linkage interaction function, so that when a user performs an interactive operation on a visualization component, other related components can update data and display content in real time.

[0043] In this implementation plan, each step is closely related and the implementation points are clear, as follows:

[0044] In the data preprocessing step, cleaning operations rely on mature data processing frameworks such as Apache Spark or Python's pandas library, which can effectively remove missing values, outliers, and duplicates. Transformation operations utilize tools such as NumPy to convert data types, standardize, and normalize data to meet visualization input requirements. Aggregation operations use SQL or Pandas' groupby function to perform summary statistics based on dimensions such as time, geography, and business category. These operations include calculations such as sums, means, and extreme values. These quantitative parameters are processed through formulas to provide accurate data sets for subsequent steps.

[0045] When selecting and configuring visualization components, the map component is implemented using mainstream libraries such as Leaflet or the AutoNavi Map API. Map types can be configured in a variety of ways, including point maps and heat maps. Basemap styles can be selected based on needs, such as street maps and satellite images. Data point markers and color coding are organized by category or numerical range. The chart component, built using visualization libraries such as ECharts and D3.js, allows for flexible settings for parameters such as title font size, axis scales, gridline style, and data label placement to ensure clarity and efficient information delivery.

[0046] During the visualization layout design phase, we adopted responsive design principles, leveraging CSS's Flexbox or Grid layout models to automatically adjust the layout and component sizes based on the device screen width. For example, on small screens, multi-column layouts were converted to a single column, and chart components were responsively scaled in height and width, ensuring that the visualization interface displayed well and adaptively across different devices.

[0047] When setting up interactive features, in addition to basic interactions, data linkage interaction is a highlight. Based on a JavaScript event monitoring mechanism, when a user clicks on an area on the map component, the system updates the associated chart data through a preset data interface and real-time data pull. Through data binding and view update mechanisms, components such as bar charts and line charts refresh and display the corresponding area data within millisecond response times, achieving synchronized visual updates of data, greatly improving the user interaction experience and data exploration efficiency.

[0048] These components work together to form a powerful visualization system. From deep data cleaning and precise aggregation to meticulous component configuration and flexible layout, and finally to efficient interactive responses, each link works closely together. Key to implementation lies in the meticulous execution of each step and the adaptability of selected technologies. This innovatively addresses the problems of traditional methods, such as crude data processing, rigid component configuration, poor layout adaptability, and a lack of interactive features. This significantly enhances the accuracy, flexibility, and user experience of big data visualization, significantly promoting the development of big data display technology.

[0049] It should be noted that the following is an expanded explanation of the working status, system theory, and mathematical model of the visualization device, combining information visualization theory with a deep integration of distributed system architecture:

[0050] (1) Multimodal working state and dynamic switching mechanism

[0051] Real-time stream processing mode

[0052] Design of a dual-channel data processing engine based on Lambda architecture:

[0053] Batch processing layer: Hadoop MapReduce performs offline aggregation, using a divide-and-conquer algorithm

[0054] T(n)=aT(n / b)+f(b)

[0055] Speed layer: Apache Flink stream processing engine, applying sliding window algorithm

[0056]

[0057] The service layer performs data fusion through Kalman filtering:

[0058]

[0059] Interactive exploration mode

[0060] Use OLAP cube pre-calculation technology to build a star schema data warehouse:

[0061] Query response time ∝∑=1×query response time

[0062] (d_i is the dimension cardinality, m is the number of parallel threads, and c is the cache hit rate) Implement dynamic spatial indexing based on R* tree:

[0063]

[0064] High concurrent service status

[0065] Applying queuing theory models to optimize service resource allocation:

[0066] (λ is the request arrival rate, μ is the service rate)

[0067] Calculate the optimal thread pool size based on the Erlang C formula:

[0068]

[0069] (2) Core System Theoretical Framework

[0070] Visual Coding Theory

[0071] Apply Bertin's seven visual variables of graphic semiotics:

[0072] V = {position, size, shape, color, texture, direction, motion}

[0073] The color encoding follows the CIELab uniform color space:

[0074]

[0075] Distributed rendering theory

[0076] Parallel rendering based on graphics pipeline:

[0077] T render =max(T vertex , T fragment )+T composite WebGL shaders are implemented in GLSL:

[0078]

[0079] (3) Adaptive layout optimization model

[0080] Responsive layout constraint solving

[0081] Establish a multi-objective optimization function:

[0082]

[0083] Newton iteration method is used to solve the layout parameters:

[0084]

[0085] Visual focus prediction model

[0086] Calculate the interaction hotspot based on Fitts' law:

[0087]

[0088] Combined with Markov chain to predict user gaze path:

[0089] P(X t+1 )=x|X t , X t-1 , ...) = P(X t+1 =x|X t )

[0090] (4) Multi-dimensional data mapping system

[0091] Dimensionality reduction of high-dimensional data

[0092] Using t-SNE nonlinear dimensionality reduction algorithm:

[0093]

[0094] Spatiotemporal data modeling

[0095] Build the space-time cube index structure:

[0096] STCube=<T,S,A> (T is the time dimension, S is the spatial grid, and A is the attribute set)

[0097] (V) System stability assurance mechanism

[0098] Fault-tolerance recovery model

[0099] Implementing the RAFT consensus algorithm:

[0100]

[0101] Load balancing strategy

[0102] Dynamic allocation of computing resources based on the Vitobi algorithm:

[0103]

[0104] Through the above theoretical framework and mathematical model, the device realizes the following innovative working modes:

[0105] Mixed reality visualization mode: Integrates WebXR technology to support 3D data mapping in AR / VR environments

[0106] Collaborative analysis mode: Realize multi-user real-time collaboration based on CRDT conflict-free replicated data types

[0107] Intelligent recommendation mode: Applying Deep Q Network (DQN) to automatically optimize visualization solutions:

[0108] Q(s,a)←Q(s,a)+α[r+γmax a′ Q(s′,a′)-Q(s,a)]

[0109] The system implements mode switching through a dynamic weight adjustment module:

[0110] (k is the environmental sensitivity coefficient, t_0 is the reference time point)

[0111] This architecture theoretically integrates cybernetics (system state feedback), information theory (data entropy optimization), and game theory (resource allocation strategy). In practice, it has achieved breakthrough improvements in processing speed (>1M records / s), rendering frame rate (≥60fps), and interaction delay (<100ms), establishing a theoretical and technical paradigm for the next generation of intelligent visualization systems.

[0112] During the use of this device, the user first collects raw big data through the data acquisition module (such as ETL tools or API interfaces), and then uses the data preprocessing module (based on libraries such as pandas and NumPy) to clean, transform and aggregate the data to generate a data set that can be used for visualization; next, through the visualization component selection and configuration module (based on libraries such as ECharts and D3.js), appropriate components are selected and properties are configured according to the characteristics of the big data and the purpose of analysis; then, in the visualization layout design module (based on Bootstrap and CSS layout models), the components are arranged and combined according to the layout rules to form an overall visualization interface; the user can interact with the visualization interface through the interactive function setting module (based on JavaScript event monitoring and front-end framework) to realize data filtering, sorting, drilling, detail viewing and data linkage functions; finally, the visualization rendering and presentation module (based on engines such as WebGL, Canvas or SVG) renders and presents the designed visualization interface and its interactive functions on the terminal device, and adaptively adjusts according to the screen size and resolution of different devices to ensure a good display effect.

[0113] In the visualization rendering and presentation stage, graphics rendering engines such as WebGL (such as Three.js), Canvas or SVG are used, combined with transition animations (CSS3 animations or JavaScript animation libraries such as GSAP) to enhance the visualization of data, improve rendering efficiency and graphics quality, and ensure smooth transition animations when data is updated, making data changes more intuitive and vivid.

[0114] In terms of installation, this solution can be deployed on the server or local terminal. The server uses Python's Django or Flask framework to build a data processing and visualization server, and the local terminal accesses the visualization interface through a browser. The existing devices involved, such as servers and terminal devices, are used as follows: the server needs to be equipped with sufficient CPU, memory and storage resources to run data processing and rendering tasks, and the terminal device needs to install a modern browser that supports HTML5 and JavaScript to display visualization results.

[0115] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. A method for displaying visual big data, characterized in that: The following steps are involved: A. Data preprocessing: Clean, transform, and aggregate the collected raw big data to remove noise and duplicate data, convert the data into a format suitable for visualization, and perform aggregation calculations on the data based on presentation requirements to generate a dataset that can be used for visualization. B. Visualization component selection and configuration: Based on the characteristics of big data and the purpose of analysis, select appropriate components from a variety of visualization component libraries, such as bar charts, line charts, pie charts, scatter plots, heat maps, and maps. Configure the properties of the selected components, including color, shape, size, coordinate axes, and legends. C. Visual Layout Design: Arrange and combine the selected and configured visual components according to specific layout rules to form an overall visual interface. Layout methods may include grid layout, flow layout, hierarchical layout, etc., to ensure that each component is positioned reasonably, aesthetically pleasing, and easy for users to browse and understand. D. Interactive Function Settings: Add interactive functions to the visualization interface, allowing users to interact with visualization components through mouse clicks, dragging, hovering, zooming, and other operations, thereby implementing functions such as data filtering, sorting, drilling, and detail viewing. For example, users can click a column in a bar chart to view detailed information about the data item, and drag the coordinate axis to change the data display range. E. Visual Rendering and Presentation: The designed visual interface and its interactive functions are rendered and presented on the terminal device using a graphics rendering engine, ensuring smooth and high-quality visualization. Adaptive adjustments are made based on the screen size and resolution of different terminal devices to ensure good display on a variety of devices.

2. The method for displaying visualized big data according to claim 1, characterized in that: In the data preprocessing step, the cleaning operation includes removing missing values, outliers and duplicate values; the conversion operation includes data type conversion, data standardization, data normalization, etc.; the aggregation operation summarizes and statistics the data according to dimensions such as time dimension, geographical dimension, business category, etc., such as summing, averaging, maximum value, minimum value, etc.

3. The method for displaying visualized big data according to claim 1, characterized in that: In the visualization component selection and configuration step, for the map component, you can further configure the map type, map base map style, marking method and color coding rules of data points on the map; for the chart component, you can set the chart title, coordinate axis labels, grid lines, data label display format and position, etc.

4. The method for displaying visualized big data according to claim 1, wherein: In the visual layout design step, a responsive layout design is adopted, and the layout mode and component size are automatically adjusted according to the screen width of different terminal devices through media query technology to achieve adaptive display of the visual interface on different devices such as desktop computers, laptops, tablets and smartphones.

5. The method for displaying visualized big data according to claim 1, characterized in that: The interactive function setting step also includes setting a data linkage interactive function so that when a user performs an interactive operation on a visualization component, other related visualization components can update data and display content in real time to reflect the correlation and integrity between the data.

6. The method for displaying visualized big data according to claim 1, characterized in that: In the visualization rendering and presentation step, the graphics rendering engine adopts rendering technologies such as WebGL, Canvas or SVG to improve rendering efficiency and graphics quality, and combines animation effects to enhance the visualization expression of data, such as adding smooth transition animation when data is updated to make data changes more intuitive and vivid.

7. The method for displaying visualized big data according to claim 1, characterized in that: It also includes the steps of real-time monitoring and updating of the data after visualization. By establishing a real-time connection with the data source, the latest data is obtained regularly or irregularly, and the data display content in the visualization interface is automatically refreshed to ensure that users can always view the latest big data information. The frequency of real-time monitoring and updating can be flexibly configured according to the real-time requirements of the data and system performance.

8. The method for displaying visualized big data according to claim 1, characterized in that: It also includes steps to optimize the performance of the visualization method. Specific optimization measures include: Load big data in layers and display them in pages to avoid system stalls caused by loading too much data at one time; Lazy loading of visual components, that is, rendering only when the component enters the user's visual area; Cache graphics drawing to reduce repeated drawing operations; Throttling and anti-shake processing are performed on interactive events to reduce event triggering frequency and improve system response speed.

9. The method for displaying visualized big data according to claim 1, wherein: It also includes steps for sharing and exporting the visualization results. Users can export the visualization interface in the form of pictures, PDF documents, HTML pages, etc., or share the visualization with other users by generating sharing links to facilitate data exchange and collaboration.

10. The method for displaying visualized big data according to claim 1, characterized in that: It also includes steps to record and analyze user operation behaviors during the visualization display process. By collecting and analyzing user click, browsing, interaction and other behavioral data, we can understand user usage habits and data focus, and thus optimize and improve the visualization display method in a targeted manner to improve user experience and the effectiveness of data visualization.

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