Large-scale data dynamic adaptation processing and visual display method and system

Abstract

The invention discloses a large-scale data dynamic adaptation processing and visual display method and system, and belongs to the field of large-scale data visual processing, and the method comprises the steps: calculating an optimal display data volume in positive correlation with equipment performance in real time, generating a grouping step length based on a query interval and the data volume, grouping original data according to x-axis coordinate values, and then executing an aggregation operation, key statistical characteristics such as the average value, the maximum value and the minimum value of each group of data are obtained; and automatically adapting the display style according to the aggregated data features. According to the method, zero-lag visualization of large-scale data is realized through server lightweight aggregation and client capability dynamic adaptation, core data value is reserved, and the method is verified in scenes such as hydropower engineering monitoring and the like, and intelligent decision making is effectively supported.

Description

Technical Field

[0001] This application relates to the field of large-scale data visualization processing, specifically to a method and system for large-scale dynamic adaptive data processing and visualization. Background Technology

[0002] With the deep application of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in industries such as manufacturing, energy, and transportation, the data sensing capabilities and collection scale of digital systems are growing exponentially. In large-scale data query and visualization scenarios, existing technologies generally employ traditional processing methods: the system retrieves and directly returns the original dataset based on user-preset query conditions (such as time range and data thresholds) to achieve data display and analysis. To ensure system stability and response efficiency, existing solutions mandate that users limit the span of a single query range when setting query conditions (e.g., only allowing queries for "last 7 days" of data) to avoid excessive server load, client lag, or system crashes due to excessive data volume. While this approach was effective when data volumes were small in the past, it is no longer suitable for the demands of today's massive data scenarios.

[0003] Existing technologies have the following core defects in large-scale data processing, which severely restrict system performance and business value.

[0004] First, the limited scope of data display reduces task flexibility and efficiency. Specifically, to prevent system failures, the query range must be forcibly narrowed (e.g., limited to "within 7 days"), preventing users from freely querying the full amount of data (e.g., "2023 full-year trend"). This not only limits the completeness of data display but also significantly reduces the flexibility of task processing and system response speed, directly dragging down user work efficiency and task completion time.

[0005] Secondly, the lack of client-side adaptation capabilities leads to resource waste and a fragmented user experience. Specifically, due to significant differences in the performance of terminal devices (such as low-end mobile phones, high-performance PCs, and command and control screens), existing technologies adopt a "one-size-fits-all" approach: all clients use the same fixed data volume display logic. As a result, low-performance devices (such as mobile phones) lag when processing massive amounts of data, resulting in a poor user experience, while high-performance devices (such as PCs) cannot utilize their hardware potential due to insufficient data volume, leading to a serious waste of resources. Furthermore, to adapt to multiple platforms, additional differentiated solutions need to be developed, significantly increasing development costs and maintenance complexity, ultimately harming the overall user experience.

[0006] Finally, this hinders in-depth data mining and leads to missed business value. Specifically, existing methods focus only on "data volume reduction," sacrificing key statistical characteristics of the data (such as trends and peaks). This prevents IoT and AI technologies from effectively utilizing raw data for in-depth analysis (such as predictive maintenance and intelligent decision-making), resulting in hindered business process optimization, decreased decision quality, and ultimately, wasted data resources and lost business value.

[0007] In summary, existing technologies, due to their limited scope, rigid client adaptation, and superficial data value, are no longer able to support efficient and intelligent applications in large-scale data scenarios, and breakthrough solutions are urgently needed to unleash the potential of data. Summary of the Invention

[0008] This application provides a method and system for large-scale dynamic data adaptation processing and visualization, which can solve the technical problems existing in the prior art, such as limited query range, poor cross-terminal compatibility, low performance caused by large-scale data transmission, and loss of data value.

[0009] In a first aspect, embodiments of this application provide a method for dynamic adaptation processing and visualization of large-scale data, the method comprising: The optimal amount of data to be displayed is determined based on the client's capabilities, and the optimal amount of data to be displayed is positively correlated with the client's capabilities. The data grouping step size is calculated based on the optimal amount of data to be displayed, and the data grouping step size is negatively correlated with the optimal amount of data to be displayed. The original data is grouped according to the data grouping step size, and aggregation operations are performed on each group of data to generate corresponding aggregated data. The aggregated data is then visualized.

[0010] In conjunction with the first aspect, in one implementation, determining the optimal amount of data to display based on the client's capabilities includes evaluating the optimal amount of data to display based on the client's data transmission rate and rendering capabilities.

[0011] In conjunction with the first aspect, in one implementation, the aggregation operation includes calculating the average value of each group of data, calculating the maximum value of each group of data, and calculating the minimum value of each group of data.

[0012] In conjunction with the first aspect, in one implementation, the visualization includes selecting a visualization style based on the characteristics of the aggregated data.

[0013] In conjunction with the first aspect, in one implementation, the data grouping is based on the x-axis coordinate values ​​of the data.

[0014] In conjunction with the first aspect, in one implementation, the data grouping step size is calculated by subtracting one from the optimal display data size, and then dividing the data range by this result.

[0015] Secondly, embodiments of this application provide a large-scale data dynamic adaptation processing and visualization system, including: The client is used to determine the optimal amount of data to be displayed based on the client's capabilities, wherein the optimal amount of data to be displayed is positively correlated with the client's capabilities; The server is used to calculate the data grouping step size based on the optimal display data volume. The data grouping step size is negatively correlated with the optimal display data volume. The server then performs aggregation operations on the original data after grouping it to generate aggregated data. The visualization module is used to visualize the aggregated data.

[0016] In conjunction with the second aspect, in one implementation, determining the optimal amount of data to be displayed based on the client's capabilities includes evaluating the optimal amount of data to be displayed based on the client's data transmission rate and rendering capabilities.

[0017] In conjunction with the second aspect, in one implementation, the aggregation operation includes calculating the average value of each group of data, calculating the maximum value of each group of data, and calculating the minimum value of each group of data.

[0018] In conjunction with the second aspect, in one implementation, the visualization includes selecting a visualization style based on the characteristics of the aggregated data.

[0019] The beneficial effects of the technical solutions provided in this application include: This invention dynamically calculates the optimal amount of data to display based on client performance differences, allowing users to freely select any data range. The server generates a grouping step size based on the query interval and the optimal data size, groups the original data by x-axis coordinates, performs aggregation operations, and sends the compressed data to the client for display. The client automatically adapts the display style based on the characteristics of the aggregated data, requiring only the rendering of lightweight results. This ensures that clients of varying performance levels are not limited by the query interval, solving the technical problems of poor cross-platform compatibility, performance degradation due to large-scale data transmission, and data value loss. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an embodiment of the method for dynamic adaptation processing and visualization of large-scale data in this application. Figure 2 This is a schematic diagram of the client display interface of a specific embodiment of the method for dynamic adaptation processing and visualization of large-scale data in this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0023] In a first aspect, embodiments of this application provide a method for large-scale dynamic data adaptation processing and visualization.

[0024] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the method for dynamic adaptation processing and visualization of large-scale data according to this application. Figure 1 As shown, methods for dynamic adaptation processing and visualization of large-scale data include: Step S1: Determine the optimal amount of data to display based on the client's capabilities. The optimal amount of data to display is positively correlated with the client's capabilities. Step S2: Calculate the data grouping step size based on the optimal display data volume. The data grouping step size is negatively correlated with the optimal display data volume. Step S3: Divide the original data into groups according to the data grouping step size, and perform aggregation operations on each group of data to generate the corresponding aggregated data; Step S4: Visualize the aggregated data.

[0025] In this embodiment, the optimal amount of data to display is dynamically calculated based on client performance differences, allowing users to freely choose any data range. The server generates a grouping step size based on the query interval and the optimal amount of data to display. After grouping the original data according to the x-axis coordinate values, it performs aggregation operations and sends the compressed data to the client for display. The client automatically adapts the display style according to the characteristics of the aggregated data. The client only needs to render the lightweight result, thus ensuring that clients of various performance levels are not limited by the query interval. This solves the technical problems of poor cross-platform compatibility, performance degradation due to large-scale data transmission, and loss of data value.

[0026] The x-axis coordinate value is a standard technical term (as shown in the table, the value corresponding to the horizontal axis, such as time, distance, category, etc.).

[0027] In one specific embodiment, compared to traditional solutions where the client system forcibly restricts the query range (e.g., only allowing 7 days), preventing users from viewing complete data (e.g., the trend for the entire year of 2023), this invention allows users to freely select any data range (e.g., the entire year of 2023), and the system automatically and dynamically calculates the appropriate data volume, eliminating the need to narrow the query range. For example, refer to... Figure 2 In PM2.5 monitoring scenarios, when users query monitoring data for a specified time period, the system quickly generates a complete trend chart without data loss.

[0028] Compared to traditional solutions that use a fixed amount of data across all client systems, resulting in lag on low-performance devices and resource waste on high-performance devices, this invention automatically reduces the optimal display size for low-performance devices (such as mobile phones), showing only key points smoothly without lag; for high-performance devices (such as PCs), it automatically increases the optimal display size, approaching the accuracy of the original data. This large-scale, dynamically adaptive data processing and visualization solution is compatible with all terminals (mobile phones / tablets / command screens), effectively reducing development costs.

[0029] Furthermore, in one embodiment, determining the optimal amount of data to be displayed based on the client's capabilities includes evaluating the optimal amount of data to be displayed based on the client's data transmission rate and rendering capabilities.

[0030] In this embodiment, the optimal data display volume n is calculated based on client data transmission efficiency, the amount of data to be displayed, and rendering capabilities (such as device performance and network bandwidth). For example, n=10000 for high-performance PCs and n=500 for low-end mobile phones.

[0031] In addition to data transfer rate and rendering capability, there are some indirect parameters that can be used to calculate the optimal amount of data to display, such as the client's CPU performance, memory size, screen size, and network type (4G / 5G).

[0032] Furthermore, in one embodiment, the aggregation operation includes calculating the average value of each group of data, calculating the maximum value of each group of data, and calculating the minimum value of each group of data.

[0033] In this embodiment, during the data compression process, the average, maximum, minimum, and boundary points (e.g., stress 18MPa on day 1, 25MPa on day 10) are calculated for each group to ensure the continuity and integrity of the visualized trend curve, avoiding the trend breakage caused by data pruning in traditional solutions. For example, in the operation and maintenance of hydropower stations, the system can accurately capture stress peak values ​​and changing trends, providing a reliable basis for predictive maintenance, significantly improving the value mining rate of IoT data, and directly promoting the improvement of business decision-making quality and the reduction of operation and maintenance costs.

[0034] In one specific implementation, assuming temperature (y-axis: temperature value) and time (x-axis: time) are recorded daily, after 100 days of records, the data volume becomes too large for a mobile phone to display completely. Therefore, data compression and grouping are necessary. For example, dividing the 100 days into 10 groups of 10 days each requires calculating the y-axis average (average temperature over those 10 days), the y-axis maximum (temperature of the hottest day), the y-axis minimum (temperature of the coldest day), and the y-axis boundary values ​​(temperatures of the first and last days of those 10 days). If only the average is calculated for each group, data gaps exist between groups, making it difficult to form trend curves. By calculating the average, maximum, minimum, and boundary values, key data information needed for subsequent business analysis can be accurately preserved.

[0035] Furthermore, in one embodiment, the visualization includes selecting a visualization style based on the characteristics of the aggregated data.

[0036] In this embodiment, after receiving the aggregated data, the client automatically selects an appropriate visualization style (e.g., using the average value for a line chart and the maximum value for a bar chart). For example, if the aggregated data contains significant extreme values ​​(e.g., the maximum value is much higher than the average), a bar chart is automatically selected to highlight the peak value. If the data is continuous and smooth (with small fluctuations in the average / minimum value), a line chart is selected to display the trend. This intelligent mapping of data characteristics to display styles avoids analytical biases caused by human error in selection.

[0037] Furthermore, in one embodiment, the data grouping is based on the x-axis coordinate value of the data.

[0038] The data grouping step size is calculated by subtracting one from the optimal display data size, and then dividing the data range by this result.

[0039] In this embodiment, the server calculates the optimal data display volume n (not a fixed value, but dynamically calculated based on the device) based on the client's data transmission efficiency, the amount of data to be displayed, and rendering capabilities (such as device performance and network bandwidth). For example, n=10000 for high-performance PCs and n=500 for low-end mobile phones.

[0040] The user-defined query interval (a, b) (e.g., time range 2023-01-01~2023-12-31) is used to calculate the step size d = (b - a) / (n - 1) according to the formula. This divides the query interval into n-1 equal segments, generating n data points. For example, the x-coordinate value of the original data is divided by the step size d, and data with the same quotient are grouped together (e.g., x=15.2, 15.2 / d=3.05, then group number 3). The last data point is automatically assigned to the last group.

[0041] For each set of data, calculate the average y-axis value, maximum y-axis value, minimum y-axis value, and the y-value (nearest value) corresponding to the boundary points on the x-axis to obtain aggregated data.

[0042] The server returns n aggregation points (x_i, y_i). The client selects the appropriate visualization style (e.g., average value for line chart, maximum value for bar chart). The client only needs to render n points, without processing the original massive amount of data, which greatly improves the response speed.

[0043] In summary, this invention calculates the optimal amount of data to be displayed in real time based on client-side capabilities, automatically eliminates the upper limit of the query interval, supports seamless exploration of data in any range (such as full analysis of millions of time series data), and solves the inflexibility problem of traditional solutions that require manual setting of intervals.

[0044] This invention provides a closed-loop mechanism for server-side dynamic aggregation and client-side capability assessment, enabling a single codebase to adapt to devices across all platforms, avoiding the need to customize logic for different devices, reducing development costs, and improving user experience consistency.

[0045] This invention groups and aggregates the original data according to the x-axis coordinate values ​​(generating an average, maximum, and minimum value for each group), which greatly compresses the amount of data transmitted and significantly reduces the response time, thus solving the problems of lag and server pressure caused by large-scale data transmission.

[0046] This invention preserves core statistical features (mean, extreme values, and boundary details) through aggregation operations, compressing data volume without losing key information, providing high-quality input for AI analysis, and supporting in-depth decision-making in intelligent construction scenarios.

[0047] Secondly, embodiments of this application also provide a large-scale data dynamic adaptation processing and visualization system.

[0048] In one embodiment, the large-scale data dynamic adaptation processing and visualization system includes: The client is used to determine the optimal amount of data to be displayed based on the client's capabilities, wherein the optimal amount of data to be displayed is positively correlated with the client's capabilities; The server is used to calculate the data grouping step size based on the optimal display data volume. The data grouping step size is negatively correlated with the optimal display data volume. The server then performs aggregation operations on the original data after grouping it to generate aggregated data. The visualization module is used to visualize the aggregated data.

[0049] In this embodiment, the optimal amount of data to display is dynamically calculated based on client performance differences, allowing users to freely choose any data range. The server generates a grouping step size based on the query interval and the optimal amount of data to display. After grouping the original data according to the x-axis coordinate values, it performs aggregation operations and sends the compressed data to the client for display. The client automatically adapts the display style according to the characteristics of the aggregated data. The client only needs to render the lightweight result, thus ensuring that clients of various performance levels are not limited by the query interval. This solves the technical problems of poor cross-platform compatibility, performance degradation due to large-scale data transmission, and loss of data value.

[0050] Furthermore, in one embodiment, determining the optimal amount of data to be displayed based on the client's capabilities includes evaluating the optimal amount of data to be displayed based on the client's data transmission rate and rendering capabilities.

[0051] In this embodiment, the optimal data display volume n is calculated based on client data transmission efficiency, the amount of data to be displayed, and rendering capabilities (such as device performance and network bandwidth). For example, n=10000 for high-performance PCs and n=500 for low-end mobile phones.

[0052] In addition to data transfer rate and rendering capability, there are some indirect parameters that can be used to calculate the optimal amount of data to display, such as the client's CPU performance, memory size, screen size, and network type (4G / 5G).

[0053] Furthermore, in one embodiment, the aggregation operation includes calculating the average value of each group of data, calculating the maximum value of each group of data, and calculating the minimum value of each group of data.

[0054] In this embodiment, during the data compression process, the average, maximum, minimum, and boundary points (e.g., stress 18MPa on day 1, 25MPa on day 10) are calculated for each group to ensure the continuity and integrity of the visualized trend curve, avoiding the trend breakage caused by data pruning in traditional solutions. For example, in the operation and maintenance of hydropower stations, the system can accurately capture stress peak values ​​and changing trends, providing a reliable basis for predictive maintenance, significantly improving the value mining rate of IoT data, and directly promoting the improvement of business decision-making quality and the reduction of operation and maintenance costs.

[0055] Furthermore, in one embodiment, the visualization includes selecting a visualization style based on the characteristics of the aggregated data.

[0056] In this embodiment, after receiving the aggregated data, the client automatically selects an appropriate visualization style (e.g., using the average value for a line chart and the maximum value for a bar chart). For example, if the aggregated data contains significant extreme values ​​(e.g., the maximum value is much higher than the average), a bar chart is automatically selected to highlight the peak value. If the data is continuous and smooth (with small fluctuations in the average / minimum value), a line chart is selected to display the trend. This intelligent mapping of data characteristics to display styles avoids analytical biases caused by human error in selection.

[0057] The functions of each module in the above-mentioned large-scale data dynamic adaptation processing and visualization system correspond to the steps in the above-mentioned large-scale data dynamic adaptation processing and visualization method embodiment, and their functions and implementation processes will not be described in detail here.

[0058] Thirdly, embodiments of this application provide a large-scale data dynamic adaptation processing and visualization display device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0059] In this embodiment, the large-scale data dynamic adaptation processing and visualization device may include a processor, a memory, a communication interface, and a communication bus.

[0060] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0061] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of devices that enable large-scale data dynamic adaptive processing and visualization, as well as interfaces used for interconnecting these devices with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0062] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0063] The processor can be a general-purpose processor, which can call the large-scale data dynamic adaptation processing and visualization display program stored in the memory and execute the large-scale data dynamic adaptation processing and visualization display method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the large-scale data dynamic adaptation processing and visualization display program is called can refer to the various embodiments of the large-scale data dynamic adaptation processing and visualization display method of this application, and will not be repeated here.

[0064] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0065] The present application stores a program for dynamic adaptation processing and visualization of large-scale data on a computer-readable storage medium, wherein when the program is executed by a processor, it implements the steps of the method for dynamic adaptation processing and visualization of large-scale data as described above.

[0066] The method implemented when the large-scale data dynamic adaptation processing and visualization display program is executed can be referred to in the various embodiments of the large-scale data dynamic adaptation processing and visualization display method of this application, and will not be repeated here.

[0067] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0068] The terminology in this application's specification, claims, and accompanying drawings, including and having any variations thereof, is intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus. The terms first, second, and third, etc., are used to distinguish different objects, etc., and do not represent a sequential order, nor do they limit first, second, and third to different types.

[0069] In the description of the embodiments in this application, terms such as exemplary, for example, or exemplify are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as exemplary, for example, or exemplify in the embodiments of this application should not be construed as being better or more advantageous than other embodiments or design solutions. Specifically, the use of terms such as exemplary, for example, or exemplify is intended to present the relevant concepts in a concrete manner.

[0070] In the description of the embodiments of this application, unless otherwise stated, / means or, for example, A / B can mean A or B; and / or in the text is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, multiple means two or more.

[0071] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0073] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for large-scale dynamic adaptive processing and visualization of data, characterized in that, The methods for dynamic adaptation processing and visualization of large-scale data include: The optimal amount of data to be displayed is determined based on the client's capabilities, and the optimal amount of data to be displayed is positively correlated with the client's capabilities. The data grouping step size is calculated based on the optimal amount of data to be displayed, and the data grouping step size is negatively correlated with the optimal amount of data to be displayed. The original data is grouped according to the data grouping step size, and aggregation operations are performed on each group to generate corresponding aggregated data; the aggregation operations include calculating the average value of each group, calculating the maximum value of each group, and calculating the minimum value of each group. The aggregated data is then visualized.

2. The method for large-scale dynamic data adaptation processing and visualization as described in claim 1, characterized in that, Determining the optimal amount of data to display based on client capabilities includes evaluating the optimal amount of data to display based on the client's data transmission rate and rendering capabilities.

3. The method for large-scale dynamic data adaptation processing and visualization as described in claim 1, characterized in that, The visualization includes selecting a visualization style based on the characteristics of the aggregated data.

4. The method for large-scale dynamic data adaptation processing and visualization as described in claim 1, characterized in that, The data is grouped based on the x-axis coordinate value of the data.

5. The method for large-scale dynamic data adaptation processing and visualization as described in claim 1, characterized in that, The data grouping step size is calculated by subtracting one from the optimal display data size, and then dividing the data range by this result.

6. A large-scale data dynamic adaptive processing and visualization system, characterized in that, include: The client is used to determine the optimal amount of data to be displayed based on the client's capabilities, wherein the optimal amount of data to be displayed is positively correlated with the client's capabilities; The server is used to calculate the data grouping step size based on the optimal display data volume. The data grouping step size is negatively correlated with the optimal display data volume. The server then performs aggregation operations on the original data after grouping it to generate aggregated data. The visualization module is used to visualize the aggregated data.

7. The large-scale data dynamic adaptive processing and visualization system as described in claim 6, characterized in that, Determining the optimal amount of data to display based on client capabilities includes evaluating the optimal amount of data to display based on the client's data transmission rate and rendering capabilities.

8. The large-scale data dynamic adaptive processing and visualization system as described in claim 6, characterized in that, The aggregation operation includes calculating the average value of each group of data, calculating the maximum value of each group of data, and calculating the minimum value of each group of data.

9. The large-scale data dynamic adaptive processing and visualization system as described in claim 6, characterized in that, The visualization includes selecting a visualization style based on the characteristics of the aggregated data.

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