Time axis data visualization method and system based on data density adaptive dynamic adjustment

Abstract

The invention discloses a time axis data visualization method and system based on data density self-adaptive dynamic adjustment, and the method comprises the steps: firstly determining a time range of display data on a time axis, then dynamically adjusting interval division intervals according to a time span, carrying out the statistics of the data size of each interval and the total amount of all data, and carrying out the calculation of the total amount of all data; calculating the density degree of each interval relative to the overall data; the method comprises the following steps: according to the data density of an interval, dynamically adjusting the pixel length of the interval according to the data density of the interval, finally drawing an interval mark on a time axis according to the interval after the pixel length is dynamically adjusted so as to distinguish different time periods, and distributing different colors or color shades for different intervals according to the data density of each interval. Therefore, the density degree of data distribution can be visually displayed. According to the method, the key data can be ensured to be clear and visible, meanwhile, redundant display is avoided, the accuracy and efficiency of data visualization are greatly improved, and double improvement of the data visualization effect and the user experience is achieved.

Description

Technical Field

[0001] The present invention relates to a timeline data visualization method and system based on adaptive dynamic adjustment of data density, belonging to the technical field of computer software and network security. Background Art

[0002] The cyber range serves as a platform for simulating actual combat environments to deepen security testing and drills. In the daily tasks assigned in the cyber range, a large number of security tasks are continuously issued and pushed to various simulated environments in real time.

[0003] At present, the network range has the following shortcomings when displaying and issuing tasks: Traditional data visualization methods often use a fixed time axis to display data, and cannot dynamically calculate the data allocation time axis component interval. In time periods with high data density, a large amount of task information may be compressed or ignored due to limited display space, resulting in omission of key information. In time periods with sparse data, there may be blank displays or waste of resources. The current unbalanced display effect not only affects the accuracy and timeliness of data analysis, but also reduces the user experience. Summary of the invention

[0004] Purpose of the invention: In view of the fact that the traditional fixed timeline display of the network target range is difficult to cope with dynamic changes in data when issuing tasks, which easily leads to information omissions or space waste, the present invention provides a timeline data visualization method and system based on adaptive dynamic adjustment of data density to ensure full display of key data, efficient use of space, and improved data visualization effect and efficiency.

[0005] Technical solution: To achieve the above-mentioned invention purpose, the present invention provides a timeline data visualization method based on adaptive dynamic adjustment of data density, which can perceive data changes in real time and intelligently adjust the display ratio, and specifically includes the following steps:

[0006] Determine the time range for displaying data on the timeline;

[0007] Dynamically adjust the interval division interval according to the time span of the data on the time axis, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the time axis;

[0008] Count the amount of data in each interval and the total amount of all data, and calculate the density of each interval relative to the overall data;

[0009] Dynamically adjust the pixel length of the interval according to the data density of the interval. When the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced.

[0010] According to the intervals dynamically adjusted according to the pixel length, interval marks are drawn on the time axis to distinguish different time periods. According to the data density of each interval, different colors or color depths are assigned to different intervals to intuitively show the density of data distribution.

[0011] Preferably, when dynamically adjusting the interval division interval according to the time span, different time span length ranges and their corresponding interval division intervals are set. As the time span becomes longer, when reaching the next length range from the previous length range, the interval division interval is dynamically adjusted to the interval division interval corresponding to the next length range; wherein the interval division interval corresponding to the next length range is larger than the interval division interval corresponding to the previous length range.

[0012] Preferably, when the pixel length of the interval is dynamically adjusted according to the data density of the interval, the pixel length length_i of the adjusted interval i on the total pixel length L of the time axis is calculated according to the following formula:

[0013] length_i=L*(d_i / Σd_j)

[0014] Where d_i represents the data density of interval i, and Σd_j represents the sum of the data densities of all intervals.

[0015] Preferably, task data is received in real time, the time range is dynamically calculated, and the time axis is updated.

[0016] Preferably, when drawing the timeline, a straight line is used as the axis of the timeline to represent the baseline of the timeline, and all intervals are traversed to draw filled rectangles. The width of each rectangle corresponds to the pixel length of the interval, and the height is adjusted as needed to maintain visual balance.

[0017] Preferably, the data density of different intervals is displayed on the time axis, and the data is identified by a mark at the time point when the data is received.

[0018] Based on the same inventive concept, the present invention provides a timeline data visualization system based on adaptive dynamic adjustment of data density, comprising:

[0019] A time range determination module, used to determine the time range of the data displayed on the time axis;

[0020] The time data analysis module is used to dynamically adjust the interval division interval according to the time span of the data on the time axis, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the time axis;

[0021] Data density analysis module, used to count the amount of data in each interval and the total amount of all data, and calculate the density of each interval relative to the overall data;

[0022] The interval dynamic adjustment module is used to dynamically adjust the pixel length of the interval according to the data density of the interval. When the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced;

[0023] The timeline rendering module is used to draw interval marks on the timeline according to the intervals dynamically adjusted according to the pixel length to distinguish different time periods. Different colors or color depths are assigned to different intervals according to the data density of each interval to intuitively show the density of data distribution.

[0024] The present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the timeline data visualization method based on adaptive dynamic adjustment of data density.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following advantages: By dynamically adjusting the display ratio of each interval on the timeline, the present invention intelligently allocates space according to data density to ensure that key data is clearly visible while avoiding redundant display. This greatly improves the accuracy and efficiency of data visualization, enhances the intuitiveness and readability of information, and enables users to quickly capture key points and optimize the decision-making process. Overall, the present invention achieves a dual improvement in data visualization effect and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is an overall flow chart of an embodiment of the present invention.

[0027] Figure 2 This is a design example diagram of timeline data in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments.

[0029] The present invention proposes a timeline data visualization method based on adaptive dynamic adjustment of data density. The method can be widely used in the scene of visual display when tasks are issued in the network range. By intelligently calculating the relationship between the time range and the data density, the display ratio of each time period on the overall timeline is dynamically adjusted. In this way, key data can be fully displayed, and the display space can be effectively used, thereby improving the effect and efficiency of data visualization.

[0030] like Figure 1 As shown, a timeline data visualization method based on adaptive dynamic adjustment of data density disclosed in an embodiment of the present invention mainly comprises the following steps:

[0031] S1: Determine the time range of the data displayed on the time axis. Specifically, the time range of the data can be from the task start time to the latest received task data time, or from the task start time to the current time. When the task ends, the time range of the data is from the task start time to the end time.

[0032] During specific implementation, the time range of the data can be calculated by the following processing method. The network range sends the data when the network range task is issued to the front end through the websocket communication method. Each piece of this data contains the task issuance time, task type and task description. When the first piece of data is received, it is immediately recorded as startTime. At the same time, a variable lastTime is maintained to record the latest time in the currently processed data. Whenever new data arrives, lastTime is updated (if the time of the new data is later than lastTime). The timeline can be used to display the data in the time period from startTime to lastTime, and it can also be used to display the data in the time period from startTime to the current time period. Whenever the WebSocket connection is closed or all task data is pushed, endTime is lastTime, thereby realizing real-time and efficient start and end time calculation. In this embodiment, the display within the time range from startTime to endTime is used as an example for explanation.

[0033] S2: Dynamically adjust the interval division interval according to the time span of the data on the time axis, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the time axis.

[0034] In this embodiment, when processing time data, we implement a flexible and efficient interval division strategy. First, the time span in the data packet is comprehensively analyzed, that is, from startTime to endTime. Based on this time range and data distribution characteristics, we dynamically adjust the interval division: if the time span is less than 1 minute, a fine division of 1 second is used to ensure that the data changes in a short time can be clearly displayed. For the time period from 1 minute to 10 minutes, we use a division method of 20 seconds intervals to balance the comprehensiveness of data display and detail clarity. If the time span is between 10 minutes and 1 hour, it is adjusted to a 5-minute interval to meet the data overview requirements in a longer time period. For time periods exceeding 1 hour, it is adjusted to a 30-minute interval, but the last part of less than 30 minutes is ensured to be independent to maintain the integrity of the division. After setting the interval division strategy, we initialize the time axis to N equal-sized intervals based on the total time length T and the total pixel length L of the time axis. The time length of each interval is accurately calculated as T / N, and the corresponding pixel length is L / N.

[0035] S3: Count the amount of data in each interval and the total amount of all data, and calculate the density of each interval relative to the overall data.

[0036] In this embodiment, the main processing method for analyzing data density is as follows: After obtaining N equal-sized time intervals, deeply analyze the data density of each interval to optimize the display effect of the time axis. The specific processing method is as follows: Calculate data density: For each interval i (i = 1 to N), first count the number of data in the interval, recorded as count_i. Then, calculate the total number of all data (across all N intervals), recorded as total_count. The data density d_i is the ratio of count_i to total_count, that is, d_i =

[0037] count_i / total_count. This ratio reflects the density of the data in interval i relative to the overall data.

[0038] S4: Dynamically adjust the pixel length of the interval according to the data density of the interval. When the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced.

[0039] In this embodiment, the method for calculating the interval size and division is as follows: Based on the calculated data density d_i, we no longer simply keep all intervals of equal length on L, but dynamically adjust them. For intervals with higher data density, more pixel length is allocated to better display its data details; conversely, for intervals with lower data density, the pixel length is appropriately reduced to avoid unnecessary blank spaces on the time axis or over-compression of other important information. The adjusted length length_i of interval i on L can be calculated by the following formula:

[0040] length_i=L*(d_i / Σd_j)

[0041] Where Σd_j represents the sum of all interval data densities, ensuring that the sum of all adjusted interval lengths is still equal to L.

[0042] S5: Draw interval marks on the time axis according to the intervals dynamically adjusted according to the pixel length to distinguish different time periods. According to the data density of each interval, assign different colors or color depths to different intervals to intuitively show the density of data distribution.

[0043] In this embodiment, the method of timeline rendering is as follows: Combine the dynamic interval size, data density and time range calculated in the previous step to efficiently draw and display the timeline, using the HTML element ( Element), and set its width and height to adapt to display requirements, initialize DOM elements for subsequent drawing operations. Draw the axis of the timeline, usually a horizontal straight line, indicating the baseline of the timeline. According to the N dynamically adjusted intervals, draw interval marks on the timeline to distinguish different time periods. Mark the start time (startTime) and end time (endTime) of the timeline, usually displayed on the left and right ends of the timeline. According to the data density d_i of each interval, different colors or color depths are assigned to different intervals to intuitively show the density of data distribution. Intervals with higher data density can use darker colors or more eye-catching styles. Traverse all intervals and use the drawing context to draw filled rectangles. The width of each rectangle corresponds to length_i, and the height can be adjusted as needed to maintain visual balance. The corresponding data density can be displayed at the corresponding time point or interval on the timeline, and the data is marked by data identifiers, indicating that there is data displayed at the corresponding moment. The design effect diagram is as follows Figure 2 shown.

[0044] The timeline data visualization method based on adaptive dynamic adjustment of data density described in the embodiment of the present invention has the following advantages:

[0045] Dynamic adaptability: This method can analyze the time range and data density of the data in real time and dynamically adjust the display ratio of the timeline based on this information. This dynamic adaptability ensures that the timeline can provide clear and effective visualization under different data distributions and time spans.

[0046] Efficiency: By receiving data in real time through WebSocket and dynamically calculating the time range, the timeline can instantly reflect the latest data status. At the same time, the dynamic interval division and size adjustment strategy reduces unnecessary calculation and rendering burdens, and improves the overall processing efficiency.

[0047] Information richness: By intelligently analyzing data density, this method ensures that key data is fully displayed while avoiding wasting display space in areas with sparse data. This processing method allows the timeline to display more useful information in a limited space.

[0048] Intuitiveness: By assigning different colors or color shades to intervals with different data densities and displaying them with special markers, the data distribution and change trends on the timeline are more intuitive and easy to understand. Users can quickly identify data-intensive areas and key time points.

[0049] The present invention can provide intuitive and comprehensive data analysis for network security teams, helping them to accurately respond to security threats and optimize the decision-making process.

[0050] Based on the same inventive concept, an embodiment of the present invention discloses a timeline data visualization system based on adaptive dynamic adjustment of data density, including: a time range determination module, used to determine the time range of data displayed on the timeline; a time data analysis module, used to dynamically adjust the interval division interval according to the time span of the data on the timeline, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the timeline; a data density analysis module, used to count the data volume of each interval and the total amount of all data, and calculate the density of each interval relative to the overall data; an interval dynamic adjustment module, used to dynamically adjust the pixel length of the interval according to the data density of the interval, when the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced; a timeline rendering module, used to draw interval marks on the timeline according to the interval after the pixel length is dynamically adjusted to distinguish different time periods, and different colors or color depths are assigned to different intervals according to the data density of each interval to intuitively display the density of data distribution.

[0051] The embodiment of the present invention also discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the timeline data visualization method based on adaptive dynamic adjustment of data density.

Claims

1. A timeline data visualization method based on adaptive dynamic adjustment of data density, characterized in that: The following steps are involved: Determine the time range for displaying data on the timeline; Dynamically adjust the interval division interval according to the time span of the data on the time axis, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the time axis; Count the amount of data in each interval and the total amount of all data, and calculate the density of each interval relative to the overall data; Dynamically adjust the pixel length of the interval according to the data density of the interval. When the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced. According to the intervals dynamically adjusted according to the pixel length, interval marks are drawn on the time axis to distinguish different time periods. According to the data density of each interval, different colors or color depths are assigned to different intervals to intuitively show the density of data distribution.

2. The time axis data visualization method based on adaptive dynamic adjustment of data density according to claim 1 is characterized in that: When dynamically adjusting the interval division interval according to the time span, different time span length ranges and their corresponding interval division intervals are set. As the time span becomes longer, when reaching the next length range from the previous length range, it is dynamically adjusted to the interval division interval corresponding to the next length range; wherein the interval division interval corresponding to the next length range is larger than the interval division interval corresponding to the previous length range.

3. The time axis data visualization method based on adaptive dynamic adjustment of data density according to claim 1 is characterized in that: When the pixel length of an interval is dynamically adjusted according to the data density of the interval, the pixel length length_i of the adjusted interval i on the total pixel length L of the time axis is calculated according to the following formula: length_i=L*(d_i / Σd_j) Where d_i represents the data density of interval i, and Σd_j represents the sum of the data densities of all intervals.

4. The method for visualizing time axis data based on adaptive dynamic adjustment of data density according to claim 1, characterized in that: Receive task data in real time and dynamically calculate the time range to update the timeline.

5. The method for visualizing time axis data based on adaptive dynamic adjustment of data density according to claim 1, characterized in that: When drawing the timeline, use a straight line as the axis of the timeline to represent the baseline of the timeline. Traverse all intervals and draw filled rectangles. The width of each rectangle corresponds to the pixel length of the interval, and the height is adjusted as needed to maintain visual balance.

6. The method for visualizing time axis data based on adaptive dynamic adjustment of data density according to claim 5, characterized in that: The data density of different intervals is displayed on the time axis, and the data is identified by marking the time point when the data is received.

7. A timeline data visualization system based on adaptive dynamic adjustment of data density, characterized in that: include: A time range determination module, used to determine the time range of the data displayed on the time axis; The time data analysis module is used to dynamically adjust the interval division interval according to the time span of the data on the time axis, and determine the time length and pixel length corresponding to each interval according to the total time length and the total pixel length of the time axis; Data density analysis module, used to count the amount of data in each interval and the total amount of all data, and calculate the density of each interval relative to the overall data; The interval dynamic adjustment module is used to dynamically adjust the pixel length of the interval according to the data density of the interval. When the total pixel length remains unchanged, the pixel length of the interval with higher data density is increased, and the pixel length of the interval with lower data density is reduced; The timeline rendering module is used to draw interval marks on the timeline according to the intervals dynamically adjusted according to the pixel length to distinguish different time periods. Different colors or color depths are assigned to different intervals according to the data density of each interval to intuitively show the density of data distribution.

8. The time axis data visualization system based on adaptive dynamic adjustment of data density according to claim 7 is characterized in that: In the time data analysis module, when dynamically adjusting the interval division interval according to the time span, different time span length ranges and their corresponding interval division intervals are set. As the time span becomes longer, when reaching the next length range from the previous length range, it is dynamically adjusted to the interval division interval corresponding to the next length range; wherein the interval division interval corresponding to the next length range is larger than the interval division interval corresponding to the previous length range.

9. The time axis data visualization system based on adaptive dynamic adjustment of data density according to claim 7, characterized in that: In the timeline rendering module, when drawing the timeline, a straight line is used as the axis of the timeline to represent the baseline of the timeline, and all intervals are traversed to draw filled rectangles. The width of each rectangle corresponds to the pixel length of the interval, and the height is adjusted as needed to maintain visual balance.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the timeline data visualization method based on adaptive dynamic adjustment of data density according to any one of claims 1 to 6 are implemented.

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