A simulation display and control method and system for determining task areas

Abstract

The present invention belongs to the field of geographic information and computer graphics technology, and provides a simulation display and control method and system for determining a mission area. The method includes: obtaining spatial geographic data of the area to be detected, performing spatial clustering, and obtaining at least one of the following clusters: a detection type cluster, a geographic location cluster, and calculating the mission area formed by the coordinate points passed within a specified time period based on the execution tasks in the simulation, and further determining the area to be displayed. When the number of the execution tasks is multiple, the overlapping areas between the mission areas are determined to determine the patrol frequency; determining visualization information, grouping different action events, and separately aggregated drawing; calculating the drawing-related information of all points in each mission detection information in the area to be displayed, and drawing the area to be displayed and the mission detection information on the map. The present invention can achieve data aggregation and centralized display of information, and can effectively display multi-dimensional situation information of air confrontation.

Description

Technical Field

[0001] The invention relates to the field of geographic information and computer graphics technology, and relates to a simulation display and control method and system for determining a mission area. Background Art

[0002] Simulation technology typically simulates or simulates adversarial training operations or adversarial gaming activities, aiming to simulate the effects of different tactical scenarios through computer models. In regional patrol missions, simulations can provide different patrol routes, countermeasures, and resource allocations based on mission requirements and environmental characteristics. However, relying solely on numerical values ​​and simulation results is not intuitive enough to help commanders fully understand the battlefield situation.

[0003] Situation visualization technology uses graphical representations to transform complex confrontation data into easily understandable visual information. By visually displaying multi-dimensional data such as patrol routes, resource utilization, and enemy and friendly situation, commanders can gain a clearer understanding of the overall mission execution situation and make more accurate decisions.

[0004] In existing technologies, traditional views are limited in the amount of information they can convey in a single view, failing to meet the multi-dimensional display requirements for simulating complex situations. For example, a single view can only display the current position of an aircraft, while multi-dimensional information such as mission status, formation status, and threat type cannot be presented simultaneously. This single-scale representation requires commanders to frequently switch between multiple views and integrate information from different sources, which increases cognitive burden and operational complexity, slowing down decision-making. In addition, existing technologies lack effective spatial aggregation when presenting information, resulting in scattered data points in the view and a cluttered interface. When analyzing the situation, commanders need to focus on and connect scattered information point by point, making it difficult to quickly obtain an overall overview of the confrontation situation. In addition, the correlation between data points is weak, and important areas or potential threats are not prominent enough, resulting in insufficient support for key decisions.

[0005] Therefore, it is necessary to provide a new simulation display and control method and system for determining a task area to solve the above problems. Summary of the Invention

[0006] The present invention aims to provide a simulation display and control method and system for determining a task area, so as to solve the technical problems in the prior art that a single view cannot express a limited amount of information, cannot meet the multi-dimensional display requirements of complex simulation situations, and requires frequent switching of multiple views to integrate information from different sources, thereby increasing the cognitive burden and operational complexity. The scattered distribution of data points in the view makes it difficult to quickly obtain an overall overview of the adversarial training situation, the correlation between data points is weak, and it is difficult to determine important areas or potential threats. The technical problems to be solved by the present invention are achieved through the following technical solutions.

[0007] The first aspect of the present invention provides a simulation display and control method for determining a task area, which includes: acquiring spatial geographic data of an area to be detected, performing spatial clustering, and obtaining at least one of the following cluster clusters: a detection type cluster, a geographic location cluster cluster, specifically including a method of defining spatial density according to the number of neighbors of a data point; grouping data points containing the same characteristics; calculating the task area formed by the coordinate points passed within a specified time period according to the execution tasks in the simulation, and further determining the area to be displayed, wherein the execution tasks include air patrol tasks, specifically including: when the number of the execution tasks is multiple, determining the overlapping areas between the task areas to determine the patrol frequency; determining visualization information, grouping different action events, and drawing them in aggregate separately; calculating the drawing-related information of all points in each task detection information in the area to be displayed, and drawing the area to be displayed and the task detection information on the map.

[0008] The second aspect of the present invention provides a simulation display and control system for determining a task area, which uses the simulation display and control method for determining a task area described in the first aspect of the present invention. The simulation display and control system includes: an acquisition module for acquiring spatial geographic data of the area to be detected, performing spatial clustering, and obtaining at least one of the following cluster clusters: a detection type cluster, a geographic location cluster cluster, specifically including a method of defining spatial density according to the number of neighbors of a data point; grouping data points containing the same characteristics; a first calculation module, based on the execution task in the simulation, calculating the task area formed by the coordinate points passed within a specified time period, and further determining the area to be displayed, the execution task including an air patrol task, specifically including: when the number of the execution tasks is multiple, determining the overlapping area between the task areas to determine the patrol frequency; determining visualization information, grouping different action events, and drawing them in aggregate separately; a second calculation module, calculating the drawing-related information of all points in each task detection information in the area to be displayed, and drawing the area to be displayed and the task detection information on the map.

[0009] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the simulation display and control method for determining the task area described in the first aspect of the present invention.

[0010] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the simulation display and control method for determining a task area as described in the first aspect of the present invention.

[0011] Beneficial effects of the present invention:

[0012] Compared with the prior art, the present invention obtains spatial geographic data of the area to be detected and performs spatial clustering to obtain at least one of the following cluster clusters: detection type cluster, geographic location cluster cluster. According to the execution tasks in the simulation, the task area formed by the coordinate points passed within the specified time period is calculated, and the area to be displayed is further determined. When the number of the execution tasks is multiple, the overlapping areas between the task areas can be accurately determined to more accurately determine the patrol frequency; determine the visualization information, group different action events, and aggregate and draw them separately; calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map, which can realize data aggregation and centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flowchart of an example of a simulation display and control method for determining a task area according to the present invention;

[0014] Figure 2 is a schematic diagram of an example of forming a to-be-displayed area in the simulation display control method for determining a task area of ​​the present invention;

[0015] Figure 3 is a schematic diagram of an example of dividing clusters in the simulation display and control method for determining a task area of ​​the present invention;

[0016] Figure 4 This is an application example of the simulation display control method for determining the task area of ​​the present invention.

[0017] Schematic diagram;

[0018] Figure 5 This is a schematic diagram of determining clusters with the same event ID in an application example of the simulation display and control method for determining a task area of ​​the present invention;

[0019] Figure 6 is a schematic diagram of an example of a simulation display and control system for determining a task area according to the present invention;

[0020] Figure 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;

[0021] Figure 8 is a schematic structural diagram of an embodiment of a computer-readable medium according to the present invention. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In view of the above problems, the present invention provides a simulation display and control method for determining a mission area. The method obtains spatial geographic data of the area to be detected and performs spatial clustering to obtain at least one of the following clusters: a detection type cluster and a geographic location cluster. Based on the execution tasks in the simulation, the task area formed by the coordinate points passed within a specified time period is calculated to further determine the area to be displayed. When the number of the execution tasks is multiple, the overlapping areas between the task areas can be accurately determined to more accurately determine the patrol frequency; the method determines visualization information and groups different action events for separate aggregation and drawing; the method calculates the drawing-related information of all points in each task detection information in the area to be displayed, and draws the area to be displayed and the task detection information on a map. This method can achieve data aggregation and centralized display of information, effectively display multi-dimensional situation information of air confrontation, and enable users to quickly and accurately analyze data.

[0024] Patrol areas, aircraft numbers, and detected targets are plotted using an aggregated approach. Targets with similar spatial locations are aggregated and displayed, reducing the dimensionality of the spatial data and visualizing it. These targets include opposing unit targets detected during air patrol missions, specifically flight targets, radar targets, and adversary targets. The method of the present invention has a wide range of applications and is particularly suitable for aggregated visual analysis of regional patrol capabilities, particularly for various missions performed in various games, including air patrol missions, multi-aircraft flight missions, and aerial adversary missions.

[0025] Example 1

[0026] Refer to the following Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , the simulation display and control method for determining the task area of ​​the present invention will be described in detail.

[0027] Figure 1 This is a flowchart of an example of a simulation display and control method for determining a task area according to the present invention.

[0028] First, in step S101, spatial geographic data of the area to be detected is obtained, and spatial clustering is performed to obtain at least one of the following clusters: detection type cluster, geographic location cluster, specifically including a method of defining spatial density according to the number of neighbors of data points; data points containing the same characteristics are grouped.

[0029] In a specific embodiment, the spatial geographic data of the area to be inspected is obtained from an area patrol mission (or an air patrol mission, such as an air patrol mission executed in a game) in a simulation process.

[0030] Specifically, spatial aggregation and dimensionality reduction are performed on the spatial geographic data related to the detection information in the area to be detected. Initial classification is performed based on the detection type of each data point's coordinates. Next, spatial clustering is performed on the spatial geographic data based on the distances between the different coordinates of each data point, resulting in at least one of the following clusters: detection type clusters and geographic location clusters. Specifically, detection information where the distance between any two adjacent data points is within a specific range (e.g., within 0.1 degrees of latitude and longitude) is grouped into one category and labeled as a distinct cluster, for example, using an ID.

[0031] More specifically, the detection information includes threat points (specifically points corresponding to different threat types) and coordinate information.

[0032] In this embodiment, the threat types include simulated aircraft, simulated radar, simulated countermeasure threat, and simulated ship.

[0033] To obtain the spatial and geographic data of the area to be inspected, selectively extracting only the columns needed for spatiotemporal data visualization from a large number of database columns can effectively reduce unnecessary data transmission and processing, thereby achieving efficient data utilization. For example, by connecting to the database through libraries such as cx_Oracle and executing SQL queries (e.g., SQL queries containing keywords or key terms related to the inspection information), large-scale spatial and geographic data can be obtained. Specifically, the sanic framework is used to extract the required fields from the database, organize the extracted data into JSON data, and submit it to relevant modules (such as data visualization modules) for subsequent data processing, effectively avoiding unnecessary data transmission and processing.

[0034] For example, the utilization data field is as follows (the following values ​​are examples only):

[0035] {

[0036] "unique_id": "2",

[0037] "number_launched": "6",

[0038] "now_latitude": "89.12642858",

[0039] "now_longitude": "0.7631882",

[0040] "detection _type": "COMBAT",

[0041] }.

[0042] For spatial clustering, it specifically includes defining the spatial density based on the number of neighbors of data points; grouping data points with the same characteristics.

[0043] Specifically, the DBSCAN algorithm in Python is used to define the spatial density according to the number of neighbors of data points. The number of various clusters can be determined in real time, and clusters with irregular shapes in space can be found. Therefore, the spatial dimensionality reduction of the detection information can be effectively achieved.

[0044] There are two main parameters for the DBSCAN algorithm: the neighborhood radius (i.e., eps) and the minimum number of points (i.e., MinPts). eps defines the maximum distance between data points; if the distance is less than or equal to eps, the points are considered neighbors. If the eps value is set too small, most of the data will be treated as noise; if it is set too large, multiple clusters will be merged, resulting in most data points being classified as the same cluster. MinPts refers to the minimum number of points required within the neighborhood of eps to form a dense region. Specifically, the value of MinPts should be selected based on the dimensionality of the dataset. Optionally, MinPts>= D+1, where D is the dimensionality of the dataset.

[0045] Specifically, for the area to be inspected, an arbitrary point (e.g., O) is selected from the detection information (specifically, threat points and coordinate information) within the area to be inspected. A specified distance (e.g., e, i.e., specified distance e) and a specified number of points in the same cluster (e.g., m) are determined based on the air patrol mission. The distances between point O and other points in the area to be inspected are calculated and compared with the specified distance e. If the calculated distance is less than or equal to the specified distance e, the point corresponding to that distance is determined to belong to the same cluster as point O. If the calculated distance is greater than the specified distance e, the point corresponding to that distance is determined to not belong to the same cluster as point O. If the distance between a point and point O is determined to be less than or equal to the specified distance e, and the number of data points in the current cluster is greater than a specified number m, cluster expansion is performed, i.e., the current cluster is expanded. Specifically, the current cluster is recursively grown. If the distance between a point and point O is determined to be greater than the specified distance e, and the number of data points in the current cluster is greater than a specified number m, the point is determined not to belong to the current cluster, and cluster expansion is not performed.

[0046] The regions to be displayed are determined according to the clusters determined above, that is, each cluster corresponds to a region to be displayed.

[0047] Specifically, the above clustering method is used to determine Figure 2The area to be displayed is shown.

[0048] In combination with the current application scenario, when it is determined that the area to be detected contains threat points, that is, when the data field detection_type (threat type) is not empty, classification is performed based on the value of the data field detection_type, and then clustering is performed based on the coordinates of the data points within the classification. For example, cluster all points whose data field detection_type value is "COMBAT". For the clustering results of these points, Figure 4 As shown in the figure, dots are used to represent the first type of threat points (for example, simulated radars), and triangles are used to represent the second type of threat points (for example, simulated aircraft). Combined with the latitude and longitude coordinates of these points (corresponding to "now_latitude" and "now_longitude" in the data field), by adjusting the neighborhood range threshold (for example, using eps) and the minimum sample value (for example, using min_samples), areas with sufficient density can be divided into the same cluster. At the same time, each coordinate point in the same cluster is assigned the same cluster value (i.e., cluster value), as shown in the figure. Figure 3 The dots framed in the figure belong to the same cluster, where the cluster of all dots is 1 and is represented by cluster 1. Figure 3 The triangles enclosed in the figure are the same cluster, where all points have cluster 2 and are represented by cluster 2. Clusters 1 and 2 have different threat types. Specifically, the threat types include simulated aircraft, simulated radar, simulated countermeasures, and simulated ships.

[0049] Specifically, the values ​​of eps and min_samples are, for example, 0.1 and 4, respectively.

[0050] Furthermore, based on the divided clusters, a JSON field file with the following format and meaning can be obtained. See Table 1 for details:

[0051] Table 1

[0052]

[0053] Table 1 shows the relevant information data of the area to be displayed and the corresponding plotting pattern.

[0054] Specifically, the events include patrol events, strike events, and the like.

[0055] In this example, the event is a patrol event, wherein different patrol event tasks have different ids, namely event ids, which are specifically the primary keys of a patrol task.

[0056] It should be noted that, in other implementations, the events also include command issuing events, aiming events, reconnaissance target discovery events, strike events, damage events, etc.

[0057] It should be noted that, in the present invention, the confrontation situation data specifically refers to relevant data to be displayed during the simulation based on mission scenarios (specifically, regional patrol missions or other aerial missions) or overall presentation requirements such as multi-information fusion. Each data point corresponds to one or more data points, and the confrontation situation data includes spatial geographic data. For example, in a regional patrol mission scenario, the confrontation situation data specifically includes multi-dimensional information data such as the regional patrol mission status, number of aircraft, formation information, threat points, and threat types. Based on the entity attributes corresponding to each piece in the confrontation situation data, the simulation data of each piece is mapped from high-dimensional space to a two-dimensional matrix in two-dimensional space. The pieces are used to represent confrontation entity information such as aircraft and personnel participating in the regional patrol mission, infrastructure, confrontation objects, and confrontation targets. The above is provided as an optional example and should not be construed as limiting the present invention.

[0058] Next, in step S102, based on the execution tasks in the simulation, the task area formed by the coordinate points passed within the specified time period is calculated, and the area to be displayed is further determined. The execution tasks include air patrol tasks, specifically including: when the number of the execution tasks is multiple, determining the overlapping areas between the task areas to determine the patrol frequency; determining the visualization information, grouping different action events, and drawing them in aggregate separately.

[0059] In the example of performing an air patrol mission, the mission area formed by the coordinate points passed within a specified time period is calculated, and the area to be displayed is further determined.

[0060] Specifically, for the party performing the air patrol mission, the aircraft is kept in a specific period of time (the entire process of performing the air patrol mission, for example, from the initial time t0 to the end time t 终 ) to perform time dimension reduction on all coordinate points passed by the aircraft. The position of the aircraft landing in each second is a coordinate point, and within a period of time (from the initial time t0 to the end time t 终 ) to form the coordinate points of an area (i.e., the mission area). The envelope of these coordinate points is calculated to determine the display area. The border of the display area is determined by the number of aircraft; the larger the number of aircraft, the thicker the border.

[0061] Preferably, the determined mission area is filled with colors of different transparency to represent the patrol frequency of the mission area, for example, green with opacity is used for area filling.

[0062] In an optional embodiment, when the number of tasks to be executed is multiple (that is, when four air patrol tasks are executed, specifically refer to Figure 4 ), determine the display areas to be shown corresponding to the four air patrol tasks (that is, four display areas to be shown), and determine the overlapping areas between the task areas to determine the patrol frequency.

[0063] When the areas of multiple air patrol tasks overlap, the opacity filled will be superimposed. Specifically, determine the opacity of the overlapping area to determine the patrol frequency. Specifically include: determine the final thickness value of the visual line segments of the task areas according to the number of aircraft; determine the opacity of the overlapping area when the areas of multiple air patrol tasks overlap to determine the patrol frequency.

[0064] First determine the final thickness value of the visual line segments of the task areas, specifically determine the final thickness value of the visual line segments of each task area on the map.

[0065] Specifically obtain the launch quantity of the aircraft (for example, represented by airLaunched), set the initial scaling factor of the aircraft (for example, represented by ariNum), and calculate the width of the area border of the task area on the map by using the following expression, so as to represent the number and performance of the aircraft in the image shown on the visual interface through the area border width.

[0066] In the air patrol task, the number of aircraft is from 1 to 10, optionally, the number of aircraft is from 1 to 3. When the user selects a larger number of aircraft, it can ensure that the data visualization visual effect is still good. Therefore, the following piecewise function method is adopted for processing.

[0067] It should be noted that in other embodiments, the number of aircraft can also be more than 10, such as 11, 12 or more. The above is only for illustrative purposes as an optional example and should not be construed as a limitation to the present invention.

[0068] The piecewise function is used to dynamically adjust the initial thickness of the line segments in data visualization (represented by y). According to the value range of the input parameter, that is, the number of aircraft x (a positive integer), different mathematical strategies are adopted to control the thickness change and balance the visualization effect and data readability. Therefore, it can effectively improve the visualization effect of data on the map. The specific piecewise definition and technical principle are as follows:

[0069] For the first interval of the piecewise function: 0 < x ≤ 3, the expression of the thickness value of the visual line segment and the number of aircraft is:

[0070] y = x

[0071] Where, y represents the thickness value of the visual line segment when the number of aircraft x is in the first interval 0 < x ≤ 3; x represents the number of aircraft in the current air patrol task.

[0072] Specifically, this first interval shows linear growth. When the number of aircraft x is less than or equal to 3 (such as 1, 2, 3), the thickness value of the visualized line segment has a simple direct proportional relationship with the number of aircraft x. For example, when x = 3, y = 3.

[0073] It should be noted that the linear relationship in the first interval facilitates the user to quickly understand the direct association between the parameter and the visual effect, which is in line with the scenario where the number of aircraft emphasizes low-value differences.

[0074] For the second interval of the piecewise function: 3 < x ≤ 10, the expression for the initial thickness value of the visualized line segment in relation to the number of aircraft is:

[0075] y = 2 * x 1 / 2

[0076] Where y represents the initial thickness value of the visualized line segment when the number of aircraft x is in the second interval 3 < x ≤ 10; x represents the number of aircraft in the current air patrol mission.

[0077] Specifically, the piecewise function in this second interval shows decelerated growth. By using the square root function, the growth rate of the initial thickness value y of the visualized line segment is slowed down, preventing the line segment from being too thick when the number of aircraft x is at a medium value. For example, when x = 10, y ≈ 6.32.

[0078] The piecewise function in this second interval has the effect of smooth transition. Due to the derivative decreasing property of the square root function, it can effectively ensure that the thickness value of the visualized line segment gradually levels off as the number of aircraft x increases, effectively preventing abrupt changes in the visualization.

[0079] For the third interval of the piecewise function: x > 10, the expression for the initial thickness value of the visualized line segment in relation to the number of aircraft is:

[0080] y = 10 * (1 - e −(x-9) )

[0081] Where y represents the initial thickness value of the visualized line segment when the number of aircraft x is in the third interval x > 10; x represents the number of aircraft in the current air patrol mission.

[0082] When the number of aircraft x is greater than 10, using the above piecewise function can play a role in saturation limitation.

[0083] Specifically, when the number of aircraft x is greater than 10, the initial thickness value y of the visualized line segment approaches the maximum value of 10 (such as when x = 19, y ≈ 9.99955). Through the above piecewise function, it can effectively prevent the line segment from being too thick and affecting readability.

[0084] By using the exponential function, it can play a role in adaptive adjustment, that is, the exponential function (1 - e −(x-9)) can effectively ensure that the growth rate of the thickness of the visualization line segment approaches zero when it approaches the upper limit, thereby achieving a natural and smooth saturation effect.

[0085] It should be noted that after obtaining the initial line segment thickness according to the piecewise functions of the above segments, the line segment thickness should be adaptively adjusted in combination with the screen size, map size, etc. to ensure the readability and aesthetics of the data visualization.

[0086] According to the above piecewise function, it is divided into three piecewise functions according to the number of aircraft. The following expression is used to calculate the final thickness of the visualization segment:

[0087] airWidth = y * mapScale * ariScale

[0088] Among them, airWidth represents the width of the regional border of the mission area of ​​the current air patrol mission in the map, that is, the final thickness of the visualization line segment; mapScale represents the zoom factor of the map, which is calculated according to mapScale = zoomNow / zoom0, where zoomNow represents the current zoom level and can be obtained from the visualization system; zoom0 represents the initial zoom level, for example, a fixed value; airScale represents the zoom factor of the aircraft, which is calculated according to the device pixel ratio, map container width, etc., airScale = pixelRatio * (containerWidthNow / containerWidth0), where containerWidthNow represents the current map container width and can be obtained through the front-end function; containerWidth0 represents the base map container width, for example, a fixed value, such as 1200px; pixelRatio can be obtained through window.devicePixelRatio.

[0089] In another optional implementation, the following expression is used to calculate the final thickness value of the visual line segment:

[0090] Where W represents the width of the border of the current air patrol mission in the mission area on the map, that is, the final thickness of the visualization line segment; i represents the i-th aircraft, i is a positive integer; n represents the number of aircraft in the current air patrol mission, that is, the number of aircraft launched in the current air patrol mission, that is, the number of aircraft; aircraft performance factor (performanceFactor i) is assigned a value based on the model of the i-th aircraft (e.g., 1.1 for a reconnaissance aircraft, 1.3 for a fighter aircraft, etc.); scale0 represents the map zoom factor; ariNum represents the initial zoom factor of the aircraft; adjustFactor represents the adjustment coefficient, which is a constant value ranging from 0.5 to 2. For example, if the constant value is 1, the user can fine-tune the border width in the picture by adjusting the adjustment coefficient to ensure that the border visual effect is balanced and in line with user preferences and aesthetics, thereby improving the map visualization effect while enhancing the user experience.

[0091] By introducing a logarithmic function and an adjustment factor, the above formula ensures that the border width is sufficiently clear when the number of aircraft is small, and avoids excessive distortion when the number of aircraft is large, thereby achieving a more effective and balanced visualization effect over a large mission area.

[0092] Furthermore, the visualization information is determined to group different action events.

[0093] Specifically determine the area to be displayed, simulated aircraft, whether there are threat points, etc. corresponding to the air patrol mission.

[0094] For aggregate drawing, the mission area of ​​the current air patrol mission, the width of the region border of each mission area, and the threat points are drawn, where the threat points serve as mission detection information.

[0095] Specifically, obtain the coordinate data of the aircraft in the current air patrol mission and map it to a two-dimensional array of points on the map (for example, using newPoints). Use the polygon envelope (i.e., polygonHull) function of a visual data processing library (such as library D3) to calculate the convex hull of each data point in the aircraft's coordinate data. The convex hull is the smallest polygon that contains all points, specifically all points passed by the aircraft with the same event ID, such as Figure 5 All points of event id1 are summed to obtain a convex hull array (e.g., using a hull representation). Each convex hull corresponds to an area to be displayed. Next, a curve generator is created using a visualization data processing library (e.g., library D3) to generate smooth closed curves corresponding to each area to be displayed. The smoothness of the generated smooth closed curve can be modified by adjusting the values ​​of control parameters. A closed path is generated based on the curve, and the area within this path is the area to be displayed (also called the patrol area in this example).

[0096] For the visualization of overlapping areas of multiple tasks, since there's no upper limit on the number of air patrol tasks, while direct overlaying has a certain opacity limit (100%), further processing is performed to display overlapping areas. For example, for overlapping areas of multiple tasks, consider the following example: For air patrol task a's patrol area, traverse the convex hull array of air patrol task a and determine whether any other air patrol tasks, such as data points from air patrol task b, appear within the detection range G of data point α in the convex hull array. If so, it indicates that air patrol tasks a and air patrol tasks b overlap. Next, traverse the points surrounding data point α in air patrol task a to determine whether any data points from air patrol task b exist. If so, add data point α and any data points located within data point α that belong to air patrol task b to a new overlapping area group. If not, terminate the traversal. Repeat the calculation according to the above rules until the overlapping area of ​​air patrol tasks a and b is obtained. At this point, the patrol frequency of this overlapping area is 2.

[0097] Specifically, the detection range G is determined according to the maximum detection range of the simulated aircraft model for the target to be detected (such as a stealth target). For example, the maximum detection range of a Class Q simulated aircraft is 100 km.

[0098] After determining the air patrol area, determine the fill color for that area. For a single area, the color opacity is a fixed value (e.g., 10%); for multiple areas, the color is filled using a scoring method, where the maximum and minimum opacities are both fixed values ​​(e.g., 10% and 60%). For example, if the patrol frequency of an overlapping area C is 3, and the maximum patrol frequency in the entire visible graph is 5, the following expression is used to calculate the opacity of the current overlapping area to represent the patrol frequency of the current overlapping area:

[0099] The opacity of the current overlapping area = the minimum opacity of multiple air patrol areas + (the maximum opacity of the air patrol area - the minimum opacity of the air patrol area) * (the patrol frequency of the current overlapping area - the minimum patrol frequency of the entire visible image) / (the maximum patrol frequency of the entire visible image - the minimum patrol frequency of the entire visible image).

[0100] The above expression is used to calculate the opacity of the patrol frequency in overlapping area C: 10 + (60 - 10) * (3 - 1) / (5 - 1) = 22.5. This ensures a specific opacity when there are no overlapping areas, allowing individual areas to be identified. This effectively avoids the excessive opacity that can occur when there are many overlapping areas and high patrol frequencies, as well as the lack of clarity in the map due to obscured areas. This allows for more effective and balanced map visualization in multiple air patrol missions.

[0101] It should be noted that, in this example, the entire visualization image refers to the display image of the determined area to be displayed on the map. The above is only described as an optional example and cannot be understood as a limitation of the present invention.

[0102] Next, in step S103 , the drawing related information of all points in each task detection information in the area to be displayed is calculated, and the area to be displayed is drawn on the map.

[0103] Specifically, the JSON data processed in step S101 is selected, and the data of each cluster is correspondingly formed into a point set for subsequent drawing, calculation and analysis.

[0104] After calculating the width of the border of the area to be displayed (i.e., the task area of ​​the current air patrol task in the map), i.e., the final thickness of the visualization line segment, the drawing-related information of all points in each task detection information in the area to be displayed is calculated.

[0105] Specifically, the processed drawing data (all points) are traversed, and the convex hull of all points in each task detection information in each array of the drawing data is calculated to obtain a convex hull array. For example, d3.polygonHull is used to calculate the convex hull of all points in each task detection information in each array of the drawing data to obtain a convex hull array (for example, using convexHull).

[0106] Specifically, each convex hull is a convex polygon formed by connecting the outermost points. It can contain all the points within the convex polygon, forming a corresponding point set, namely the convex hull array.

[0107] Next, calculate the center point of each convex hull to determine the coordinates of the plotted pattern. Calculate the area of ​​each convex hull to determine the size of the plotted pattern on the map. Specifically, calculate the center point of each convex hull using, for example, d3.polygonCentroid(convexHull) (e.g., using centroid). This calculated center point serves as the coordinates of the plotted pattern on the map. Calculate the area of ​​the convex hull (using area) and multiply it by scale0 to obtain the size of the plotted pattern on the map (using imageSize).

[0108] Specifically, for example, the convex hull b corresponds to a simulated airplane, and the convex hull d corresponds to a simulated confrontation point, which is, for example, a device.

[0109] Draw the area to be displayed and the corresponding plotting pattern on the map, and the relevant information data of the plotting pattern (specifically including various data corresponding to the detection information, the detection information including the coordinate information of the threat point and the threat type), see Table 1 above for details.

[0110] To plot the display area, we need to determine the pattern, coordinates, and size of the area to be displayed. The pattern is determined by the threat type of each cluster, the coordinates are determined by the center point of each cluster, and the size is related to the distribution of clusters, both of which are related to the coordinates of the cluster's midpoint. Next, we will process the initial data to obtain the pattern of the area to be displayed, the cluster type of each cluster, and the coordinates of all points in each cluster.

[0111] In one specific implementation, the entire JSON data is traversed. According to Table 1, the coordinates of threat points with the same non-empty category value (i.e., cluster value) and the same threat type (i.e., detection_type) are placed into the coordinates array of the corresponding clusterType. For example, the coordinates of all points with a cluster value of 1 and the same threat type (e.g., COMBAT) are used to obtain the following coordinate array (i.e., coordinates): [[25.30, 120.40], [25.33, 120.41].... ], with threat type: 1. Specifically, the threat type is matched against the path of the pattern in the area to be displayed to obtain threatType: "pics / combat.png". This unit is called a task detection information, which specifically includes the pattern in the area to be displayed, the cluster type, and the coordinate set of the point. For example: {

[0112] coordinates: [[25.30, 120.40], [25.33, 120.41]....], clusterType: 1, threatType: "pics / combat.png"}.

[0113] The resulting drawn data is then visualized on a map, where the x-coordinate of each cluster is the x-coordinate of the center point minus the size of the area to be displayed, and the y-coordinate of the center point minus the size of the area to be displayed. The address of each area to be displayed is path, and the length and width of the area to be displayed are both info.imageSize. This allows the resulting drawn data to be visualized on a map.

[0114] It should be noted that the above is merely provided as an optional example and should not be construed as a limitation to the present invention.

[0115] Compared with the prior art, the present invention obtains spatial geographic data of the area to be detected and performs spatial clustering to obtain at least one of the following cluster clusters: detection type cluster, geographic location cluster cluster. According to the execution tasks in the simulation, the task area formed by the coordinate points passed within the specified time period is calculated, and the area to be displayed is further determined. When the number of the execution tasks is multiple, the overlapping areas between the task areas can be accurately determined to more accurately determine the patrol frequency; determine the visualization information, group different action events, and aggregate and draw them separately; calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map, which can realize data aggregation and centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation.

[0116] Example 2

[0117] The following are system embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the system embodiments of the present invention, please refer to the method embodiments of the present invention.

[0118] Figure 6 2 is a schematic structural diagram of an example of a simulation display and control system for determining a task area according to the present invention.

[0119] The following will refer to Figure 6 , a simulation display and control system is described, which uses the simulation display and control method for determining the task area described in the first aspect of the present invention.

[0120] like Figure 6 As shown, the simulation display and control system 500 includes an acquisition module 510 , a first determination module 520 , and a second determination module 530 .

[0121] In a specific embodiment, the acquisition module 510 is used to obtain the spatial geographic data of the area to be detected, perform spatial clustering, and obtain at least one of the following clusters: detection type clusters, geographic location clusters, specifically including a method of defining spatial density based on the number of neighbors of data points; grouping data points containing the same characteristics. The first calculation module 520 calculates the task area formed by the coordinate points passed within a specified time period based on the execution tasks in the simulation, and further determines the area to be displayed. The execution tasks include air patrol tasks, specifically including: when the number of the execution tasks is multiple, determining the overlapping areas between the task areas to determine the patrol frequency; determining visualization information, grouping different action events, and drawing them in aggregate separately. The second calculation module 530 is used to calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map.

[0122] According to an optional implementation, when the number of tasks to be executed is multiple, determine the final thickness value of the visualization line segment of the task area, and determine the overlapping area between task areas to determine the patrol frequency.

[0123] For the multi-segment piecewise function divided according to the number of aircraft, the following expression is used to calculate the final thickness value of the visualization line segment:

[0124] airWidth = y*mapScale*ariScale

[0125] Where, airWidth represents the width of the area border of the task area of the current air patrol task on the map, that is, the final thickness value of the visualization line segment; mapScale represents the zoom factor of the map, calculated according to mapScale = zoomNow / zoom0, where, zoomNow represents the current zoom level, which can be obtained from the visualization system; zoom0 represents the initial zoom level; ariScale represents the zoom factor of the aircraft, calculated according to the device pixel ratio and the width of the map container, ariScale = pixelRatio *(containerWidthNow / containerWidth0), where, containerWidthNow represents the current width of the map container, which can be obtained through the front-end function; containerWidth0 represents the reference map container width; pixelRatio can be obtained through window.devicePixelRatio.

[0126] According to an optional implementation, it is divided into a three-segment piecewise function according to the number of aircraft, specifically including:

[0127] For the first segment piecewise function: 0 < x ≤ 3, the expression of the thickness value of the visualization line segment and the number of aircraft is:

[0128] y = x

[0129] Where, y represents the thickness value of the visualization line segment when the number of aircraft x is in the first interval 0 < x ≤ 3; x represents the number of aircraft of the current air patrol task.

[0130] For the second segment piecewise function: 3 < x ≤ 10, the expression of the initial thickness value of the visualization line segment and the number of aircraft is:

[0131] y = 2 * x 1 / 2

[0132] Where, y represents the initial thickness value of the visualization line segment when the number of aircraft x is in the second interval 3 < x ≤ 10; x represents the number of aircraft of the current air patrol task.

[0133] For the third segment of the piecewise function: x>10, the expression of the initial thickness of the visualization line segment and the number of aircraft is:

[0134] y=10*(1−e −(x-9) )

[0135] Where y represents the initial thickness of the visualization line segment when the number of aircraft x is in the third interval x>10; x represents the number of aircraft in the current air patrol mission.

[0136] According to an optional implementation, when there are multiple tasks to be performed, the overlapping areas between the task areas are determined to determine the patrol frequency.

[0137] To determine the patrol frequency, the patrol frequency of the overlapping area between the task areas is calculated based on the overlapping area and the number of overlaps.

[0138] The following expression is used to calculate the opacity of the current overlapping area to represent the patrol degree of the current overlapping area:

[0139] The opacity of the current overlapping area = the minimum opacity of multiple air patrol areas + (the maximum opacity of the air patrol area - the minimum opacity of the air patrol area) * (the patrol frequency of the current overlapping area - the minimum patrol frequency of the entire visible image) / (the maximum patrol frequency of the entire visible image - the minimum patrol frequency of the entire visible image).

[0140] According to an optional embodiment, the calculating of the drawing related information of all points in each task detection information in the area to be displayed, and drawing the area to be displayed and the task detection information on the map specifically includes the following steps:

[0141] The processed drawing data is traversed, and the convex hull of all points in each object in each array in the drawing data is calculated to obtain a convex hull array.

[0142] Calculate the center point of each convex hull, which is the coordinate of the plotted pattern.

[0143] The area of ​​each convex hull is calculated to further determine the size of the plotted pattern on the map.

[0144] According to an optional embodiment, the DBSCAN algorithm is used to determine the number of various clusters in real time by defining the spatial density according to the number of neighbors of a data point.

[0145] According to an optional implementation manner, grouping the data points containing the same characteristics includes grouping according to the number of detected aircraft, coordinate information of the aircraft, threat type, spatial shape, etc.

[0146] It should be noted that due to Figure 6The simulation display and control method executed by the simulation display and control system Figure 1 The simulation display and control methods in the examples are roughly the same, so the description of the same parts is omitted.

[0147] Compared with the prior art, the present invention obtains spatial geographic data of the area to be detected and performs spatial clustering to obtain at least one of the following cluster clusters: detection type cluster, geographic location cluster cluster. According to the execution tasks in the simulation, the task area formed by the coordinate points passed within the specified time period is calculated, and the area to be displayed is further determined. When the number of the execution tasks is multiple, the overlapping areas between the task areas can be accurately determined to more accurately determine the patrol frequency; determine the visualization information, group different action events, and aggregate and draw them separately; calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map, which can realize data aggregation and centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation.

[0148] Example 3

[0149] Figure 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

[0150] like Figure 7 As shown, the electronic device is implemented as a general-purpose computing device. The processor may be one or multiple processors working in concert. The present invention also does not exclude distributed processing, meaning that the processors may be dispersed across different physical devices. The electronic device of the present invention is not limited to a single entity but may also be the sum of multiple physical devices.

[0151] The memory stores a computer executable program, typically a machine-readable code, which can be executed by the processor to enable the electronic device to perform the method of the present invention, or at least some of the steps in the method.

[0152] The memory includes a volatile memory, such as a random access memory unit (RAM) and / or a cache memory unit, and may also be a non-volatile memory, such as a read-only memory unit (ROM).

[0153] Optionally, in this embodiment, the electronic device further includes an I / O interface for exchanging data with an external device. The I / O interface may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0154] It should be understood that Figure 7The electronic device shown is merely an example of the present invention. The electronic device of the present invention may also include elements or components not shown in the above examples. For example, some electronic devices also include display units such as screens, and some electronic devices also include human-computer interaction elements such as buttons and keyboards. As long as the electronic device can execute a computer-readable program stored in its memory to implement the method of the present invention or at least some of the steps of the method, it is considered an electronic device covered by the present invention.

[0155] Through the above description of the embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Figure 8 As shown, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of commands to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present invention.

[0156] The software product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0157] The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with a command execution system, device, or component. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0158] Program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] The computer-readable medium carries one or more programs. When the one or more programs are executed by a device, the computer-readable medium implements the data interaction method of the present disclosure.

[0160] Those skilled in the art will appreciate that the modules described above can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The modules of the above embodiment can be combined into one module or further divided into multiple submodules.

[0161] From the above description of the embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes commands that cause a computing device (such as a personal computer, server, mobile terminal, or network device) to execute the methods according to the embodiments of the present invention.

[0162] It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs.

[0163] In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless the context dictates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

[0164] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A simulation display and control method for determining a task area, characterized in that: Including: Obtain the spatial geographical data of the area to be detected, perform spatial clustering, and obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster, specifically including the method of defining spatial density according to the number of data point neighbors; group the data points with the same features; According to the tasks executed in the simulation, calculate the task area formed by the coordinate points passed within a specified time period, and further determine the area to be displayed. The executed tasks include air patrol tasks, specifically including: when the number of executed tasks is multiple, determine the overlapping area between task areas to determine the patrol frequency; determine the visualization information, group different action events, and aggregate and draw them separately; when the number of executed tasks is multiple, determine the final thickness value of the visualization line segment of the task area, determine the overlapping area between task areas to determine the patrol frequency; According to the piecewise function divided by the number of aircraft, use the following expression to calculate the final thickness value of the visualization line segment: airWidth = y*mapScale*ariScale Where, airWidth represents the width of the area border of the task area of the current air patrol task on the map, that is, the final thickness value of the visualization line segment; mapScale represents the zoom factor of the map, calculated according to mapScale = zoomNow / zoom0, where, zoomNow represents the current zoom level, which can be obtained from the visualization system; zoom0 represents the initial zoom level; ariScale represents the zoom factor of the aircraft, calculated according to the device pixel ratio and the width of the map container, ariScale = pixelRatio *(containerWidthNow / containerWidth0), where, containerWidthNow represents the current width of the map container, which can be obtained through the front-end function; containerWidth0 represents the reference map container width; pixelRatio can be obtained through window.devicePixelRatio; Calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map.

2. The simulation display and control method for determining a task area according to claim 1, characterized in that: Further including: Divided into a three-segment piecewise function according to the number of aircraft, specifically including: For the first piecewise function: 0 < x ≤ 3, the expression of the thickness value of the visualization line segment and the number of aircraft is: y = x Where, y represents the thickness value of the visualization line segment when the number of aircraft x is in the first interval 0 < x ≤ 3; x represents the number of aircraft of the current air patrol task; For the second piecewise function: 3 < x ≤ 10, the expression of the initial thickness value of the visualization line segment and the number of aircraft is: y=2*x 1 / 2 Where, y represents the initial thickness value of the visualization line segment when the number of aircraft x is in the second interval 3 < x ≤ 10; x represents the number of aircraft of the current air patrol task; For the third piecewise function: x > 10, the expression of the initial thickness value of the visualization line segment and the number of aircraft is: y=10*(1-e −(x-9) ) Where y represents the initial thickness of the visualization line segment when the number of aircraft x is in the third interval x>10; x represents the number of aircraft in the current air patrol mission.

3. The simulation display and control method for determining a task area according to claim 1, characterized in that: Determining the patrol frequency includes: The patrol frequency of the overlapping areas between mission areas is calculated based on the overlapping areas and the number of overlapping times; The following expression is used to calculate the opacity of the current overlapping area to represent the patrol frequency of the current overlapping area: The opacity of the current overlapping area = the minimum opacity of multiple air patrol areas + (the maximum opacity of the air patrol area - the minimum opacity of the air patrol area) * (the patrol frequency of the current overlapping area - the minimum patrol frequency of the entire visible image) / (the maximum patrol frequency of the entire visible image - the minimum patrol frequency of the entire visible image).

4. The simulation display and control method for determining a task area according to claim 1, characterized in that: The step of calculating the drawing related information of all points in each task detection information in the area to be displayed and drawing the area to be displayed and the task detection information on the map specifically includes the following steps: Traversing the processed drawing data, calculating the convex hull of all points in each object in each array of the drawing data, and obtaining a convex hull array; Calculate the center point of each convex hull, which is the coordinate of the plotted pattern; The area of ​​each convex hull is calculated to further determine the size of the plotted pattern on the map.

5. The simulation display and control method for determining a task area according to claim 1, characterized in that: include: The DBSCAN algorithm is used to define the spatial density based on the number of neighbors of data points, and the number of various clusters is determined in real time.

6. The simulation display and control method for determining a task area according to claim 1, characterized in that: The data points containing the same features are grouped, including: The aircraft are grouped according to the number of detected aircraft, aircraft coordinate information, threat type, spatial shape, etc.

7. A simulation display and control system for determining a task area, characterized in that: It uses the simulation display and control method for determining a task area according to any one of claims 1 to 6, and the simulation display and control system for determining a task area includes: An acquisition module is used to obtain spatial geographic data of the area to be detected and perform spatial clustering to obtain at least one of the following clusters: a detection type cluster and a geographic location cluster, specifically including a method of defining spatial density based on the number of neighbors of a data point; and grouping data points containing the same characteristics; A first calculation module calculates a task area formed by coordinate points passed within a specified time period based on execution tasks in the simulation, and further determines an area to be displayed. The execution tasks include air patrol tasks. Specifically, when there are multiple execution tasks, the module determines overlapping areas between task areas to determine patrol frequency; determines visualization information, groups different action events, and draws them in aggregate; and further determines a final thickness value of a visualization line segment in the task area. The multi-segment piecewise function is divided according to the number of aircraft, and the final thickness of the visualization line segment is calculated using the following expression: airWidth = y*mapScale*ariScale Among them, airWidth represents the width of the regional border of the task area of the current air patrol task on the map, that is, the final thickness value of the visualized line segment; mapScale represents the zoom factor of the map, calculated according to mapScale = zoomNow / zoom0, where zoomNow represents the current zoom level and can be obtained from the visualization system; zoom0 represents the initial zoom level; ariScale represents the zoom factor of the aircraft, calculated according to the device pixel ratio and the width of the map container, ariScale = pixelRatio * (containerWidthNow / containerWidth0), where containerWidthNow represents the current width of the map container and can be obtained through a front-end function; containerWidth0 represents the reference map container width; pixelRatio can be obtained through window.devicePixelRatio The second calculation module is used to calculate the drawing-related information of all points in each task detection information in the area to be displayed, and draw the area to be displayed and the task detection information on the map.

8. The simulation display and control system for determining the task area according to claim 7 further includes:[[]] Divided into three-piecewise functions according to the number of aircraft, specifically including:[[]] For the first piecewise function: 0 < x ≤ 3, the expression of the thickness value of the visualized line segment and the number of aircraft is:[[]] y = x Among them, y represents the thickness value of the visualized line segment when the number of aircraft x is in the first interval 0 < x ≤ 3; x represents the number of aircraft in the current air patrol task. For the second piecewise function: 3 < x ≤ 10, the expression of the initial thickness value of the visualized line segment and the number of aircraft is:[[]] y=2*x 1 / 2 Among them, y represents the initial thickness value of the visualized line segment when the number of aircraft x is in the second interval 3 < x ≤ 10; x represents the number of aircraft in the current air patrol task. For the third piecewise function: x > 10, the expression of the initial thickness value of the visualized line segment and the number of aircraft is:[[]] y=10*(1-e −(x-9) ) Among them, y represents the initial thickness value of the visualized line segment when the number of aircraft x is in the third interval x > 10; x represents the number of aircraft in the current air patrol task.

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