Simulation display control method and system for determining task area

Abstract

The invention belongs to the technical field of geographic information and computer graphics, and provides a simulation display control method and system for determining a task area. The method comprises the following steps: acquiring spatial geographic data of a to-be-detected area, performing spatial clustering to obtain at least one of the following clusters: a detection type cluster and a geographic position cluster, calculating a task area formed by coordinate points passing by within a specified time period according to an execution task in simulation, further determining a to-be-displayed area, and displaying the to-be-displayed area in the detection type cluster. When the number of the execution tasks is multiple, determining an overlapping area between task areas to determine patrol frequency; visual information is determined, and different action events are grouped so as to be respectively aggregated and drawn; and calculating drawing related information of all points in each piece of task detection information in the to-be-displayed area, and drawing the to-be-displayed area and the task detection information on a map. According to the method, data aggregation and information centralized display can be realized, and multi-dimensional situation information of aerial confrontation can be effectively displayed.

Description

Technical Field

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

[0002] Simulation technology usually simulates or emulates countermeasure training operations or countermeasure game activities, aiming to simulate the effects of different tactical plans through computer models. In the area patrol mission, the simulation can provide different patrol paths, countermeasure plans, and resource allocations according to mission requirements and environmental characteristics. However, relying solely on numerical values and simulation results is not intuitive enough to help the commander comprehensively understand the battlefield situation.

[0003] Situation visualization technology transforms complex countermeasure data into visual information that is easy to understand through a graphical method. By visualizing multi-dimensional data such as patrol paths, resource usage, and the enemy-our situation, the commander can more clearly understand the overall situation of mission execution and thus make more accurate decisions.

[0004] In the prior art, the amount of information expressed in a single view by the traditional view is limited and cannot meet the multi-dimensional display requirements of simulating complex situations. For example, in one view, only the current position of the aircraft can be displayed, and multi-dimensional information such as mission status, formation situation, and threat types cannot be presented simultaneously. This single-scale expression method requires the commander to frequently switch between multiple views to integrate information from different sources, thereby increasing the cognitive burden and operational complexity and delaying the decision-making speed. In addition, the existing technology lacks effective spatial aggregation when presenting information, resulting in scattered distribution of data points in the view and a messy overall interface. When analyzing the situation, the commander needs to focus on and connect the scattered information point by point, making it difficult to quickly obtain the overall overview of the countermeasure 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 mission 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 mission area to solve the technical problems in the prior art that the amount of information expressed by a single view is limited and cannot meet the multi-dimensional display requirements of simulating complex situations, resulting in the need to frequently switch between multiple views to integrate information from different sources, thereby increasing the cognitive burden and operational complexity, and it is difficult to quickly obtain the overall overview of the countermeasure training situation due to the scattered distribution of data points in the view, and the correlation between data points is weak, and it is not easy to determine important areas or potential threats, etc. 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 mission area, including: obtaining spatial geographical data of an area to be detected, performing spatial clustering to obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster, specifically including a method of defining spatial density according to the number of neighbors of data points; grouping data points with the same features; according to the tasks executed in the simulation, calculating the mission area formed by the coordinate points passed through within a specified time period, and further determining the area to be displayed, where the executed tasks include air patrol tasks, specifically including: when the number of executed tasks is multiple, determining the overlapping area between mission areas to determine the patrol frequency; determining visualization information, grouping different action events for separate aggregated rendering; calculating the rendering-related information of all points in each mission detection information in the area to be displayed, and rendering the area to be displayed and mission detection information on a map.

[0008] The second aspect of the present invention provides a simulation display and control system for determining a mission area, which uses the simulation display and control method for determining a mission area described in the first aspect of the present invention. The simulation display and control system includes: an acquisition module for obtaining spatial geographical data of an area to be detected, performing spatial clustering to obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster, specifically including a method of defining spatial density according to the number of neighbors of data points; grouping data points with the same features; a first calculation module for calculating the mission area formed by the coordinate points passed through within a specified time period according to the tasks executed in the simulation, and further determining the area to be displayed, where the executed tasks include air patrol tasks, specifically including: when the number of executed tasks is multiple, determining the overlapping area between mission areas to determine the patrol frequency; determining visualization information, grouping different action events for separate aggregated rendering; a second calculation module for calculating the rendering-related information of all points in each mission detection information in the area to be displayed, and rendering the area to be displayed and mission detection information on a map.

[0009] The third aspect of the present invention provides an electronic device, including: 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, enabling the one or more processors to implement the simulation display and control method for determining a mission area described in the first aspect of the present invention.

[0010] The fourth aspect of the present invention provides a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the simulation display and control method for determining a mission area described in the first aspect of the present invention.

[0011] Advantages of the present invention: Compared with the prior art, the present invention obtains the spatial geographical data of the area to be detected, performs spatial clustering, and obtains at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster. According to the tasks executed in the simulation, the task area formed by the coordinate points passed within a specified time period is calculated, and the area to be displayed is further determined. When the number of executed tasks is multiple, the overlapping area 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 the aggregation of data and the centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a flowchart of the steps of an example of the simulation display and control method for determining the task area of the present invention; Figure 2 is a schematic diagram of an example of forming the area to be displayed in the simulation display and control method for determining the task area of the present invention; Figure 3 is a schematic diagram of an example of dividing clusters in the simulation display and control method for determining the task area of the present invention; Figure 4 is an schematic diagram of an application example of the simulation display and control method for determining the task area of the present invention; Figure 5 is a schematic diagram of determining the cluster with the same event id in an application example of the simulation display and control method for determining the task area of the present invention; Figure 6 is a schematic diagram of an example of the simulation display and control system for determining the task area of the present invention; Figure 7 is a schematic diagram of the structure of an electronic device embodiment according to the present invention; Figure 8 is a schematic diagram of the structure of a computer-readable medium embodiment according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0014] In view of the above problems, the present invention provides a simulation display and control method for determining a mission area. The method obtains the spatial geographic data of the area to be detected, performs spatial clustering, and obtains at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster. According to the tasks executed in the simulation, it calculates the mission area formed by the coordinate points passed within a specified time period, further determines the area to be displayed. When the number of executed tasks is multiple, it can accurately determine the overlapping area between mission areas to more accurately determine the patrol frequency; determines the visualization information, groups different action events for separate aggregation and plotting; calculates the plotting-related information of all points in each mission detection information in the area to be displayed, and plots the area to be displayed and the mission detection information on the map, which can achieve data aggregation and centralized display of information, can effectively display the multi-dimensional situation information of air combat, can achieve data aggregation and centralized display of information, can effectively display the multi-dimensional situation information of air combat, and enables users to quickly and accurately analyze the data.

[0015] Adopt an aggregation method to plot the patrol area, the number of aircraft, and the detected targets. Aggregate and display the detected target spatial positions that are close to each other, thereby reducing the dimension and visualizing the spatial data. The targets include unit targets of one side in the air patrol mission, specifically including flight type targets, radar type targets, and confrontation type targets. The method of the present invention has wide applications, is particularly suitable for the aggregation visual analysis of regional patrol capabilities, and is especially suitable for various tasks executed in various games, specifically including air patrol tasks, multi-aircraft flight tasks, air combat tasks, and so on.

[0016] Embodiment 1 The following will refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 to elaborate in detail on the simulation display and control method for determining a mission area of the present invention.

[0017] Figure 1 is a step flow chart of an example of the simulation display and control method for determining a mission area of the present invention.

[0018] First, in step S101, obtain the spatial geographic 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 neighbors of data points; group the data points with the same characteristics.

[0019] In a specific embodiment, obtain the spatial geographic data of the area to be detected from the regional patrol mission (or air patrol mission) in the simulation process, such as the air patrol mission executed in a certain game.

[0020] Specifically, perform spatial aggregation and dimensionality reduction processing on the spatial geographical data related to the detection information in the area to be detected. Initially classify according to the detection types of the coordinate points of each data point. Then, perform spatial clustering on the spatial geographical data based on the distances between different coordinate points of each data point to obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster. Specifically, classify the detection information where the distance between any two adjacent data points is within a specific range (for example, within 0.1 of longitude and latitude) into one category and label it with different clusters, such as using an id.

[0021] More specifically, the detection information specifically includes threat points (specifically points corresponding to different threat types) and coordinate information.

[0022] In this embodiment, the threat types include simulated aircraft, simulated radar, simulated confrontation threats, and simulated ships.

[0023] For obtaining the spatial geographical data of the area to be detected, for a large number of database columns, selectively extract only the columns required for spatio-temporal data visualization, which can effectively reduce unnecessary data transmission and processing, and thus achieve efficient utilization of data. For example, connect to the database through a library such as the cx_Oracle library, execute an SQL query statement (for example, the SQL query statement contains keywords or key phrases related to the detection information), and obtain the large-scale data of the spatial geographical data. Specifically, use the Sanic framework to extract the required fields from the database, organize the extracted data into JSON data, and submit it to the relevant module (such as the data visualization module) for subsequent data processing. Thus, unnecessary data transmission and data processing are effectively avoided.

[0024] For example, the data fields are as follows (the following values are only examples): { "unique_id": "2", "number_launched": "6", "now_latitude": "89.12642858", "now_longitude": "0.7631882", "detection _type": "COMBAT", }。

[0025] For spatial clustering, it specifically includes the method of defining spatial density according to the number of data point neighbors; grouping the data points with the same characteristics.

[0026] Specifically, the DBSCAN algorithm in Python is used to define the spatial density based on the number of neighbors of data points, determine the number of various clusters in real time, and can find clusters with irregular shapes in space. Therefore, it can effectively achieve the spatial dimensionality reduction of the detection information.

[0027] For the DBSCAN algorithm, there are two main parameters: 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, these points are considered neighbors. If the value of eps is set too small, most data will be regarded as noise; if set too large, multiple clusters will be merged, resulting in most data points being classified into the same cluster. MinPts refers to the minimum number of points required within the eps neighborhood to form a dense region. Specifically, the value of MinPts should be selected according to the dimension of the dataset. Optionally, MinPts >= D + 1, where D is the dimension of the dataset.

[0028] Specifically, for the area to be detected, an arbitrary point (e.g., represented by O) is selected from the detection information (specifically threat points and coordinate information) of the area to be detected. According to the air patrol task, the specified distance (e.g., represented by e) within the same cluster and the specific number (e.g., represented by m) within the same cluster are determined. The distances between other points in the area to be detected and this point O are calculated and compared with the specified distance e. When the calculated distance is less than or equal to the specified distance e, it is determined that the point corresponding to this distance and point O belong to the same cluster. When the calculated distance is greater than the specified distance e, it is determined that the point corresponding to this distance and point O do not belong to the same cluster. When it is determined that the distance between a certain point and point O is less than or equal to the specified distance e and the number of data points in the current cluster is greater than the specific number m, cluster expansion processing is performed, that is, the current cluster is expanded. Specifically, the current cluster is recursively grown. And when it is determined that the distance between a certain point and point O is greater than the specified distance e and the number of data points in the current cluster is greater than the specific number m, it is determined that this point does not belong to the current cluster and no cluster expansion processing is performed.

[0029] According to the clusters determined above, each area to be displayed is determined, that is, each cluster corresponds to an area to be displayed.

[0030] Specifically, using the above method of dividing clusters, the areas to be displayed as shown in Figure 2 are determined.

[0031] Combined 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, it is classified according to the value of the data field detection_type. Then, clustering is performed based on the coordinates of the data points within the classification. For example, all points with the value of the data field detection_type being "COMBAT" are clustered. For the clustering results of these points, as Figure 4 shown, specifically, circular dots are used to represent the first type of threat points (such as simulated radars), and triangles are used to represent the second type of threat points (such as simulated airplanes). Combining the longitude and latitude coordinates of these points (corresponding to "now_latitude" and "now_longitude" in the data field), by debugging the neighborhood range threshold (such as represented by eps) and the minimum sample value (such as represented by min_samples), areas with sufficient density can be divided into the same cluster. At the same time, the same cluster value (i.e., the cluster value) is assigned to each coordinate point within the same cluster, as Figure 3 shown, the circular dots enclosed in the box in are the same cluster, where the cluster of all points is 1 and is represented by cluster 1. And Figure 3 the triangles enclosed in the box in are the same cluster, where the cluster of all points is 2 and is represented by cluster 2. The threat types of cluster 1 and cluster 2 are different. Specifically, the threat types include simulated airplanes, simulated radars, simulated combat threats, and simulated ships.

[0032] Specifically, the values of eps and min_samples are, for example, 0.1 and 4 respectively.

[0033] Furthermore, according to the divided clusters, a JSON field file with the following format and meaning can be obtained. For specific details, refer to Table 1: Table 1

[0034] Table 1 shows the relevant information data of the area to be displayed and the plotting patterns corresponding to the display.

[0035] Specifically, the events specifically include patrol events, strike events, etc.

[0036] In this example, the event is a patrol event. Among them, different patrol event tasks have different ids, that is, event ids, which are specifically the primary keys of a patrol task.

[0037] It should be noted that in other implementation manners, the events also include instruction issuing events, aiming events, reconnaissance target discovery events, strike events, damage events, etc.

[0038] It should be noted that in the present invention, the confrontation situation data specifically refers to the relevant data to be displayed according to the task scenario (specifically including area patrol tasks or other air tasks) or the overall display requirements such as multi-information fusion during the simulation process. Each data corresponds to one data point or multiple data points, and the confrontation situation data includes spatial geographical data. For example, in the area patrol task scenario, the confrontation situation data specifically includes multi-dimensional information data such as the area patrol task status, the number of aircraft, formation information, threat points, threat types, etc. According to the entity attributes corresponding to each piece in the confrontation situation data, the simulation data of each piece is mapped from a high-dimensional space to a two-dimensional matrix in a two-dimensional space. The pieces are used to represent confrontation entity information such as aircraft and personnel participating in the area patrol task, infrastructure, confrontation objects, and confrontation targets. The above is described as an optional example and should not be construed as a limitation to the present invention.

[0039] Next, in step S102, according to the tasks executed in the simulation, calculate the task area formed by the coordinate points passed through during the specified time period, and further determine the area to be displayed. The tasks executed include air patrol tasks, specifically including: when the number of tasks executed is multiple, determine the overlapping area between the task areas to determine the patrol frequency; determine the visualization information, group different action events, and draw them separately by aggregation.

[0040] In the example of executing an air patrol task, calculate the task area formed by the coordinate points passed through during the specified time period, and further determine the area to be displayed.

[0041] Specifically, for the party executing the air patrol task, perform time dimensionality reduction on all the coordinate points passed through by the aircraft during a specific period of time (the entire process of executing the air patrol task, for example, from the initial time t0 to the termination time t 终 )). The position where the aircraft drops every second is a coordinate point, and the coordinate points form an area (i.e., the task area) during a period of time (from the initial time t0 to the termination time t 终 ). Specifically calculate the envelope surface of these coordinate points to determine the display area. The border of the display area is determined by the number of aircraft. The more aircraft there are, the thicker the border.

[0042] Preferably, fill the determined task area with colors of different transparencies to characterize the patrol frequency of the task area. For example, use green with opacity to fill the area.

[0043] In an optional implementation manner, when the number of tasks executed is multiple (that is, when four air patrol tasks are executed, specifically refer to Figure 4 ), determine the areas to be displayed corresponding to the four air patrol tasks (that is, four areas to be displayed), and determine the overlapping area between the task areas to determine the patrol frequency.

[0044] When the areas of multiple air patrol missions overlap, the filled opacity will be superimposed, and the opacity of the overlapping area will be specifically determined to determine the patrol frequency. Specifically, it includes: determining the final thickness value of the visual line segment of the mission area according to the number of aircraft; determining the opacity of the overlapping area when the areas of multiple air patrol missions overlap to determine the patrol frequency.

[0045] First, determine the final thickness value of the visual line segment of the mission area, specifically determine the final thickness value of the visual line segment of each mission area on the map.

[0046] Specifically obtain the launch number 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 mission area on the map using the following expression, so as to characterize the number and performance of the aircraft in the image displayed on the visual interface through the area border width.

[0047] In the air patrol mission, the number of aircraft is from 1 to 10, and optionally, the number of aircraft is from 1 to 3. When the user selects a larger number of aircraft, the data visualization visual effect can still be ensured to be good. Therefore, the following piecewise function method is used for processing.

[0048] 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 used as an optional example for illustration and should not be construed as a limitation to the present invention.

[0049] The piecewise function is used to dynamically adjust the initial thickness of the line segment 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, balance the visualization effect and data readability, so as to effectively improve the visualization effect of data on the map. The specific piecewise definition and technical principle are as follows:

[0050] For the first interval of the piecewise function: 0 < x ≤ 3, the expression for the thickness value of the visual line segment and the number of aircraft is: y = x 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 mission.

[0051] Specifically, this first interval is a 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 visual line segment is in a simple proportional relationship with the number of aircraft x. For example, when x = 3, y = 3.

[0052] It should be noted that the linear relationship in the first interval facilitates the user to quickly understand the direct association between the parameters and the visual effects, which is in line with the scenario where the number of aircraft emphasizes low-value differences.

[0053] For the second interval of the piecewise function: 3 < x ≤ 10, the expression for the initial thickness value of the visualized line segment and the number of aircraft is: y = 2 * x 1 / 2 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.

[0054] Specifically, the piecewise function in this second interval shows a decelerating growth. The growth rate of the initial thickness value y of the visualized line segment is slowed down by the square root function to avoid the line segment being too thick when the number of aircraft x is at a medium value. For example, when x = 10, y ≈ 6.32.

[0055] The piecewise function in this second interval has the effect of smooth transition. Due to the decreasing derivative property of the square root function, it can effectively ensure that the thickness value of the visualized line segment gradually flattens out as the number of aircraft x increases, and can effectively prevent abrupt changes in visualization.

[0056] For the third interval of the piecewise function: x > 10, the expression for the initial thickness value of the visualized line segment and the number of aircraft is: y = 10 * (1 - e −(x-9) ) 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.

[0057] When the number of aircraft x is greater than 10, using the above piecewise function can play a role of saturation limit.

[0058] 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 (e.g., when x = 19, y ≈ 9.99955). Through the above piecewise function, it can effectively avoid the line segment being too thick and affecting readability.

[0059] By using the exponential function, it can play a role of adaptive adjustment, that is, the exponential function (1 - e −(x-9) ) can effectively ensure that the growth rate of the thickness value of the visualized line segment approaches zero when approaching the upper limit, thereby achieving a natural and smooth saturation effect.

[0060] It should be noted that according to the piecewise functions in the above paragraphs, after obtaining the initial line thickness, the line thickness should also be adaptively adjusted in combination with the screen size, map size, etc. to ensure the readability and aesthetics of data visualization.

[0061] According to the above piecewise function, it is specifically divided into three piecewise functions according to the number of aircraft, and the following expression is used to calculate the final thickness value of the visualized line segment: airWidth = y * mapScale * ariScale Among them, airWidth represents the width of the regional border of the mission area of the current air patrol mission 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, for example, it is a fixed value; airScale represents the zoom factor of the aircraft, calculated according to the device pixel ratio, the width of the map container, etc., airScale = pixelRatio *(containerWidthNow / containerWidth0), where containerWidthNow represents the current width of the map container and can be obtained through the front-end function; containerWidth0 represents the reference map container width, for example, it is a fixed value, and this fixed value is, for example, 1200px; pixelRatio can be obtained through window.devicePixelRatio.

[0062] In another alternative implementation, the following expression is used to calculate the final thickness value of the visualized line segment: Among them, W represents the width of the regional border of the mission area of the current air patrol mission on the map, that is, the final thickness value of the visualized 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 launch number of aircraft in the current air patrol mission, that is, the number of aircraft; the aircraft performance factor (performanceFactor i ) is assigned according to the model of the i-th aircraft (such as 1.1 for reconnaissance aircraft, 1.3 for fighter aircraft, etc.); scale0 represents the zoom factor of the map; ariNum represents the initial zoom factor of the aircraft; adjustFactor represents the adjustment coefficient, which is a fixed value, and the value range of this fixed value is between 0.5-2. For example, this fixed value is 1. Users can adjust this adjustment coefficient to fine-tune the border width in the picture to ensure that the border visual effect is balanced and meets the user's preferences and aesthetics, so as to improve the user experience while improving the map visualization effect.

[0063] By introducing the logarithmic function and the adjustment factor, the above formula ensures that the border width is obvious enough when the number of aircraft is small, and avoids excessive distortion of the border when the number of aircraft is large, so as to achieve a more effective and balanced visualization effect in a large-scale mission area.

[0064] Further, determine the visualization information and group different action events.

[0065] Specifically, determine the area to be displayed corresponding to the air patrol mission, the simulated aircraft, whether there are threat points, etc.

[0066] For aggregated drawing, draw the mission area of the current air patrol mission, the border width of each mission area, and the threat points, where the threat points are used as mission detection information.

[0067] Specifically, obtain the coordinate data of the aircraft in the current air patrol mission and map it to a two-dimensional point array on the map (for example, represented by newPoints). Use the polygon hull function of the visualization data processing library (such as library D3) to calculate the convex hull of each data point in the coordinate data of the aircraft. The convex hull is the smallest polygon that contains all points, specifically referring to all the points passed by the aircraft with the same event id, such as Figure 5 All the points of event id1 as shown to obtain a convex hull array (for example, represented by hull), and each convex hull corresponds to an area to be displayed. Then, use the visualization data processing library (such as library D3) to create a curve generator to generate a smooth closed curve corresponding to each area to be displayed, and modify the smoothness of the generated smooth closed curve by modifying the value of the control parameter. Generate a closed path according to the curve, and the area within this path is the area to be displayed (in this example, also called the patrol area).

[0068] For the visualization display of the multi-task overlapping area, since the number of air patrol missions has no upper limit, while directly superimposing the opacity has a certain upper limit (one hundred percent), further processing is done on the display of the multi-task overlapping area. For the multi-area overlapping area, taking the overlap of two areas as an example: for the patrol area of air patrol mission a, traverse the convex hull array of air patrol mission a, and judge whether other air patrol missions appear within the detection range G of the data point α in the convex hull array, such as the data points of air patrol mission b. If it appears, it means that there is an overlapping area between air patrol mission a and air patrol mission b. Then, gradually traverse the points around the data point α in air patrol mission a and judge whether there are data points of air patrol mission b. If there are, add these two points, namely the data point α and the data point belonging to air patrol mission b located at the data point α, to the new overlapping area group. If not, end the traversal. Repeat the calculation according to the above rules. Finally, the overlapping area between air patrol missions a and b can be obtained. At this time, the patrol frequency of this overlapping area is 2.

[0069] 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 q-type simulated aircraft is 100 km.

[0070] After determining the aerial patrol area, determine the fill color of the aerial patrol area. For a single area, the opacity of the color is a fixed value (e.g., ten percent); for multiple areas, the color is filled by assigning scores, where both the maximum opacity and the minimum opacity are fixed values (e.g., ten percent and sixty percent). For example, if the patrol frequency of an existing overlapping area C is 3 and the maximum patrol frequency in the entire visible map 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:

[0071] Opacity of the current overlapping area = Minimum opacity of multiple aerial patrol areas + (Maximum opacity of aerial patrol areas - Minimum opacity of aerial patrol areas) * (Patrol frequency of the current overlapping area - Minimum patrol frequency of the entire visible map) / (Maximum patrol frequency of the entire visible map - Minimum patrol frequency of the entire visible map).

[0072] Using the above expression to calculate the opacity of the patrol frequency of overlapping area C, that is, 10 + (60 - 10) * (3 - 1) / (5 - 1) = 22.5. Therefore, it can ensure a specific opacity when there is no overlapping area, can identify each area, can effectively avoid the problem of too high opacity caused by many overlapping areas and high patrol frequency, and can also effectively avoid the problem of unclear map information itself caused by covering the map, thus achieving a more effective and balanced map visualization effect in the case of multiple aerial patrol tasks.

[0073] It should be noted that in this example, the entire visualization map refers to the display map of the determined area to be displayed in the map. The above is only for illustrative purposes as an optional example and should not be construed as a limitation of the present invention.

[0074] Next, in step S103, 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 on the map.

[0075] Specifically, select the JSON data processed in step S101, and form a point set for each cluster of data for subsequent drawing calculation and analysis.

[0076] After calculating the border width of the area to be displayed (i.e., the final thickness value of the visualization line segment) of the current aerial patrol task in the map, calculate the drawing-related information of all points in each task detection information in the area to be displayed.

[0077] Specifically, traverse the processed drawing data (all obtained point sets), calculate the convex hulls of all points in each task detection information in each array of the drawing data, and obtain an array of convex hulls. For example, use d3.polygonHull to calculate the convex hulls of all points in each task detection information in each array of the drawing data, and obtain an array of convex hulls (for example, represented by convexHull).

[0078] Specifically, each convex hull is a convex polygon formed by connecting the outermost points, which can contain all the points within the convex polygon, corresponding to forming a point set, that is, an array of convex hulls.

[0079] Next, determine the coordinates of the plotted pattern by calculating the center points of each convex hull. Determine the size of the plotted pattern on the map by calculating the area of each convex hull. Specifically calculate the center points of each convex hull. For example, use d3.polygonCentroid(convexHull) to calculate the center points of each convex hull (for example, represented by centroid), and the calculated center points are the coordinates of the plotted pattern on the map. Calculate the area of the convex hull (represented by area), and through area * scale0, the size of the plotted pattern on the map can be obtained (represented by imageSize).

[0080] Specifically, for example, convex hull b corresponds to a simulated aircraft, and convex hull d corresponds to a simulated confrontation point, and the simulated confrontation point is, for example, a device.

[0081] Draw the area to be displayed and the corresponding plotted pattern on the map. The relevant information data of the plotted pattern (specifically including the data corresponding to the detection information, and the detection information includes the coordinate information and threat type of the threat point), specifically refer to Table 1 above.

[0082] For the plotting of the area to be displayed, it is necessary to determine the pattern of the area to be displayed, the plotted coordinates, and the plotted size. The pattern of the area to be displayed is determined by the threat type of each cluster, the plotted coordinates are determined by the center point of each cluster, and the plotted size is related to the distribution of the clusters. The latter two are both related to the coordinates of the points in the clusters. Next, the initial relevant data will be processed 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.

[0083] In a specific embodiment, traverse the overall JSON data. According to Table 1, put the coordinates with non-empty and identical corresponding category values (i.e., cluster values) of threat points and the same threat type (i.e., detection_type) into the coordinates array of the corresponding clusterType. For example, use the coordinates of all points with a cluster value of 1 and the same threat type of a certain confrontation type (for example, this confrontation type is represented by COMBAT) to obtain the coordinate array (i.e., coordinates): [[25.30, 120.40], [25.33, 120.41].... ], threat type: 1. Specifically, match according to the threat type and the path of the pattern in the area to be displayed to obtain threatType: "pics / combat.png". Such a 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 points. For example: {

[0084] coordinates: [[25.30, 120.40], [25.33, 120.41].... ], clusterType: 1, threatType: "pics / combat.png"}.

[0085] Furthermore, visually display on the map according to the obtained drawing data, 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; the length and width of the area to be displayed are both info.imageSize. Thus, the obtained drawing data can be visually presented on the map.

[0086] It should be noted that the above is only for illustrative purposes as an optional example and should not be construed as a limitation to the present invention.

[0087] Compared with the prior art, the present invention obtains the spatial geographical data of the area to be detected, performs spatial clustering to obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster, calculates the task area formed by the coordinate points passed through during a specified time period according to the tasks executed in the simulation, further determines the area to be displayed, and when the number of the executed tasks is multiple, can accurately determine the overlapping area between the task areas to more accurately determine the patrol frequency; determines the visualization information, groups different action events for separate aggregation and drawing; 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 the map, which can achieve data aggregation and centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation.

[0088] Embodiment 2 The following is an embodiment of the system of the present invention, which can be used to execute the method embodiment of the present invention. For the details not disclosed in the system embodiment of the present invention, please refer to the method embodiment of the present invention.

[0089] Figure 6 It 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.

[0090] Next, reference will be made to Figure 6 , to describe the simulation display and control system, which uses the simulation display and control method for determining a task area described in the first aspect of the present invention.

[0091] As Figure 6 shown, the simulation display and control system 500 includes an acquisition module 510, a first determination module 520, and a second determination module 530.

[0092] In a specific embodiment, the acquisition module 510 is used to acquire the spatial geographic data of the area to be detected, perform spatial clustering, and obtain at least one of the following clustering clusters: detection type cluster, geographic location clustering cluster, specifically including the method of defining spatial density according to the number of data point neighbors; grouping data points with the same characteristics. The first calculation module 520 calculates the task area formed by the coordinate points passed through during a specified time period according to the tasks executed in the simulation, and further determines the area to be displayed. The executed tasks include air patrol tasks, specifically including: when the number of executed tasks is multiple, determining the overlapping area between the task areas to determine the patrol frequency; determining the visualization information, grouping different action events, and aggregating and drawing them 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.

[0093] According to an alternative embodiment, when the number of executed tasks is multiple, determine the final thickness value of the visualization line segment of the task area, and determine the overlapping area between the task areas to determine the patrol frequency.

[0094] According to 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: 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.

[0095] According to an alternative implementation, it is 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.

[0096] 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.

[0097] 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.

[0098] According to an alternative implementation, when the number of tasks being executed is multiple, determine the overlapping area between task areas to determine the patrol frequency.

[0099] For determining the patrol frequency, calculate the patrol frequency of the overlapping area between task areas based on the overlapping area and the number of overlaps.

[0100] The opacity of the current overlapping area is calculated using the following expression to characterize the patrol degree of the current overlapping area: Opacity of the current overlapping area = Minimum opacity of the multi-air patrol areas + (Maximum opacity of the air patrol areas - Minimum opacity of the air patrol areas) * (Patrol frequency of the current overlapping area - Minimum patrol frequency of the entire visible map) / (Maximum patrol frequency of the entire visible map - Minimum patrol frequency of the entire visible map).

[0101] According to an alternative embodiment, 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: Traverse the processed drawing data, calculate the convex hull of all points in each object in each array in the drawing data, and obtain an array of convex hulls.

[0102] Calculate the center points of the convex hulls, which are the coordinates of the plotted patterns.

[0103] Calculate the areas of the convex hulls to further determine the sizes of the plotted patterns on the map.

[0104] According to an alternative embodiment, the DBSCAN algorithm is used to define the spatial density based on the number of neighbors of data points to determine the number of various clusters in real time.

[0105] According to an alternative embodiment, grouping the data points including the same features includes: grouping according to the detected number of aircraft, coordinate information of the aircraft, threat types, spatial shapes they have, etc.

[0106] It should be noted that since Figure 6 the simulation display and control method executed by the simulation display and control system of Figure 1 is substantially the same as the simulation display and control method in the example of

[0107] Compared with the prior art, the present invention obtains the spatial geographical data of the area to be detected, performs spatial clustering, and obtains at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster. According to the tasks executed in the simulation, the task area formed by the coordinate points passed within a specified time period is calculated, and the area to be displayed is further determined. When the number of executed tasks is multiple, the overlapping area 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 the aggregation of data and the centralized display of information, and can effectively display the multi-dimensional situation information of air confrontation.

[0108] Embodiment 3 Figure 7 It is a schematic structural diagram of an embodiment of an electronic device according to the present invention.

[0109] As Figure 7 shown, the electronic device is presented in the form of a general-purpose computing device. The processor can be one or multiple and work collaboratively. The present invention does not exclude distributed processing, that is, the processors can be dispersed in different physical devices. The electronic device of the present invention is not limited to a single entity, but can also be the sum of multiple physical devices.

[0110] The memory stores computer-executable programs, usually machine-readable codes. The computer-readable programs can be executed by the processor so that the electronic device can execute the method of the present invention or at least part of the steps in the method.

[0111] The memory includes volatile memory, such as a random access storage unit (RAM) and / or a cache storage unit, and can also be non-volatile memory, such as a read-only storage unit (ROM).

[0112] Optionally, in this embodiment, the electronic device further includes an I / O interface, which is used for the electronic device to exchange data with external devices. The I / O interface can represent one or more of several bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0113] 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 example. For example, some electronic devices also include a display unit such as a display screen, and some electronic devices also include human-computer interaction elements such as buttons and keyboards. As long as the electronic device can execute the computer-readable program in the memory to implement at least part of the steps of the method of the present invention, it can be considered as the electronic device covered by the present invention.

[0114] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, as Figure 8 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 portable hard disk, etc.) or on a network, including several 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.

[0115] The software product may adopt 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, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium 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 of the above.

[0116] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with a command execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0117] The 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++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's 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's computing device through 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., by connecting through the Internet using an Internet service provider).

[0118] The above computer-readable medium carries one or more programs which, when executed by a device, cause the computer-readable medium to implement the data interaction method of the present disclosure.

[0119] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiments, or can be correspondingly changed and distributed in one or more devices that are only different from the present embodiment. The modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0120] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by the way of software in combination with necessary hardware. Therefore, the technical solution according to the embodiments 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, including several commands to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0121] 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 technical field to which the present application belongs.

[0122] In the foregoing detailed description, reference has been made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components, unless the context indicates otherwise. The illustrated embodiments described in the detailed description, the drawings, and the 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.

[0123] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope 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 characteristics; According to the tasks executed in the simulation, calculate the task area formed by the coordinate points passed through within the 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 the task areas to 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.

2. The simulation display and control method for determining a task area according to claim 1, wherein Including: 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 the task areas to determine the patrol frequency; According to the multi-segment 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 map container width, ariScale = pixelRatio *(containerWidthNow / containerWidth0), where, containerWidthNow represents the current map container width, which can be obtained through the front-end function; containerWidth0 represents the reference map container width; pixelRatio can be obtained through window.devicePixelRatio.

3. The simulation display and control method for determining a task area according to claim 2, wherein, Further including: Divided into a three-segment piecewise function according to the number of aircraft, specifically including: 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: 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 in the current air patrol task; 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: 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 in the current air patrol task; For the third segment 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) ) Among them, y represents the initial thickness value 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.

4. The simulation display and control method for determining a task area according to claim 2 or 4, characterized in that The determining the patrol frequency includes: Calculating the patrol frequency of the overlapping area between mission areas based on the overlapping area and the number of overlaps; Using the following expression to calculate the opacity of the current overlapping area to characterize the patrol frequency of the current overlapping area: Opacity of the current overlapping area = Minimum opacity of multiple air patrol areas + (Maximum opacity of air patrol areas - Minimum opacity of air patrol areas) * (Patrol frequency of the current overlapping area - Minimum patrol frequency of the entire visible map) / (Maximum patrol frequency of the entire visible map - Minimum patrol frequency of the entire visible map).

5. The simulation display and control method for determining a task area according to claim 1, wherein The 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 specifically includes the following steps: Traversing the processed drawing data, calculating the convex hull of all points in each object in each array in the drawing data to obtain a convex hull array; Calculating the center point of each convex hull, which is the coordinate of the plotting pattern; Calculating the area of each convex hull to further determine the size of the plotting pattern on the map.

6. The simulation display and control method for determining a task area according to claim 1, wherein Including: Using the DBSCAN algorithm to define the spatial density according to the number of neighbors of data points and determining the number of various clusters in real time.

7. The simulation display and control method for determining a task area according to claim 1, wherein The grouping the data points containing the same features includes: Grouping according to the detected number of aircraft, the coordinate information of the aircraft, the threat type, the spatial shape possessed, etc.

8. A simulation display and control system for determining a task area, characterized in that, It uses the simulation display and control method for determining the mission area according to any one of claims 1 to 7. The simulation display and control system for determining the mission area includes: An acquisition module for acquiring the spatial geographical data of the area to be detected, performing spatial clustering to obtain at least one of the following clustering clusters: detection type cluster, geographical location clustering cluster, specifically including defining the spatial density according to the number of neighbors of data points; grouping the data points containing the same features; A first calculation module for calculating the mission area formed by the coordinate points passed through during a specified time period according to the tasks executed in the simulation, and further determining the area to be displayed. The executed tasks include air patrol tasks, specifically including: when the number of executed tasks is multiple, determining the overlapping area between mission areas to determine the patrol frequency; determining the visualization information and grouping different action events for separate aggregation and drawing; A second calculation module for 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.

9. The simulation display and control system for determining a task area according to claim 8, wherein Including: Determining the final thickness value of the visualization line segment of the mission area; According to the piecewise function divided by the number of aircraft, using the following expression to calculate the final thickness value of the visualization line segment: 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 the front-end function; containerWidth0 represents the reference map container width; pixelRatio can be obtained through window.devicePixelRatio.

10. The simulation display and control system for determining the task area according to claim 9, further comprising: 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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