Intelligent scheduling method, system and equipment for low-altitude management and control resources and medium

Abstract

The invention discloses an intelligent scheduling method, system and device for low-altitude management and control resources and a medium, and relates to the technical field of low-altitude management and control resource scheduling, and the method comprises the steps: for each flight target in a detectable range, calculating to obtain a basic index between each detection device in a detection range and the flight target; calculating the information entropy of the basic indexes, and obtaining the index weight of each index in the basic indexes according to the calculated information entropy; based on the basic indexes and the index weight of each index in the basic indexes, target parameters between each detection device and the flight target are calculated; determining the detection equipment with the minimum numerical value of the target parameter as target equipment, and detecting the flying target by using the target equipment; an intelligent management and control equipment scheduling model for calculating target parameters in a complex environment is established, so that the problems of non-uniform equipment resource allocation and low efficiency in a low-altitude detection management and control system in the complex environment are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude control resource scheduling, and more specifically, to an intelligent scheduling method, system, equipment and medium for low-altitude control resources. Background Art

[0002] In the process of low-altitude control, the detection of flying targets has become a top priority; in complex environments, the administrative area is large, including a large number of cities, villages, mountains and other typical landforms, so it is particularly necessary to control the flying targets therein; and how to effectively coordinate and dispatch detection equipment resources and complete real-time dispatch tasks in an environment where the types of flying targets are rapidly increasing and the detection equipment resource dispatch strategy is becoming more complex is the main problem that the current flying target detection system needs to solve.

[0003] The scheduling process of intelligent low-altitude control resources in complex environments mainly combines and analyzes the real-time target flight situation data, the detection performance of the detection equipment, and the detection evaluation system to obtain the optimal detection equipment selection plan at the corresponding flight time. At present, the types of various low-altitude aircraft are increasing rapidly, the number of aircraft is increasing rapidly, and the use is becoming more and more frequent, and the scheduling strategy of aircraft detection sensors is becoming more and more complex. Although some current detection systems have realized the automatic scheduling of sensor resources, and some sensor equipment has also realized the ability of automatic operation and unattended operation, there are still common problems such as uneven distribution of detection sensor resources, insufficient utilization of comprehensive resources, insufficient coverage of equipment operation, and poor adaptability. Therefore, on the basis of existing detection equipment, effectively coordinating equipment resources, enhancing the automation capability of the overall system, completing real-time resource scheduling analysis, and finally obtaining an intelligent scheduling method for low-altitude control resources at this moment are the main problems that the current low-altitude aircraft detection system needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent scheduling method, system, equipment and medium for low-altitude control resources to solve the problems existing in the above-mentioned background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] In the first aspect, the present application provides an intelligent scheduling method for low-altitude control resources, including the following specific steps:

[0007] For each flying target within the detectable range, calculate the basic index between each detection device within the detection range and the flying target;

[0008] Calculate the information entropy of the basic indicators, and obtain the indicator weight of each indicator in the basic indicators according to the calculated information entropy;

[0009] Based on the basic indicators and the indicator weights of each indicator in the basic indicators, the target parameters between each detection equipment and the flight target are calculated;

[0010] The detection device with the smallest target parameter value is determined as the target device, and the flying target is detected using the target device.

[0011] The beneficial effects of the present invention are as follows: in this scheme, firstly, the flight status data of each flying target within the detectable range, including the coordinates of the flying target, etc., are obtained, and for each flying target existing in the detectable range, the basic indicators between each detection device in the detection range and the flying target are calculated; secondly, the information entropy and the indicator weight are calculated for the basic indicators between each flying target and each detection device, and the final target parameters between each flying target and each detection device are calculated by the indicator weight of each indicator; finally, the target parameters are used for judgment, that is, the detection device corresponding to the target parameter with the smallest numerical value is determined as the device for detecting the flying target; at the same time, real-time scheme generation is performed according to the changes in the position of the flying target at different times.

[0012] In this scheme, multiple different basic indicators are used to measure the priority of detection equipment selection decision-making, and the information entropy of multiple different basic indicators is calculated. The entropy weight method is used to weight them, which amplifies the algorithm results by an order of magnitude, improves the rapid response capability of the detection system, and establishes an intelligent control equipment scheduling model for calculating target parameters in complex environments, thereby solving the problems of uneven equipment resource allocation and low efficiency in low-altitude detection and control systems in complex environments.

[0013] Based on the above technical solution, the present invention can also be improved as follows.

[0014] Furthermore, the above basic indicators include Euclidean distance, Manhattan distance and cosine distance, among which:

[0015] The Euclidean distance is specifically:

[0016]

[0017] The Manhattan distance is:

[0018] M=|x 1 -x 3 |+|y 1 -y 3 |+|z 1 -z 3 |;

[0019] Where M represents the Manhattan distance between the detection device and the flying target, E represents the Euclidean distance between the detection device and the flying target, (x 1 ,y 1 , z 1 ) represents the coordinates of the flying target in the coordinate system established with the detectable range. The X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the coordinate system are longitude, latitude and altitude respectively. 3 ,y 3 , z 3 ) represents the coordinates of the detection device in the coordinate system established based on the detectable range.

[0020] The beneficial effect of adopting the above further scheme is: using Euclidean distance, Manhattan distance and cosine distance as basic indicators to measure the priority problem of detection equipment selection decision.

[0021] Furthermore, the above cosine distance is specifically:

[0022] The Z-axis coordinate of the virtual point is calculated by using the preset unit distance between the virtual point and the detection device, and the pitch angle between the detection device and the virtual point;

[0023] The X-axis coordinate and Y-axis coordinate of the virtual point are calculated by the projection point of the virtual point on the XY plane in the coordinate system and the position point where the projection point is perpendicular to the X-axis in the coordinate system;

[0024] The cosine distance between the detection device and the flying target is calculated using the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the virtual point.

[0025] Furthermore, the Z-axis coordinates of the above virtual points are specifically:

[0026]

[0027] In the formula, z 2 Represents the Z-axis coordinate of the virtual point, z 3 Indicates the Z-axis coordinate of the detection device. represents the pitch angle between the detection device and the virtual point, d CB Indicates the preset unit distance between the virtual point and the detection device;

[0028] The X-axis coordinates and Y-axis coordinates of the virtual point are:

[0029]

[0030] In the formula, x 2 Indicates the X-axis coordinate of the virtual point, y 2 Indicates the Y-axis coordinate of the virtual point, x 3 Indicates the X-axis coordinate of the detection device, d CDrepresents the distance between the virtual point and the projection point, σ represents the azimuth between the detection device and the projection point, which is calculated by the position point of the projection point perpendicular to the X axis in the coordinate system, and y 3 Indicates the Y-axis coordinate of the detection device.

[0031] Furthermore, the cosine distance between the above detection equipment and the flying target is specifically:

[0032]

[0033] Where C represents the cosine distance between the detection device and the flying target. Represents the vector formed by the coordinates of the detection device and the coordinates of the flying target, Represents the vector formed by the coordinates of the detection device and the coordinates of the virtual point, Representation vector The model, Representation vector Model.

[0034] Furthermore, the information entropy of the basic indicators is calculated, and the indicator weights of each indicator in the basic indicators are obtained according to the calculated information entropy, which is specifically:

[0035] in:

[0036]

[0037] In the formula, E i represents the information entropy of index i in the basic index, n represents the number of indexes, p ij Indicates the probability of state j in state i, Y ij represents the standardized value of each indicator data, W i Represents the weight of indicator i in the basic indicators.

[0038] Furthermore, the target parameters between the above detection equipment and the flying target are specifically:

[0039] D=W 1 ×E+W 2 ×M+W 3 ×C;

[0040] Where D represents the target parameter between the detection equipment and the flying target, W 1 represents the index weight of the Euclidean distance, E represents the Euclidean distance between the detection device and the flight target, W 2 Indicates the indicator weight of Manhattan distance, M represents the Manhattan distance between the detection device and the flight target, W 3 Represents the index weight of cosine distance, and C represents the cosine distance between the detection equipment and the flying target.

[0041] In a second aspect, the present application provides an intelligent scheduling system for low-altitude control resources, which is applied to the intelligent scheduling method for low-altitude control resources in any one of the first aspects, including:

[0042] The first module is used to calculate the basic index between each detection device and the flight target in the detection range for each flight target within the detectable range;

[0043] The second module is used to calculate the information entropy of the basic indicators and obtain the indicator weight of each indicator in the basic indicators according to the calculated information entropy;

[0044] The third module is used to calculate the target parameters between each detection device and the flight target based on the basic indicators and the indicator weights of each indicator in the basic indicators;

[0045] The fourth module is used to determine the detection device with the smallest target parameter value as the target device, and use the target device to detect the flying target.

[0046] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods in the first aspect when executing the computer program.

[0047] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any one of the methods in the first aspect.

[0048] Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] In the present application, firstly, the flight status data of each flying target within the detectable range, including the coordinates of the flying target, etc., are obtained, and for each flying target within the detectable range, the basic indicators between each detection device in the detection range and the flying target are calculated; secondly, the information entropy and indicator weights are calculated for the basic indicators between each flying target and each detection device, and the final target parameters between each flying target and each detection device are calculated with the indicator weights of each indicator; finally, the target parameters are used for judgment, that is, the detection device corresponding to the target parameter with the smallest numerical value is determined as the device for detecting the flying target; at the same time, real-time scheme generation is performed according to the changes in the position of the flying target at different times.

[0050] In this application, Euclidean distance, Manhattan distance and cosine distance are used as basic indicators to measure the priority of detection equipment selection decision, and the information entropy of multiple different basic indicators is calculated, and the entropy weight method is used for weighting, which amplifies the algorithm results by an order of magnitude, improves the rapid response capability of the detection system, and establishes an intelligent control equipment scheduling model for calculating target parameters in complex environments, thereby solving the problems of uneven equipment resource allocation and low efficiency in low-altitude detection and control systems in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0052] Figure 1 A method flow chart of a scheduling method in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a scheduling process of a detection device in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of calculating the cosine distance in an embodiment of the present invention;

[0055] Figure 4 Schematic diagram of the connection of the scheduling system in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0057] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0059] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components must be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0060] In the description of the embodiments of the present invention, "plurality" means at least 2.

[0061] Embodiment 1:

[0062] In order to solve the current problems of uneven distribution of detection sensor resources, insufficient utilization of comprehensive resources, insufficient coverage of equipment operation, and poor adaptability, this embodiment provides an intelligent scheduling method for low-altitude control resources, such as Figure 1 As shown, the following specific steps are included:

[0063] S1, for each flying target existing in the detectable range, calculate the basic indexes between each detection device in the detection range and the flying target.

[0064] Among them, before calculating the basic indicators between each flight target and each detection device, it is necessary to determine the status of each detection device. The status of the detection device can be determined by the command and control center for unified management of each device. Each detection device reports the device status according to a pre-planned interface protocol, so that the command and control center can determine that each detection device is turned on and available; after determining the status of the detection device, each device detects the flight target and reports the detected real-time target flight situation data according to the pre-planned interface protocol. The real-time target flight situation data includes the coordinates of the flight target, etc., and the real-time target flight situation data is used to calculate the basic indicators between each flight target and each detection device.

[0065] Specifically, in the actual flight detection process, multiple detection devices need to cooperate with each other to improve the detection efficiency of the system. The specific diagram of the detection equipment scheduling process is as follows: Figure 2 As shown, the coordinate axes are longitude, latitude and altitude respectively, and the blue sector area is the detection range of the detection equipment and the direction of real-time tracking; Figure 3 At t1, the radar device reports the flying target. At t2, detection device 2 is selected to observe the flying target according to the detection conditions and algorithm. At t3, when detection device 2 can no longer meet the detection requirements, the device scheduling algorithm is called again to switch the device to detection device 5, which continues to track the flying target until t4, when the detection task is completed or the flying target leaves the detection range of all devices.

[0066] Optionally, the above basic indicators include Euclidean distance, Manhattan distance and cosine distance, where:

[0067] The Euclidean distance is specifically:

[0068]

[0069] Furthermore, the Manhattan distance is:

[0070] M=|x 1 -x 3 |+|y 1 -y 3 |+|z 1 -z 3 |;

[0071] Where M represents the Manhattan distance between the detection device and the flying target, E represents the Euclidean distance between the detection device and the flying target, (x 1 ,y 1 , z 1 ) represents the coordinates of the flying target in the coordinate system established with the detectable range. The X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the coordinate system are longitude, latitude and altitude respectively. 3 ,y 3 , z 3 ) represents the coordinates of the detection device in the coordinate system established based on the detectable range.

[0072] Optionally, the calculation principle of the above cosine distance is as follows Figure 3 As shown, specifically:

[0073] S11, calculating the Z-axis coordinate of the virtual point through a preset unit distance between the preset virtual point and the detection device, and a pitch angle between the detection device and the virtual point.

[0074] Optional, see Figure 3 , set the distance between virtual point B and device point C to unit length, and the pitch angle is known The height z of virtual point B can be calculated 2 : z 2 =z 3 +d BD , from these three formulas we can get:

[0075]

[0076] In the formula, z 2 Represents the Z-axis coordinate of the virtual point, z 3 Indicates the Z-axis coordinate of the detection device. represents the pitch angle between the detection device and the virtual point, d CB Indicates the preset unit distance between the virtual point and the detection device.

[0077] S12, calculating the X-axis coordinate and the Y-axis coordinate of the virtual point through the projection point of the virtual point on the XY plane in the coordinate system and the position point where the projection point is perpendicular to the X-axis in the coordinate system.

[0078] Among them, see Figure 3 According to the longitude and latitude of device point C, the distance from device point C to the projection of virtual point D and the azimuth σ, the longitude and latitude of point D, that is, the longitude and latitude of virtual point B, can be calculated.

[0079] Optionally, the X-axis coordinate and Y-axis coordinate of the virtual point are specifically:

[0080]

[0081] In the formula, x 2 Indicates the X-axis coordinate of the virtual point, y 2 Indicates the Y-axis coordinate of the virtual point, x 3 Indicates the X-axis coordinate of the detection device, d CD represents the distance between the virtual point and the projection point, σ represents the azimuth between the detection device and the projection point, which is calculated by the position point of the projection point perpendicular to the X axis in the coordinate system, and y 3 Indicates the Y-axis coordinate of the detection device.

[0082] S13, using the X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the virtual point, calculate the cosine distance between the detection device and the flying target.

[0083] Optionally, the cosine distance between the detection device and the flying target is specifically:

[0084]

[0085] Where C represents the cosine distance between the detection device and the flying target. Represents the vector formed by the coordinates of the detection device and the coordinates of the flying target, Represents the vector formed by the coordinates of the detection device and the coordinates of the virtual point, Representation vector The model, Representation vector Model.

[0086] S2, calculate the information entropy of the basic indicators, and obtain the indicator weight of each indicator in the basic indicators according to the calculated information entropy.

[0087] Among them, before calculating the information entropy, it is necessary to standardize the basic indicators. For example, given i indicators x 1 -x i , the standardized values ​​of each indicator data are y1, y2…, yk, and the processing process can be expressed as:

[0088] In the formula, x represents each indicator, and y represents the indicator after standardization. If each indicator is a negative indicator, it is expressed as:

[0089] Optionally, the information entropy of the basic indicators is calculated, and the indicator weight of each indicator in the basic indicators is obtained according to the calculated information entropy, specifically:

[0090] in:

[0091] In the formula, n represents the number of exponents, p ij Indicates the probability of state j in state i, Y ij represents the standardized value of each indicator data, W i Represents the weight of indicator i in the basic indicator, where if p ij =0, then

[0092] Among them, according to the calculation formula of the above information entropy, the information entropy of each indicator is calculated as E1, E2..., Ek, and the weight of each indicator is calculated by the information entropy:

[0093]

[0094] In the formula, E i Represents the information entropy of indicator i in the basic indicators.

[0095] S3, based on the basic indicators and the indicator weights of each indicator in the basic indicators, calculate the target parameters between each detection equipment and the flight target.

[0096] Optionally, the target parameters between the above detection equipment and the flying target are specifically:

[0097] D=W 1 ×E+W 2 ×M+W 3 ×C;

[0098] Where D represents the target parameter between the detection equipment and the flying target, W 1 represents the index weight of the Euclidean distance, E represents the Euclidean distance between the detection device and the flight target, W 2 Indicates the indicator weight of Manhattan distance, M represents the Manhattan distance between the detection device and the flight target, W 3 Represents the index weight of cosine distance, and C represents the cosine distance between the detection equipment and the flying target.

[0099] S4, determining the detection device with the smallest target parameter value as the target device, and using the target device to detect the flying target.

[0100] Among them, the resource scheduling method provided in this embodiment can effectively utilize sensor resources and realize efficient and high-quality sensor resource allocation; it provides a feasible solution to the problems of uneven equipment resource allocation and low efficiency in low-altitude detection and control systems in complex environments; this embodiment studies the intelligent control equipment scheduling algorithm in complex environments in combination with actual demand characteristics; first, based on Euclidean distance, Manhattan distance and cosine distance as basic indicators, it fully considers practical problems and measures application indicators; secondly, it calculates the information entropy of the three types of indicators, and uses the entropy weight method to weight them, which improves the rapid response capability of the detection system, and finally establishes a scheduling model for intelligent low-altitude control resources in complex environments, effectively improving the efficiency of the detection and control system.

[0101] Embodiment 2:

[0102] The embodiment of the present application provides an intelligent scheduling system for low-altitude control resources, which is applied to any intelligent scheduling method for low-altitude control resources in embodiment 1, such as Figure 4 As shown, including:

[0103] The first module is used to calculate the basic indicators between each detection device in the detection range and the flying target for each flying target existing in the detectable range.

[0104] The second module is used to calculate the information entropy of the basic indicators and obtain the indicator weight of each indicator in the basic indicators according to the calculated information entropy.

[0105] The third module is used to calculate the target parameters between each detection device and the flight target based on the basic indicators and the indicator weights of each indicator in the basic indicators.

[0106] The fourth module is used to determine the detection device with the smallest target parameter value as the target device, and use the target device to detect the flying target.

[0107] Embodiment 3:

[0108] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods in Embodiment 1 when executing the computer program.

[0109] Embodiment 4:

[0110] An embodiment of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any method in Example 1.

[0111] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for low-altitude control resources, characterized in that: The specific steps include: For each flying target within the detectable range, basic indicators between each detection device within the detection range and the flying target are calculated; Calculating the information entropy of the basic indicators, and obtaining the indicator weight of each indicator in the basic indicators according to the calculated information entropy; Based on the basic indicators and the indicator weights of the various indicators in the basic indicators, the target parameters between each detection device and the flight target are calculated; The detection device with the smallest value of the target parameter is determined as the target device, and the flying target is detected using the target device.

2. The intelligent scheduling method for low-altitude control resources according to claim 1 is characterized in that: The basic indicators include Euclidean distance, Manhattan distance and cosine distance, among which: The Euclidean distance is specifically: The Manhattan distance is specifically: M=|x1-x3|+|y1-y3|+|z1-z3|; Wherein, M represents the Manhattan distance between the detection device and the flying target, E represents the Euclidean distance between the detection device and the flying target, (x1, y1, z1) represents the coordinates of the flying target in the coordinate system established with the detectable range, the X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the coordinate system are longitude, latitude and altitude respectively, and (x3, y3, z3) represents the coordinates of the detection device in the coordinate system established with the detectable range.

3. The intelligent scheduling method for low-altitude control resources according to claim 2 is characterized in that: The cosine distance is specifically: The Z-axis coordinate of the virtual point is calculated by using a preset unit distance between the preset virtual point and the detection device, and a pitch angle between the detection device and the virtual point; The X-axis coordinate and the Y-axis coordinate of the virtual point are calculated by the projection point of the virtual point on the XY plane in the coordinate system and the position point where the projection point is perpendicular to the X-axis in the coordinate system; The cosine distance between the detection device and the flying target is calculated using the X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the virtual point.

4. The intelligent scheduling method for low-altitude control resources according to claim 3 is characterized in that: The Z-axis coordinates of the virtual point are specifically: In the formula, z2 represents the Z-axis coordinate of the virtual point, z3 represents the Z-axis coordinate of the detection device, represents the pitch angle between the detection device and the virtual point, d CB Indicates the preset unit distance between the virtual point and the detection device; The X-axis coordinate and Y-axis coordinate of the virtual point are specifically: In the formula, x2 represents the X-axis coordinate of the virtual point, y2 represents the Y-axis coordinate of the virtual point, x3 represents the X-axis coordinate of the detection device, and d CD It represents the distance between the virtual point and the projection point, σ represents the azimuth between the detection device and the projection point, which is calculated by the position point where the projection point is perpendicular to the X-axis in the coordinate system, and y3 represents the Y-axis coordinate of the detection device.

5. The intelligent scheduling method for low-altitude control resources according to claim 3 is characterized in that: The cosine distance between the detection device and the flying target is specifically: Where C represents the cosine distance between the detection device and the flying target. Represents the vector formed by the coordinates of the detection device and the coordinates of the flying target, Represents the vector formed by the coordinates of the detection device and the coordinates of the virtual point, Representation vector The model, Representation vector Model.

6. The intelligent scheduling method for low-altitude control resources according to claim 1 is characterized in that: The information entropy of the basic indicators is calculated, and the indicator weight of each indicator in the basic indicators is obtained according to the calculated information entropy, specifically: in: In the formula, E i represents the information entropy of index i in the basic index, n represents the number of indexes, p ij Indicates the probability of state j in state i, Y ij represents the standardized value of each indicator data, W i Represents the weight of indicator i in the basic indicators.

7. The intelligent scheduling method for low-altitude control resources according to claim 1 is characterized in that: The target parameters between the detection device and the flying target are specifically: D = W1×E+W2×M+W3×C; Where D represents the target parameter between the detection device and the flying target, W1 represents the index weight of the Euclidean distance, E represents the Euclidean distance between the detection device and the flying target, W2 represents the index weight of the Manhattan distance, M represents the Manhattan distance between the detection device and the flying target, W3 represents the index weight of the cosine distance, and C represents the cosine distance between the detection device and the flying target.

8. An intelligent dispatching system for low-altitude control resources, applied to the intelligent dispatching method for low-altitude control resources according to any one of claims 1 to 7, characterized in that: include: The first module is used to calculate the basic index between each detection device in the detection range and the flight target for each flight target within the detectable range; The second module is used to calculate the information entropy of the basic indicators and obtain the indicator weight of each indicator in the basic indicators according to the calculated information entropy; The third module is used to calculate the target parameters between each detection device and the flight target based on the basic indicators and the indicator weights of each indicator in the basic indicators; The fourth module is used to determine the detection device with the smallest value of the target parameter as the target device, and use the target device to detect the flying target.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the method according to any one of claims 1 to 7 is implemented when the processor executes the computer program.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which enable a computer to execute the method of any one of claims 1-7.

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