Unmanned aerial vehicle task execution risk studying and judging method and system based on thermal stealth function

Abstract

The invention relates to an unmanned aerial vehicle task execution risk studying and judging method and system based on a thermal stealth function, and the risk studying and judging method achieves the risk assessment of a target unmanned aerial vehicle in a task execution process by comprehensively assessing the influence of a friendly army and an enemy army and taking a task risk index as an assessment index. According to the method, the number, distance and function factors of unmanned aerial vehicles or attack weapons of both the enemy army and the our army are comprehensively considered, the stealth correction factor is obtained through calculation, the corrected task risk index is obtained, and then a scientific and reasonable judgment and decision basis is provided for decision making before task execution.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) risk assessment, and in particular to a method and system for assessing the risk of UAV mission execution based on a thermal stealth function. Background Art

[0002] With the continuous advancement of drone technology, drones with thermal stealth capabilities have been widely used in high-risk missions such as military reconnaissance and attack. Traditional drone missions are usually subject to strict distance restrictions and target constraints, which limit the flexibility and efficiency of the mission. Drones with thermal stealth capabilities can get closer to the target area, thereby increasing the success rate of the mission.

[0003] However, with the development of modern stealth drone technology, modern anti-stealth technology and air defense systems are also becoming more and more perfect. When the distance between drones and targets is shortened when performing tasks, the risks they face also increase. This requires that when planning tasks, the risks of performing tasks need to be accurately assessed. In existing studies, the Chinese invention application "UAV flight risk intelligent analysis and control system based on data analysis" (publication number: CN118732710A, publication date: October 1, 2024) sets the flight area according to the type of signal in the dynamic signal. The flight area includes a safe area, a warning area, and a dangerous area to complete the regional risk assessment. The Chinese patent application "A real-time risk assessment system and method for submarine-launched drones" (publication number: CN118643275A, publication date: September 13, 2024) obtains the real-time operating status of submarine-launched drone related systems, drone operating status, and operating environment data based on a distributed multi-sensor network, and invents a real-time risk assessment system and method for submarine-launched drones. The Chinese invention application "A method for assessing the operation risks of UAVs in an air-ground collaborative manner" (publication number: CN117010078A, publication date: November 7, 2023) is based on the probability density distribution of system failures and track errors and kinematic models, and evaluates the operation risks of UAVs through multiple indicators such as personnel damage, economic losses and noise impact.

[0004] However, the existing technology still lacks a systematic and effective method to comprehensively evaluate the impact of friendly and enemy forces on mission execution risk, especially in the case of coordinated operations of multiple drone swarms. Therefore, there is an urgent need for a UAV mission execution risk assessment method based on thermal stealth function to provide effective risk assessment in a dynamic environment. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a method for assessing the risk of UAV mission execution based on thermal stealth function, which can accurately assess the impact of friendly and enemy forces during the mission execution, thereby optimizing the UAV mission planning.

[0006] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is: A method for evaluating the risk of UAV mission execution based on thermal stealth function is proposed. The method comprehensively evaluates the influence of friendly forces and enemy forces, and uses the mission risk index as an evaluation index to achieve risk evaluation of the target UAV during the mission execution process. The expression of the mission risk index is as follows: R=I enemy / (I friendly +1) Where R is the task risk index, I friendly is the friendly influence index, I enemy The enemy threat index.

[0007] Preferably, the friendly force influence index is expressed as follows:

[0008] Where m is the number of friendly UAV types, W j is the weight coefficient of the jth friendly UAV, N j is the number of friendly UAVs of type j, D j is the distance between the jth friendly UAV and the target UAV, F j is the functional coefficient of the j-th friendly UAV.

[0009] Preferably, the enemy threat index is expressed as follows:

[0010] Where n is the number of enemy attack systems, v k is the weight coefficient of the kth enemy attack system, M k is the number of the kth enemy attack system, R k is the distance between the kth enemy attack system and the target UAV, H k is the functional coefficient of the kth enemy attack system; where the enemy attack system includes enemy drones and attack weapons.

[0011] Preferably, the risk assessment method further comprises correcting the mission risk index by using the stealth protection coefficient as an evaluation index to achieve risk assessment of the target UAV during the mission execution; wherein the corrected mission risk index is: R'=R•exp(-0.01•β•D) Where R' is the modified mission risk index, D is the distance between the friendly UAV and the enemy attack system, and β is the stealth protection coefficient.

[0012] Preferably, the expression of the stealth protection coefficient is: β=α1•P+α2•(1 / D)+α3•exp(-kT)+α4•[E / (E+k)] Where D is the distance between the target UAV and the mission target, T is the ambient temperature, E is the threat index of the surrounding enemy forces, α1, α2, α3, α4 are weight coefficients, and k is a constant.

[0013] Furthermore, a UAV mission execution risk assessment system based on thermal stealth function is also adopted, including: Basic information collection module, used to obtain basic information of friendly and enemy forces, including the number, distance and function information of friendly and enemy forces; The influence index calculation module is used to calculate the friendly force influence index and the enemy force threat index; The mission risk index calculation module is used to calculate the mission risk index of the target UAV based on the friendly force influence index and the enemy force threat index.

[0014] Preferably, the risk assessment system further includes: A stealth protection coefficient calculation module is used to calculate the stealth protection coefficient of the target UAV; The risk correction module is used to correct the mission risk index of the target UAV according to the stealth protection coefficient.

[0015] Furthermore, a computer-readable storage medium storing one or more programs is also used, wherein the one or more programs include instructions, characterized in that when the instructions are executed by a computing device, the computing device executes the method as described above.

[0016] Furthermore, an electronic device is also used, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as described above.

[0017] Beneficial effects: The method for evaluating the risk of UAV mission execution based on thermal stealth function provided by the present invention can accurately evaluate the risk of UAVs in executing missions by quantifying the influencing factors of friendly forces and enemy forces. This method not only improves the success rate and concealment of missions, but also provides strong support for the coordinated operations of UAV clusters. Compared with the prior art, the present invention can effectively integrate dynamic environmental factors, realize risk assessment in UAV mission planning, and provide a basis for subsequent mission execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the content of the present invention clearer, the present invention is described in detail below in conjunction with specific embodiments.

[0020] Assume that in a military operation, a drone cluster with thermal stealth function consists of 3 drones, which perform reconnaissance missions in a specific area. During the mission execution, the influence factors of friendly forces and enemy forces need to be considered.

[0021] Friendly UAV situation: Number of drones: 5 Average distance between various types of drones: 500 meters Function: 3 are reconnaissance drones, 2 are attack drones Function coefficient: Reconnaissance drone F recon =0.8, attack drone F attack =1.0.

[0022] Enemy drones and attack weapons: Number of enemy drones: 4 Number of enemy weapons: 3 Average distance between various types of drones: 300 meters Function: 2 attack drones, 2 reconnaissance drones Function coefficient: Reconnaissance UAV H recon =0.7, attack drone H attack =1.0.

[0023] To set the risk level: Level 1: Low risk [0.001, 0.005): Friendly support is strong, enemy threat is low, the drone can perform tasks at a relatively safe distance, and the stealth protection effect is significant; Level 2: Medium risk [0.005, 0.01): Moderate friendly support and moderate enemy threat. The drone needs to be alert to enemy activities when performing missions. The stealth effect can effectively reduce certain risks. Level 3: High risk level [0.01, 0.02): Friendly support is weak, enemy threats are greater, the UAV may face the threat of enemy anti-stealth technology when performing missions, and the concealment and success rate of the mission are low; Level 4: Extremely high risk level [0.02, +∞): Friendly support is extremely weak, enemy threat is extremely high, drone missions are almost at risk of being detected and destroyed by the enemy, and stealth capabilities cannot effectively avoid attacks from enemy anti-stealth technology.

[0024] According to the above-mentioned UAV mission execution risk assessment method based on thermal stealth function, the mission execution risk of the aforementioned UAV cluster with thermal stealth function is assessed: Step 1: Calculate the friendly influence index I friendly : .

[0025] Friendly UAV weight calculation: Assume that the weight of all friendly drones is W j is 1, distance D j 5000 meters; Calculate the impact of friendly reconnaissance and attack drones.

[0026] Reconnaissance UAV (3 units): ; Attack drones (2): ; Total friendly influence index: I friendly =0.0048+0.0040=0.0088.

[0027] Step 2: Calculate the enemy threat index I enemy : .

[0028] Calculation of enemy drone and attack weapon weights: Assume that the weights of all enemy targets are v k is 1, the distance between the enemy UAV and the attack weapon is R k 3000 meters; Attack drones (2): ; Reconnaissance UAVs (2): ; Enemy attack weapons (3): ; Total enemy threat index: I enemy =0.00667+0.00467+0.01=0.02134.

[0029] Step 3: Comprehensively evaluate the risk index R: R=I enemy / (I friendly +1)= 0.02134 / (0.0088+1)≈0.02112.

[0030] Step 4: Calculation of stealth protection factor The stealth protection coefficient function is designed as follows: β=f(P,D,T,E)=α1•P+α2•(1 / D)+α3•exp(-kT)+α4•[E / (E+k)].

[0031] Assumed parameters: Stealth performance P=0.85 Distance D = 200 meters Ambient temperature T = 30°C Enemy threat index E=5 Weight coefficients α1=0.5, α2=0.3, α3=0.2, α4=0.4 Constant k = 10 Substitute to calculate the stealth protection coefficient: β=0.5·0.85+0.3·(1 / 200)+0.2·exp(-10·30)+0.4·[5 / (5+10)] ≈0.5618.

[0032] Step 5: Modify the risk index R' The impact of introducing the stealth function: R'=R•exp(-0.01×β•D) Calculate the modified risk index: R'=0.02112·exp(-0.01·0.5618·200)≈0.006866.

[0033] According to the risk level, this military operation is of medium risk. Friendly support is moderate and enemy threat is moderate. UAVs need to be alert to enemy activities when performing missions, and stealth can effectively reduce certain risks.

[0034] Through the above calculations, we demonstrated how to apply the UAV mission execution risk assessment method based on thermal stealth function. By utilizing the thermal stealth function, the UAV can get closer to the target area, thereby increasing the probability of mission success while avoiding detection by the enemy. This can reduce the risk of exposure to enemy countermeasures and increase operational flexibility. Taking into account the influence of friendly and enemy forces, the revised mission risk index is calculated. After introducing the stealth protection coefficient function, the stealth protection effect of the UAV can be evaluated more dynamically and flexibly, providing more accurate data support for risk assessment.

[0035] The present invention also provides a UAV mission execution risk assessment system based on thermal stealth function, comprising: Basic information collection module, used to obtain basic information of friendly and enemy forces, including the number, distance and function information of friendly and enemy forces; The influence index calculation module is used to calculate the friendly force influence index and the enemy force threat index; The mission risk index calculation module is used to calculate the mission risk index of the target UAV based on the friendly force influence index and the enemy force threat index.

[0036] Preferably, the risk assessment system further includes: A stealth protection coefficient calculation module is used to calculate the stealth protection coefficient of the target UAV; The risk correction module is used to correct the mission risk index of the target UAV according to the stealth protection coefficient.

[0037] The technical solution of the above-mentioned risk assessment system is similar to the technical solution of the above-mentioned risk assessment method, which will not be repeated here.

[0038] Based on the same technical solution, the present invention also discloses an electronic device, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-mentioned UAV mission execution risk assessment method based on thermal stealth function.

[0039] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes the above-mentioned UAV mission execution risk assessment method based on thermal stealth function.

[0040] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for evaluating the risk of UAV mission execution based on thermal stealth function, characterized in that: The risk assessment method comprehensively evaluates the impact of friendly forces and enemy forces, and uses the mission risk index as an evaluation index to achieve risk assessment of the target UAV during the mission execution process; wherein, the expression of the mission risk index is as follows: R=I enemy / (I friendly +1), where R is the mission risk index, I friendly is the friendly influence index, I enemy The enemy threat index.

2. The risk assessment method according to claim 1, characterized in that: The expression of friendly force influence index is as follows: , where m is the number of friendly UAV types, W j is the weight coefficient of the jth friendly UAV, N j is the number of friendly UAVs of type j, D j is the distance between the jth friendly UAV and the target UAV, F j is the functional coefficient of the j-th friendly UAV.

3. The risk assessment method according to claim 1 is characterized in that: The expression of enemy threat index is as follows: , where n is the number of enemy attack systems, v k is the weight coefficient of the kth enemy attack system, M k is the number of the kth enemy attack system, R k is the distance between the kth enemy attack system and the target UAV, H k is the functional coefficient of the kth enemy attack system; where the enemy attack system includes enemy drones and attack weapons.

4. The risk assessment method according to claim 1, characterized in that: The risk assessment method also includes correcting the mission risk index through the stealth protection coefficient as an evaluation indicator to achieve risk assessment of the target UAV during the mission execution; wherein the corrected mission risk index is: R'=R•exp(-0.01•β•D), wherein R' is the corrected mission risk index, D is the distance between the friendly UAV and the enemy attack system, and β is the stealth protection coefficient.

5. The risk assessment method according to claim 1 is characterized in that: The expression of stealth protection coefficient is: β=α1•P+α2•(1 / D)+α3•exp(-kT)+α4•[E / (E+k)], where D is the distance between the target UAV and the mission target, T is the ambient temperature, E is the threat index of the surrounding enemy forces, α1, α2, α3, α4 are weight coefficients, and k is a constant.

6. A UAV mission execution risk assessment system based on thermal stealth function, characterized in that: include: Basic information collection module, used to obtain basic information of friendly and enemy forces, including the number, distance and function information of friendly and enemy forces; The influence index calculation module is used to calculate the friendly force influence index and the enemy force threat index; The mission risk index calculation module is used to calculate the mission risk index of the target UAV based on the friendly force influence index and the enemy force threat index.

7. The risk assessment system according to claim 6, characterized in that: Also includes: A stealth protection coefficient calculation module is used to calculate the stealth protection coefficient of the target UAV; The risk correction module is used to correct the mission risk index of the target UAV according to the stealth protection coefficient.

8. A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that: When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 5.

9. An electronic device, characterized in that: The method comprises one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as claimed in any one of claims 1 to 5.

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