Method and device for dynamically sensing target threat area of unmanned aerial vehicle

Abstract

The embodiment of the invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for dynamically sensing a target threat area of an unmanned aerial vehicle. The embodiment of the invention provides a method for dynamically sensing a target threat area of an unmanned aerial vehicle. The method comprises the following steps: respectively establishing a terrain threat model, an artificial flight target threat model, a bird threat model and a sensor threat model; determining the weight of each threat model by using a Critic comprehensive objective weighting method and a dynamic weight change theory; and judging whether the area is safe or not according to all threat models and weights thereof. The embodiment of the invention provides a method and a device for dynamically sensing a target threat area of an unmanned aerial vehicle, electronic equipment and a storage medium, and can provide a more accurate method for dynamically sensing the target threat area of the unmanned aerial vehicle.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of unmanned aerial vehicles, and in particular to a method and device for dynamically sensing a target threat area of ​​an unmanned aerial vehicle. Background Art

[0002] With the development of Unmanned Aerial Vehicle (UAV) technology, the autonomous flight planning capability of UAVs has received more and more attention from scientific researchers. As the functions of UAVs continue to develop, the complex environmental impacts they face are becoming more complex and changeable. Therefore, conducting a comprehensive threat assessment of the UAV flight area and determining the UAV safe flight area based on the assessment results is one of the important means to improve UAV flight safety.

[0003] In real environments, different factors have different degrees of impact on the flight safety of UAVs. In addition, with the changes in flight position, status, and mission, the impact and importance of different factors on UAVs are constantly changing accordingly. However, the traditional flight threat zone planning method mainly uses a fixed weight algorithm, which does not change with the scene, and is not applicable to scenes where threats are closely related to the flight status of the aircraft.

[0004] Therefore, in response to the above problems, a more accurate dynamic perception method of UAV target threat area is urgently needed. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for dynamically sensing a target threat area of ​​an unmanned aerial vehicle, which can provide a more accurate method for dynamically sensing a target threat area of ​​an unmanned aerial vehicle.

[0006] In a first aspect, an embodiment of the present invention provides a method for dynamically sensing a target threat area of ​​an unmanned aerial vehicle, comprising:

[0007] Establish terrain threat model, man-made flying target threat model, flying bird threat model and sensor threat model respectively;

[0008] The weights of each threat model are determined using the Critic comprehensive objective weighting method and dynamic contingency theory;

[0009] Determine whether the area is safe based on all threat models and their weights.

[0010] In a possible design, the terrain threat model is:

[0011]

[0012] Among them, Δh represents the maximum terrain drop between adjacent plots, Hmax Represents the maximum flight altitude of the aircraft, h i Represents the altitude of the central plot, d mp Represents the horizontal distance between the aircraft and the central plot;

[0013] The artificial flying target threat model is:

[0014]

[0015] Among them, d min and d max They represent the upper and lower bounds of the effective flight distance of the UAV, d AA,p is the relative distance between the unmanned aerial vehicle and the artificial flying target;

[0016] The Flying Bird threat model is:

[0017]

[0018] Among them, r max and r max Respectively represent the maximum distance and minimum distance of the bird activity area, d AM,p is the relative distance between the UAV and the bird;

[0019] The sensor threat model is:

[0020] T r =α×Pr r ×b

[0021] Among them, α is the detection probability gain coefficient, Pr r When the distance between the target and the sensor is d r is the probability of being detected when , and b is the detection probability of the sensor for the target.

[0022] In one possible design, the target is at a distance d from the sensor. r The probability of being detected is:

[0023]

[0024] Among them, Pr0 is the probability of the target being detected when it is at the maximum detection effective distance R0 of the sensor.

[0025] In a possible design, the weights of each threat model are determined by using the Critic comprehensive objective weighting method and the dynamic contingency theory, including:

[0026] The initial weight of each threat type is calculated using the Critic comprehensive objective weighting method;

[0027] The initial weights are modified using dynamic variable weight theory to obtain weights of various threat models.

[0028] In a possible design, the initial weight of each threat type is calculated using the Critic comprehensive objective weighting method, including:

[0029] Gridding the map to obtain multiple grids;

[0030] Evaluate each grid for different threat types and obtain an evaluation matrix;

[0031] Performing positive processing on the evaluation matrix and calculating the information carrying capacity of each threat type;

[0032] The initial weights of different threat types are calculated according to the information carrying capacity.

[0033] In a possible design, the initial weights are modified by using the dynamic variable weight theory to obtain the weights of each threat model, including:

[0034] Performing a first-order linear function correction on the terrain threat model to obtain a terrain threat correction model;

[0035] Performing linear function plus exponential function correction on the sensor threat model to obtain a sensor correction model;

[0036] The artificial flying target threat model and the flying bird threat model are respectively corrected by a linear function plus a logarithmic function to obtain an artificial flying target threat correction model and a flying bird threat correction model respectively;

[0037] The weight function is used to solve the equilibrium function composed of all correction models to obtain the state weight vector;

[0038] The initial weights and state contingency vectors are used to obtain the modified weights of each threat model.

[0039] In one possible design, the equalization function is:

[0040] B(T g ,T b ,T AA ,T AM )=B g +B b +B AA +B AM

[0041] Among them, B g , B b , B AA and B AMThey are terrain threat correction model, sensor threat correction model, flying target threat correction model and flying bird threat correction model;

[0042] The state contingency vector is:

[0043]

[0044] The modified weights of each threat model are:

[0045]

[0046] Wherein, W is an initial weight vector, including initial weights w of multiple threat types, and W' is a vector composed of modified threat models, including weights w' of multiple modified threat types.

[0047] In a second aspect, an embodiment of the present invention further provides a device for dynamically sensing a target threat area of ​​an unmanned aerial vehicle, comprising:

[0048] A modeling unit, used to respectively establish a terrain threat model, an artificial flying target threat model, a flying bird threat model and a sensor threat model;

[0049] The calculation unit is used to determine the weight of each threat model by using the Critic comprehensive objective weighting method and dynamic contingency theory;

[0050] The judgment unit is used to judge whether the area is safe based on all threat models and their weights.

[0051] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0052] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0053] The embodiment of the present invention provides a method, device, electronic device and storage medium for dynamic perception of target threat areas of unmanned aerial vehicles, a method for dynamic perception of target threat areas of unmanned aerial vehicles. Firstly, a threat index system of unmanned aerial vehicles is established by comprehensively considering factors such as terrain, aerial flight targets and sensor detection, and modeling is performed according to the threat index system; then, the weight of the threat index of the unmanned aerial vehicle is determined by using the CRITIC comprehensive objective weighting method and dynamic variable weight theory; then, the current threat size of the unmanned aerial vehicle is calculated; finally, the safety threshold of the unmanned aerial vehicle is set by comprehensively considering the parameters of the unmanned aerial vehicle itself and the actual situation to determine the threat area and safe flight area of ​​the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 This is a flow chart of a method for dynamically sensing a target threat area of ​​an unmanned aerial vehicle provided by an embodiment of the present invention;

[0056] Figure 2 It is a critical schematic diagram of sensor detection provided by an embodiment of the present invention;

[0057] Figure 3 It is a schematic diagram of a Bekaa Valley topographic map provided by an embodiment of the present invention;

[0058] Figure 4 It is a schematic diagram of comprehensive threats to an unmanned aerial vehicle during flight provided by an embodiment of the present invention;

[0059] FIG5 is a threat visualization result of various threat factors provided by an embodiment of the present invention;

[0060] Figure 6 It is a relationship between the flight altitude of an unmanned aerial vehicle and each threat weight provided by an embodiment of the present invention;

[0061] Figure 7 A relationship between a flight position of an unmanned aerial vehicle and each threat weight is provided by an embodiment of the present invention;

[0062] Figure 8 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0063] Fig. 9 It is a structural diagram of a dynamic perception device for target threat areas of unmanned aerial vehicles provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0064] 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, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0065] The specific implementation of the above concept is described below.

[0066] Please refer to Figure 1 The embodiment of the present invention provides a method for dynamically sensing a target threat area of ​​an unmanned aerial vehicle, the method comprising:

[0067] Step 100, respectively establishing a terrain threat model, an artificial flying target threat model, a flying bird threat model and a sensor threat model;

[0068] Step 102, using the Critic comprehensive objective weighting method and dynamic contingency theory to determine the weight of each threat model;

[0069] Step 104: determine whether the area is safe based on all threat models and their weights.

[0070] In an embodiment of the present invention, firstly, a UAV threat index system is established by comprehensively considering factors such as terrain, aerial flight targets, and sensor detection, and a model is built based on the threat index system; then, the weights of the UAV threat index are determined using the CRITIC comprehensive objective weighting method and the dynamic variable weight theory; then, the current threat level of the UAV is calculated; finally, the UAV safety threshold is set based on the UAV's own parameters and actual conditions to determine the UAV threat area and safe flight area.

[0071] Described below Figure 1 How the various steps are performed.

[0072] First, with respect to step 100, a UAV threat index system is established by comprehensively considering factors such as terrain, aerial flight targets, and sensor detection, and a model is built based on the threat index system.

[0073] Terrain Threat Model:

[0074] Terrain threats to unmanned aerial vehicles during flight:

[0075]

[0076] In the formula, Δh represents the maximum terrain drop between adjacent plots, H max Represents the maximum flight altitude of the aircraft, h i represents the altitude of the central plot, d mp Represents the horizontal distance between the aircraft and the center plot.

[0077] Airborne targets:

[0078] Aerial targets can be roughly divided into two categories: man-made flying target threats and aerial bird threats.

[0079] The threat area of ​​artificial flying targets is described by the upper and lower bounds of the effective flight distance, denoted by d min and d max Assume that the UAV is located at the artificial flight target unit A at a relative distance d AA,p The target is threatened by artificial flying targets. AA The following formula can be used for modeling:

[0080]

[0081] When a target is simultaneously within the effective flight range of multiple artificial flying targets, the average of the artificial flying target threats is taken as the comprehensive threat value.

[0082] The activity area of ​​​​the flying birds in the sky can be expressed by the maximum distance r max and the minimum distance r min To characterize the effective threat distance. Assume that the UAV is at a relative distance d from the target group A of flying birds in the sky. AM,p If the target is threatened by birds in the sky, the target is threatened by birds in the sky. AM The following formula can be used for modeling:

[0083]

[0084] In the formula, R k represents the threat radius of the kth bird to the UAV target; σ represents the flight path error of the bird.

[0085] Sensor detection unit factors:

[0086] Please refer to Figure 2 , equivalent earth radius R 地 , d 机 is the straight-line distance between the target UAV and the sensor, d 障 is the straight-line distance between the obstacle and the sensor, and the altitude of the UAV is H 机 , the altitude of the obstacle is H 障 , the sensor's altitude is H 传 .

[0087] In addition, β 障 It represents the angle between the straight line from the top of the sensor to the top of the obstacle and the line from the sensor to the center of the earth. 机 Critical detection distance of target d 临 (H 机 )Calculation formula:

[0088]

[0089] L′x = R 地 + H x (7)

[0090] In the formula, L′ x represents the distance from point x to the center of the earth.

[0091] Therefore, if it is known that under the influence of obstacles in a certain direction, the critical detection distance of the sensor for the height layer H is d 临 (H). Then when the distance d between the target and the sensor is greater than d 临 (H), the target is in the blind area of the sensor's field of view. Conversely, the target is detected by the sensor. When there are multiple obstacles in the direction of the line connecting the sensor and the target unmanned aerial vehicle, the critical detection distance of the sensor is actually the shortest visible distance between the sensor and the target.

[0092] Assume that the probability of the target being detected when it is at the maximum detection effective distance R0 of the sensor is Pr0. Then when the distance between the target and the sensor is d r the probability Pr r of being detected can be modeled by the following formula:

[0093]

[0094] In the formula, d r < R0. Therefore, the threat Tr of the target by the sensor detection can be calculated by the following formula.

[0095] T r = α × Pr r × b (9)

[0096] In the formula, α is the detection probability gain coefficient, which is determined by the stealth performance of different types of unmanned aerial vehicles. The better the stealth performance, the smaller α is, and b is the detection result of the sensor for the target.

[0097] In summary, the comprehensive threat calculation formula during the target flight can be initially obtained:

[0098] T 总 = w g T g + w AA T AA + w AM T AM + w r T r (10)

[0099] In the formula, w represents the weights of different threat values respectively.

[0100] Then, for step 102, the weights of the UAV threat indicators are determined using the CRITIC comprehensive objective weighting method and the dynamic variable weight theory.

[0101] The initial weight of each threat type is calculated using the Critic comprehensive objective weighting method:

[0102] The Critic method is an objective weighting method that comprehensively measures indicators by comparing the strength of the comparison and the conflict between the indicators. It takes into account the correlation between the indicators while considering the variability of the indicators, and uses the objective attributes of the data itself to make scientific evaluations. Specifically, we divide the map into m×n grids by gridding, and each point on the map can be regarded as an object to be evaluated. For each object point to be evaluated, there are k evaluation indicators, which are T in this article. g , T AA , T AM , T r The evaluation values ​​of the four threats can transform the problem into the data X=(x ij ) mn×k The matrix (i.e. the evaluation matrix) is calculated.

[0103] First, all threat indicators in the evaluation matrix need to be positively normalized. In this paper, since threat indicators are negative indicators, the following formula is used for positive normalization:

[0104]

[0105] Then, the information carrying capacity C of each indicator needs to be calculated j :

[0106] C j =σ j f j (12)

[0107] In the formula, σ j represents the contrast of the jth threat, f j represents the contradiction of the j-th threat and is calculated as follows:

[0108]

[0109] In the formula, r ij Represents the Pearson correlation coefficient between indicator i and indicator j.

[0110] The initial weights w of different threats can be calculated j :

[0111]

[0112] Dynamic variable weight theory is used to modify threat weights:

[0113] In addition, since the terrain threat changes less and tends to change linearly, a first-order linear function is used to model it in this paper:

[0114] B g =T g (16)

[0115] The other three threats use nonlinear change functions. Specifically, when the aircraft is in the visible area and outside the sensor's field of view, the sensor threat plays an important role; when the aircraft enters the sensor's field of view, a small change will not cause a drastic change, but the weight of the air defense threat needs to be increased accordingly. Therefore, for sensor detection threats, this article chooses a linear function plus an exponential function:

[0116]

[0117] In the formula, λ b Represents the adjustment factor of the sensor detection threat. For the threat of artificial flying targets and birds in the air, this paper chooses the form of a linear function plus a logarithmic function:

[0118] B AA =T AA +λ AA ln(T AA +1) (18)

[0119] B AM =T AM +λ AM (lnT AM +1) (19)

[0120] In the formula, λ AA and λ AM They are the adjustment factors for the threat of man-made flying targets and the threat of flying birds in the sky, respectively.

[0121] To sum up, the equilibrium function can be obtained as shown in the formula.

[0122] B(T g ,T r ,T AA ,T AM )=B g +B r +B AA +B AM (20)

[0123] Then, the state variable weight vector can be solved according to the variable weight function:

[0124]

[0125] Finally, the constant weight vector W of each threat initial weight factor and the state variable weight vector S X Multiply them together to calculate the normalized Hadamard product:

[0126]

[0127] In the formula, ⊙ is the dot product operation, and each element in W′ is the weight value corrected by the comprehensive variable weight calculation.

[0128] Finally, for step 104, calculate the current threat size of the UAV

[0129] The formula for the comprehensive threat value of unmanned aerial vehicles has been revised as follows:

[0130] T 总 =w' g T g +w' AA T AA +w' AM T AM +w' r T r (twenty three)

[0131] The UAV safety threshold is set based on the UAV's own parameters and actual conditions to determine the UAV threat area and safe flight area.

[0132] Assuming that the safety threshold is set to AN, the discrimination result WA of the threat area and the safe flight area is determined according to the following formula:

[0133]

[0134] Please refer to Figure 3 Taking the classic Bekaa Valley as the scene, a simulation experimental scene with a range of 100KM×100KM was constructed.

[0135] Figure 4 This is the effect of the proposed method for determining the safe flight zone for unmanned aerial vehicles. The blue dots in the figure represent ground units, including sensors, artificial flying targets, birds in the air, etc. The green area is the safe flight zone, and the red area is the dangerous area. The darker the red, the higher the threat level to the aircraft.

[0136] In addition, in order to verify the role of each threat factor in the comprehensive threat value, we visualized each threat. It can be clearly seen from Figure 5 that the high threat area in the terrain threat is basically consistent with the mountainous area.

[0137] from Figure 6 It can be clearly seen that the weights of sensor detection threats and bird threats vary with flight altitude. Figure 7 It can be seen from the figure that different flight positions have an impact on the weights of various threat factors.

[0138] like Figure 8 , Fig. 9 As shown, an embodiment of the present invention provides a device for dynamically sensing target threat areas of unmanned aerial vehicles. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 8 As shown, it is a hardware architecture diagram of an electronic device where a dynamic sensing device for a target threat area of ​​an unmanned aerial vehicle provided by an embodiment of the present invention is located. Figure 7 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Fig. 9 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a dynamic perception device for target threat areas of unmanned aerial vehicles, including:

[0139] A modeling unit, used to respectively establish a terrain threat model, an artificial flying target threat model, a flying bird threat model and a sensor threat model;

[0140] The calculation unit is used to determine the weight of each threat model by using the Critic comprehensive objective weighting method and dynamic contingency theory;

[0141] The judgment unit is used to judge whether the area is safe based on all threat models and their weights.

[0142] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a dynamic perception device for a target threat area of ​​an unmanned aerial vehicle. In other embodiments of the present invention, a dynamic perception device for a target threat area of ​​an unmanned aerial vehicle may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0143] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0144] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for dynamic perception of a target threat area of ​​an unmanned aerial vehicle in any embodiment of the present invention is implemented.

[0145] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a method for dynamic perception of a target threat area of ​​an unmanned aerial vehicle in any embodiment of the present invention.

[0146] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0147] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0148] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0149] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0150] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0151] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0152] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically sensing a target threat area of ​​an unmanned aerial vehicle, characterized in that: include: Establish terrain threat model, man-made flying target threat model, flying bird threat model and sensor threat model respectively; The weights of each threat model are determined using the Critic comprehensive objective weighting method and dynamic contingency theory; Determine whether the area is safe based on all threat models and their weights.

2. The method according to claim 1, characterized in that: The terrain threat model is: Among them, Δh represents the maximum terrain drop between adjacent plots, H max Represents the maximum flight altitude of the aircraft, h i Represents the altitude of the central plot, d mp Represents the horizontal distance between the aircraft and the central plot; The artificial flying target threat model is: Among them, d min and d max They represent the upper and lower bounds of the effective flight distance of the UAV, d AA,p is the relative distance between the unmanned aerial vehicle and the artificial flying target; The Flying Bird threat model is: Among them, r max and r max Respectively represent the maximum distance and minimum distance of the bird activity area, d AM,p is the relative distance between the UAV and the bird; The sensor threat model is: T r =α×Pr r ×b Among them, α is the detection probability gain coefficient, Pr r When the distance between the target and the sensor is d r is the probability of being detected, and b is the detection probability of the sensor for the target.

3. The method according to claim 2, characterized in that The distance between the target and the sensor is d r The probability of being detected is: Among them, Pr0 is the probability of the target being detected when it is at the maximum detection effective distance R0 of the sensor.

4. The method according to claim 1, characterized in that: The weights of each threat model are determined by using the Critic comprehensive objective weighting method and dynamic contingency theory, including: The initial weight of each threat type is calculated using the Critic comprehensive objective weighting method; The initial weights are modified using dynamic variable weight theory to obtain weights of various threat models.

5. The method according to claim 4, characterized in that The initial weight of each threat type is calculated using the Critic comprehensive objective weighting method, including: Gridding the map to obtain multiple grids; Evaluate each grid for different threat types and obtain an evaluation matrix; Performing positive processing on the evaluation matrix and calculating the information carrying capacity of each threat type; The initial weights of different threat types are calculated according to the information carrying capacity.

6. The method according to claim 5, characterized in that The method of modifying the initial weights by using the dynamic variable weight theory to obtain the weights of each threat model includes: Performing a first-order linear function correction on the terrain threat model to obtain a terrain threat correction model; Performing linear function plus exponential function correction on the sensor threat model to obtain a sensor correction model; The artificial flying target threat model and the flying bird threat model are respectively corrected by a linear function plus a logarithmic function to obtain an artificial flying target threat correction model and a flying bird threat correction model respectively; The weight function is used to solve the equilibrium function composed of all correction models to obtain the state weight vector; The initial weights and state contingency vectors are used to obtain the modified weights of each threat model.

7. The method according to claim 6, characterized in that The balance function is: B(T g ,T b ,T AA ,T AM )=B g +B b +B AA +B AM Among them, B g , B b , B AA and B AM They are terrain threat correction model, sensor threat correction model, flying target threat correction model and flying bird threat correction model; The state contingency vector is: The modified weights of each threat model are: Wherein, W is an initial weight vector, including initial weights w of multiple threat types, and W' is a vector composed of modified threat models, including weights w' of multiple modified threat types.

8. A dynamic sensing device for target threat areas of unmanned aerial vehicles, characterized in that: include: A modeling unit, used to respectively establish a terrain threat model, an artificial flying target threat model, a flying bird threat model and a sensor threat model; The calculation unit is used to determine the weight of each threat model by using the Critic comprehensive objective weighting method and dynamic contingency theory; The judgment unit is used to judge whether the area is safe based on all threat models and their weights.

9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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