VIKOR-based dynamic decision-making method and system in airborne directional energy system

Abstract

The invention relates to the technical field of target threat assessment and decision making, in particular to a VIKOR-based dynamic decision making method and system in an airborne directional energy system. According to the method, on the basis of a three-way decision theory, a compromise sorting value is calculated by adopting a VIKOR method under intuitionistic fuzzy information, so that the conditional probability of external target evaluation is determined; meanwhile, utilizing intuitionistic fuzzy information to construct a loss function matrix of each target attribute, and calculating a dynamic comprehensive loss function and a decision threshold value of each target through matrix aggregation; afterwards, an algorithm calculates a comprehensive threshold value and formulates a decision rule meeting the requirements of an airborne laser orientation energy system, so that scientificity of target threat assessment and reasonable allocation of resources are ensured, and autonomous decision of airborne laser orientation energy to a target in a dynamic environment is completed. The technical problems of difficulty in determining a priority processing target, low decision-making efficiency and poor resource allocation efficiency in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of target threat assessment and decision-making, and in particular relates to a dynamic decision-making method and system based on VIKOR in an airborne directed energy system. Background Art

[0002] Aircraft-based laser directed energy systems show significant promise in technological applications. Airborne laser directed energy systems often operate in dynamic environments with close connections and complex interactions, making them typical complex adaptive systems. Given the current trends of diversified situations, diverse technological approaches, and increasingly complex environments, optimizing resource allocation during decision-making is crucial for the efficient operation of airborne laser directed energy systems. Within airborne laser directed energy systems, target threat assessment involves quantitative calculation and prioritization of targets based on their state information. It is a crucial step in the dynamic "perception-adjustment-decision-execution" (OODA) cycle and can provide powerful decision support for optimal resource allocation. Traditional target threat assessment methods include those based on multi-attribute decision-making theory, neural network-based assessments, Bayesian network-based estimations, and fuzzy set theory-based methods.

[0003] However, differences in target threat assessment methods' focus and implementation mechanisms often lead to varying final ranking results, making it difficult to prioritize targets in the dynamic application of airborne laser directed energy systems. Furthermore, traditional methods rely on manual intervention or preset thresholds to assign priorities, reducing decision-making efficiency and failing to adapt to complex and changing application scenarios. Furthermore, traditional two-way decision-making can lead to erroneous judgments when insufficient information is available, reducing resource allocation efficiency.

[0004] Based on this, the present invention proposes an objective VIKOR-based dynamic decision-making method and system in an airborne directed energy system to effectively represent the target uncertainty situation information and achieve efficient decision-making in the dynamic application of the airborne laser directed energy system, thereby solving the problems existing in the above-mentioned prior art. Summary of the Invention

[0005] In order to solve the above technical problems of difficulty in determining priority processing targets, low decision-making efficiency and poor resource allocation efficiency, the present invention proposes a dynamic decision-making method and system based on VIKOR in an airborne directed energy system.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a first solution:

[0008] The VIKOR-based dynamic decision-making method in airborne directed energy systems includes:

[0009] Step S1: In an airborne directed energy system, a mathematical model for the transmission of laser directed energy in the atmosphere is established to obtain the power density of the airborne laser directed energy to the target;

[0010] Step S2: Based on the damage logic between the target components and the whole after the laser interacts with the target, a calculation model for the damage capability of airborne laser directed energy to typical targets is established, and the damage capability under the interaction of the laser and target vulnerability model is calculated and analyzed based on the power density of the laser directed energy to the target and the calculation model for the damage capability of the airborne laser directed energy to typical targets;

[0011] Step S3: During the aircraft movement, a physical model of the functional units in the airborne laser directed energy system is established;

[0012] Step S4: Modeling the aircraft state transition in a dynamic environment based on the physical model of the functional unit in the airborne laser directed energy system, and establishing an aircraft agent state transition model;

[0013] Step S5: Based on the calculation model of the damage capability of airborne laser directed energy to typical targets applied in step S2 and the aircraft agent state transition model in step S4, a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information is established to evaluate and quantify the target threat;

[0014] Step S6: On the basis of the information matrix, a target conditional probability calculation model based on the VIKOR method is established, and the compromise ranking value is calculated using the VIKOR method to obtain the dynamic conditional probability of the external target;

[0015] Step S7: Based on the target conditional probability calculation model in step S6, in the intuitive fuzzy decision environment, determine the maximum value of the target threat assessment value under the assessment attribute, construct the target loss function matrix under the assessment attribute through the assessment value, and then construct the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-way decision;

[0016] Step S8: combining the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain a classification ranking result of the multi-target threat assessment;

[0017] Step S9: Based on the classification and sorting results obtained in step S8, a rule-based target allocation method is used to allocate airborne laser directed energy resources to incoming targets that need to be processed first, thereby completing autonomous decision-making of airborne laser directed energy on targets in a dynamic environment.

[0018] In a preferred embodiment of the present invention, the mathematical model of the transmission of laser directional energy in the atmosphere includes an atmospheric absorption and scattering effect model, an atmospheric diffraction effect model, an atmospheric turbulence effect model, a beam jitter effect model, a thermal blooming effect model, an optical compensation effect model and a target-to-target power density effect model.

[0019] In a preferred embodiment of the present invention, the atmospheric absorption and scattering effect model is:

[0020]

[0021] Where, τ a is the atmospheric transmittance during laser oblique transmission; H a is the height of the laser directed energy system; H b is the height of the target; K is a constant that depends on the aerosol type; V M is the atmospheric visibility; θ is the zenith angle;

[0022] The atmospheric diffraction effect model is:

[0023]

[0024] Where θ0 is the diffraction angle of the light beam, L is the distance between the laser emitting mirror and the target, λ is the wavelength of the laser, D is the diameter of the laser emitting mirror, and β0 is the beam quality factor.

[0025] The atmospheric turbulence effect model is:

[0026]

[0027] Where, is the atmospheric refractive index structure constant, w is the average wind speed at a height of 5 to 20 km above the ground; z is the altitude; is the typical value of the near-surface refractive index structure constant;

[0028] The beam jitter effect model is:

[0029]

[0030] Where C n is the calculation parameter of the air refractive index; ω0 is the beam waist radius, φ is the angular deviation of the laser beam caused by atmospheric disturbance; S0 is a constant;

[0031] The heat blooming effect model is:

[0032]

[0033] Where N c is the thermal blooming distortion parameter, n T is the rate of change of atmospheric refractive index with temperature; μ a is the atmospheric absorption coefficient; P0 is the initial laser power; R is the transmission distance; n0 is the atmospheric refractive index; ρ is the air density; c p is the heat capacity of air; v is the wind speed;

[0034] The optical compensation effect model is:

[0035] a tAO =(1-β AO )a t ;

[0036] Where a tAO To compensate for the spot expansion radius caused by the turbulence; a t To compensate for the spot expansion radius caused by the front turbulence; β AO is the compensation coefficient of the adaptive optical system;

[0037] The target-to-target power density effect model is:

[0038]

[0039] Where, I is the equivalent spot power density; a d is the beam radius expansion caused by the diffraction effect; a j is the beam radius expansion caused by beam jitter; a t To compensate for the expansion of the spot radius caused by the front turbulence.

[0040] In a preferred embodiment of the present invention, the process of establishing a calculation model for the damage capability of airborne laser directed energy against typical targets includes:

[0041] Step S21: Modeling the intersection of laser and target;

[0042] Describe the damage logic between target components and the whole after the laser interacts with the target, and analyze the damage capability under the interaction of the laser and target vulnerability model;

[0043] Step S22: target vulnerability modeling and laser damage mode modeling of target components;

[0044] To damage an external target, laser directed energy must first interact with the target. The interaction model is used as input to constrain the target vulnerability model, thereby qualitatively describing the damage logic between the target components and the entire system after the laser interacts with the target.

[0045] Afterwards, a damage model of key components is established based on the damage logic to determine whether the components are damaged under laser irradiation;

[0046] Taking the laser-to-target parameters as input, the damage capability under the interaction of laser and target vulnerability model is quantitatively analyzed.

[0047] In a preferred embodiment of the present invention, the physical model of the functional unit in the airborne laser directed energy system includes a functional unit motion model, a radar detection model, a guidance and control model, and an optoelectronic sensor model.

[0048] In a preferred embodiment of the present invention, the functional unit motion model is:

[0049]

[0050] Where v is the velocity scalar of the aircraft entity; χ is the pitch angle of the aircraft entity; γ is the yaw angle of the aircraft entity; g is the acceleration of gravity;

[0051] The radar detection model is:

[0052]

[0053] Where, P D is the detection probability; N CA is the number of samples in the reference window; SNR is the signal-to-noise ratio; α CA is the constant false alarm processing constant;

[0054] The guidance control model is:

[0055]

[0056] Where: R r is the relative distance between the target and the aircraft; V r is the relative speed between the target and the aircraft; a is the command acceleration; N G is the proportional guidance coefficient; V m is the target's velocity vector; ω is the target's line of sight rotation angular velocity;

[0057] The photoelectric sensor model is:

[0058]

[0059] Where θ h and θ v 、Pix h and Pix v , GSD h and GSD v are the horizontal and vertical field of view angles, pixel pitch, and ground sampling distance of the sensor respectively; w tg and h tg are the width and height of the target respectively; r is the slant distance from the lens to the target; δ look N is the viewing angle under the lens; cyc is the number of scan line pairs that pass through the projection of the target on the sensor; GSD avg is the average ground sampling distance; d c is the target feature size; P d (N cyc ) is the target static detection probability; N cyc,50 The number of scan line pairs required for a 50% detection probability.

[0060] In a preferred embodiment of the present invention, step S5 establishes a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information, and the specific process of evaluating and quantifying the target threat includes:

[0061] Assume that at time t k At time t, the evaluation indicators of the distance l between the target and the aircraft, the target's flight altitude h, the target's flight speed v, and the target's expected arrival time e are as follows:

[0062]

[0063]

[0064] Where c1, c2, c3, c4, and b are all constants representing priority levels;

[0065] Assume that m targets are detected and each target has n evaluation attributes. The above indicators are converted into dynamic membership and non-membership of intuitionistic fuzzy numbers as follows:

[0066] (1) Benefit indicators:

[0067]

[0068] (2) Cost indicators:

[0069]

[0070] Where η∈[0,1] is the additional fuzzy factor, μ ij is the intuitionistic fuzzy number membership, v ij is the non-membership degree of the intuitionistic fuzzy number, a ij It is a threat value evaluation indicator.

[0071] In a preferred embodiment of the present invention, the specific process of constructing the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-way decision in step S7 includes:

[0072] Step S71: In the intuitive fuzzy decision environment, determine the maximum value of the target threat assessment value under the assessment attribute, and dynamically update the loss function parameters in combination with real-time environmental changes;

[0073] Step S72: Construct the loss function matrix of each target under each evaluation attribute through the evaluation value:

[0074]

[0075] Where, σ is the risk aversion coefficient; and are the maximum and minimum values ​​of the j-th evaluation attribute, respectively, and d is the distance calculation function;

[0076] Based on the above loss function matrix, the decision threshold corresponding to each external target is obtained as:

[0077]

[0078] Where w j is the attribute weight, λ PP ,λ BP and λ NP Respectively indicate that when the target is in state A, action a is taken P 、a B and a N The loss brought by the action set {a P ,a B ,a N} represent three actions: accepting an event, delaying decision, and rejecting an event; PN ,λ BN and λ NN They represent the losses caused by taking three actions when the target does not belong to state A, and the loss relationship satisfies: 0≤λ PP ≤λ BP ≤λ NP and 0≤λ NN ≤λ BN ≤λ PN .

[0079] In a preferred embodiment of the present invention, step S8 combines the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain the classification and ranking results of the multi-target threat assessment, and the specific process includes:

[0080] Step S81: combining the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain a classification ranking of the multi-objective priority evaluation;

[0081] Step S82: obtaining a classification result by setting the value of the decision mechanism coefficient k to reflect the preference between the overall attribute and the individual attributes of the compromise;

[0082] Among them, the classification results are three classification results, including targets that need to be prioritized, targets that do not need to be prioritized, and targets that require more information to determine whether they need to be prioritized.

[0083] The present invention provides a second solution:

[0084] The VIKOR-based dynamic decision-making system in the airborne directed energy system includes:

[0085] The mathematical model of laser directed energy transmission in the atmosphere is used to describe the attenuation of laser directed energy during its transmission in the atmosphere, so as to obtain the power density of airborne laser directed energy to the target;

[0086] A calculation model for the damage capability of airborne laser directed energy against typical targets is used to describe the damage logic between target components and the entire system after the interaction between laser directed energy and the target, and to analyze the damage capability under the interaction between laser and target vulnerability model;

[0087] The physical model of the functional units in the airborne laser directed energy system is used to simulate the use of airborne laser directed energy, including the functional unit motion model, radar detection model, guidance and control model, and optoelectronic sensor model;

[0088] The aircraft agent state transition model is used to describe the state transition of the aircraft in a dynamic environment and dynamically update the aircraft state based on the evaluation results;

[0089] The target conditional probability calculation model calculates the compromise ranking value based on the VIKOR method to obtain the dynamic conditional probability of external targets. Simultaneously, a loss function matrix for each target attribute is constructed using intuitionistic fuzzy information, and the dynamic comprehensive loss function and decision threshold for each target are calculated through matrix aggregation. Finally, by calculating the comprehensive threshold and formulating decision rules that meet the requirements of the airborne laser directed energy system, autonomous decision-making of airborne laser directed energy on targets in dynamic environments is achieved.

[0090] Among them: the mathematical model of laser directed energy transmission in the atmosphere, the calculation model of the destructive capability of airborne laser directed energy on typical targets, the physical model of the functional units in the airborne laser directed energy system, the aircraft agent state transition model and the target conditional probability calculation model are all implemented based on the VIKOR-based dynamic decision-making method in the airborne directed energy system.

[0091] The present invention has at least the following beneficial effects:

[0092] This method is based on the three-branch decision theory and is rooted in the rough set model. It provides a semantic interpretation for the positive, negative, and boundary domains in the rough set, demonstrates a strong ability in handling uncertainty, and can achieve effective classification and real-time optimization of resource allocation for airborne laser directed energy systems.

[0093] This method uses the VIKOR method under intuitive fuzzy information to calculate the compromise ranking value, which can determine the conditional probability of each external target evaluation in real time and eliminate the subjective interference of artificial priority determination in traditional two-branch sorting to the greatest extent.

[0094] This method utilizes intuitive fuzzy information to construct a loss function matrix for each target's attributes. Through matrix aggregation, it calculates a dynamic, comprehensive loss function and decision threshold for each target, significantly improving the rationality of laser directed energy resource allocation. Furthermore, by calculating comprehensive thresholds and formulating decision rules that meet the requirements of airborne laser directed energy systems, it ensures scientific target threat assessment and rational resource allocation. This method is well-suited to situations where decision preferences fluctuate, significantly improving the overall effectiveness of airborne laser directed energy systems and providing efficient, accurate, and intelligent decision support for dynamic applications. It possesses significant practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0096] Figure 1 Flowchart of the dynamic decision-making method based on VIKOR applied to an airborne laser directed energy system according to the present invention;

[0097] Figure 2 A flow chart for modeling a mathematical model for the transmission of airborne laser directed energy in the atmosphere according to the present invention;

[0098] Figure 3 This is a flow chart showing the calculation of the damage capability of airborne laser directed energy to typical targets according to the present invention;

[0099] Figure 4 A schematic diagram of the definition of 26 firing lines for calculating the damage capability of airborne laser directed energy to typical targets according to the present invention;

[0100] Figure 5 Schematic diagram of a typical application scenario of airborne laser directed energy of the present invention;

[0101] Figure 6 This is a physical model diagram of the airborne laser directed energy functional unit of the present invention;

[0102] Figure 7 This is a schematic diagram of the aircraft Agent state transition process of the present invention;

[0103] Figure 8 The figure is a calculation flow chart of the dynamic comprehensive evaluation information matrix based on intuitive fuzzy information of the present invention;

[0104] Figure 9 The flowchart of the calculation of the dynamic target conditional probability based on VIKOR of the present invention;

[0105] Figure 10 This is a flow chart for calculating the comprehensive threshold value of the three-branch decision-making of the present invention. DETAILED DESCRIPTION

[0106] The present invention will be further described below with reference to the accompanying drawings and examples.

[0107] Example 1:

[0108] See also Figures 1 to 10 This embodiment provides a VIKOR-based dynamic decision-making method for an airborne directed energy system. This method is used to optimize the resource allocation of the airborne laser directed energy system in real time by objectively classifying and ranking dynamic targets in the airborne directed energy system, thereby improving the overall efficiency of the airborne laser directed energy system. The method specifically includes the following steps:

[0109] Step S1: In an airborne directed energy system, a mathematical model for the transmission of laser directed energy in the atmosphere is established to obtain the power density of the airborne laser directed energy to the target;

[0110] Among them, the mathematical model of the laser directional energy transmission in the atmosphere includes an atmospheric absorption and scattering effect model, an atmospheric diffraction effect model, an atmospheric turbulence effect model, a beam jitter effect model, a thermal blooming effect model, an optical compensation effect model and a target-to-target power density effect model.

[0111] Step S2: Based on the damage logic between target components and the entire target after the laser interacts with the target, a calculation model for the damage capability of airborne laser directed energy to typical targets is established to describe the damage logic between target components and the entire target after the laser directed energy interacts with the target. At the same time, based on the airborne laser directed energy to target power density obtained in step S1 and the calculation model for the damage capability of airborne laser directed energy to typical targets, the damage capability under the interaction of the laser and target vulnerability model is calculated and analyzed;

[0112] Step S3: During the aircraft movement, a physical model of the functional units in the airborne laser directed energy system is established;

[0113] The physical model of the functional unit in the airborne laser directed energy system includes a functional unit motion model, a radar detection model, a guidance and control model, and an optoelectronic sensor model;

[0114] Step S4: Establish an aircraft agent state transition model: Based on the functional unit physical model established in step S3, the aircraft state transition is modeled in a dynamic environment to simulate the use of airborne laser directed energy. At the same time, dynamic threat assessment and resource allocation are combined based on real-time status to obtain the aircraft agent state transition model.

[0115] Step S5: Based on the calculation model of the damage capability of airborne laser directed energy to typical targets applied in step S2 and the aircraft agent state transition model in step S4, a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information is established to evaluate and quantify the target threat;

[0116] Among them, the factors affecting the target threat priority include the distance from the aircraft, flight altitude, flight speed and expected arrival time of the target;

[0117] Step S6: Based on the information matrix, a target conditional probability calculation model based on the VIKOR (VlseKriterijumskaOptimizacija IKompromisno Resenje) method is established, and the compromise ranking value is calculated using the VIKOR method to obtain the dynamic conditional probability of the external target;

[0118] Step S7: Based on the target conditional probability calculation model in step S6, in the intuitive fuzzy decision environment, determine the maximum value of the target threat assessment value under the assessment attribute, construct the target loss function matrix under the assessment attribute through the assessment value, and then construct the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-way decision;

[0119] Step S8: combining the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain an objective and accurate classification and ranking result of the multi-target threat assessment;

[0120] Step S9: Based on the classification and sorting results obtained in step S8, a rule-based target allocation method is used to allocate airborne laser directed energy resources to incoming targets that need to be processed first, thereby completing autonomous decision-making of airborne laser directed energy on targets in a dynamic environment.

[0121] For details, please refer to Figure 2 The specific process of establishing the mathematical model for the transmission of laser directed energy in the atmosphere described in step S1 of this embodiment includes:

[0122] After emanating from the transmitter, the laser passes through the atmosphere before reaching the target surface. During this process, the laser's directed energy is attenuated by various atmospheric effects, resulting in a sharp decrease in the laser power density reaching the target. Depending on how the laser interacts with various atmospheric molecules and aerosol particles, these effects can be categorized as linear, nonlinear, and compensation effects within the laser itself. Linear effects primarily include absorption and scattering, diffraction, turbulence, and jitter; nonlinear effects primarily include thermal blooming; and compensation effects refer to the use of compensators to increase the laser's power density to the target.

[0123] Step S11: Establishing an atmospheric absorption and scattering effect model;

[0124] The absorption and scattering effect of the atmosphere on lasers is related to the altitude. The lower the altitude, the higher the concentration of atmospheric molecules, the greater the absorption and scattering effect on the laser, and the more severe the attenuation of the laser beam when transmitting the same distance. For the case of oblique laser transmission, the atmospheric transmittance can be estimated using the empirical formula:

[0125]

[0126] Where, τ a is the atmospheric transmittance during laser oblique transmission; H a is the height of the laser directed energy system; H b is the height of the target; K is a constant that depends on the aerosol type; V M is the atmospheric visibility; θ is the zenith angle;

[0127] Step S12: establishing an atmospheric diffraction effect model;

[0128] During the laser transmission process, the diffraction effect of the laser beam causes the beam diameter to diverge. The diffraction effect is related to the laser wavelength and the aperture size of the laser emission mirror, and the laser beam diffraction angle a is obtained. d The calculation formula is:

[0129]

[0130] Where θ0 is the diffraction angle of the light beam, L is the distance between the laser emitting mirror and the target, λ is the wavelength of the laser, D is the diameter of the laser emitting mirror, and β0 is the beam quality factor.

[0131] Step S13: establishing an atmospheric turbulence effect model;

[0132] The laser beam expands, bends, and drifts under the influence of turbulence. The ITU-R atmospheric refractive index structure constant model is used to establish the atmospheric turbulence effect model:

[0133]

[0134] Where, is the atmospheric refractive index structure constant, w is the average wind speed at a height of 5 to 20 km above the ground, which is taken as 21 m / s; z is the altitude; is the typical value of the near-ground refractive index structure constant, which is 1.7×10 -14 m -2 / 3 .

[0135] Step S14: establishing a beam jitter effect model;

[0136] According to the theoretical basis of the atmospheric turbulence model, the angle deviation φ of the laser beam caused by atmospheric disturbance and the equivalent radius expansion a of the laser spot caused by beam jitter are jfor:

[0137]

[0138] Where C n is the air refractive index calculation parameter; ω0 is the beam waist radius; S0 = 0.35, unit is μm / m 3 / 2 ; L is the distance between the laser emitting mirror and the target.

[0139] Step S15: establishing a heat-blowing effect model;

[0140] For collimated beams or Gaussian beams with nonlinear effects, the thermal blooming distortion parameter N c for:

[0141]

[0142] Where N c is the thermal blooming distortion parameter, n T is the rate of change of atmospheric refractive index with temperature; μ a is the atmospheric absorption coefficient; P0 is the initial laser power; R is the transmission distance; n0 is the atmospheric refractive index; ρ is the air density; c p is the heat capacity of air; v is the wind speed; D is the diameter of the transmitting mirror.

[0143] Step S16: establishing an optical compensation effect model;

[0144] Adaptive optics compensation system is used, and the compensation capability of the adaptive optics system is measured by the ratio of the spot expansion radius before and after compensation:

[0145] a tAO =(1-β AO )a t ;

[0146] Where a tAO To compensate for the spot expansion radius caused by the turbulence; a t To compensate for the spot expansion radius caused by the front turbulence; β AO is the compensation coefficient of the adaptive optical system, 0<β AO <100%, when β AO =0 means the adaptive optics system is not enabled.

[0147] Step S17: establishing a target-to-target power density effect model;

[0148] Taking into account the atmospheric attenuation factors and models, the analytical calculation method is used to equate the atmospheric influence to the increase of the spot area and the attenuation of the laser energy, and the equivalent spot power density I is obtained as:

[0149]

[0150] Where I is the equivalent spot power density; τ a is the atmospheric transmittance during laser oblique transmission; P0 is the initial laser power; a d is the beam radius expansion caused by the diffraction effect; a j is the beam radius expansion caused by beam jitter; β AO is the compensation coefficient of the adaptive optical system; a t To compensate for the spot expansion radius caused by the front turbulence; N c is the thermal blooming distortion parameter.

[0151] For details, please refer to Figure 3 The process of establishing a calculation model for the damage capability of airborne laser directed energy against typical targets in step S2 of this embodiment includes:

[0152] Step S21: Modeling the intersection of laser and target;

[0153] Describe the damage logic between the target components and the whole after the laser interacts with the target, and analyze the damage capability under the interaction of the laser and target vulnerability model. Specifically: when the airborne laser directed energy acts on the target, the characteristics of the laser linear transmission show that the laser beam is a direct beam when it hits the target surface. Considering the influence of various random factors and target maneuvers, it can be assumed that the laser irradiation points obey uniform distribution on the intersection plane. The shooting line scanning method is used to consider 26 typical directions to calculate its damage capability, as shown in the attached figure. Figure 4 shown.

[0154] Step S22: target vulnerability modeling and laser damage mode modeling of target components;

[0155] To damage a foreign target, laser directed energy first requires interaction with the target. This interaction model serves as input to constrain the target vulnerability model. The target vulnerability model includes a geometric mesh model of the target and a damage tree for the target under laser irradiation. This model qualitatively describes the damage logic between the target components and the entire system after the laser-target interaction. Subsequently, damage models for key components are established based on this damage logic to determine whether the components are damaged by laser irradiation. Using laser-to-target parameters as input, the damage capability of the laser under the target vulnerability model is quantitatively analyzed.

[0156] For details, please refer to Figure 6 The specific process of establishing the physical model of the functional unit in the airborne laser directed energy system in step S3 of this embodiment includes:

[0157] Step S31: establishing a functional unit motion model for use in an airborne laser directed energy system;

[0158] The aircraft motion process is described by the three-degree-of-freedom center-of-mass kinematic equation in the track coordinate system, and the dynamic equation of overload in the track coordinate system is obtained as follows:

[0159]

[0160] Where v is the velocity scalar of the aircraft entity; χ is the pitch angle of the aircraft entity; γ is the yaw angle of the aircraft entity; During the simulation process, the overload (n x ,n y ,n z ) value to achieve different forms of movement such as level flight, acceleration, and turning; g is the acceleration due to gravity.

[0161] Step S32: establishing a radar detection model for use in an airborne laser directed energy system;

[0162] The radar detection model uses the unit average constant false alarm algorithm to calculate the radar detection probability P D :

[0163]

[0164] Where, P D is the detection probability; N CA is the number of samples in the reference window; SNR is the signal-to-noise ratio; α CA is the constant false alarm processing constant.

[0165] Step S33: establishing a guidance and control model for use in an airborne laser directed energy system;

[0166] In the guidance and control model, the target line of sight angular velocity ω and the relative velocity a between the target and the aircraft are calculated as follows:

[0167]

[0168] Where: R r is the relative distance between the target and the aircraft; V r is the relative speed between the target and the aircraft; a is the command acceleration; N G is the proportional guidance coefficient; V m is the velocity vector of the target; ω is the angular velocity of the target’s sight line.

[0169] Step S34: establishing a photoelectric sensor model for use in an airborne laser directed energy system;

[0170] Laser emission and aiming require guidance from a photoelectric detection module, which uses a CCD / CMOS camera to detect the target. The photoelectric sensor model uses the Johnson criterion to estimate the probability of target detection. The Johnson criterion divides the photoelectric sensor's target perception into three levels: detection, recognition, and identification, based on the size of the target image projected on the sensor. The specific form of the photoelectric sensor model is as follows:

[0171]

[0172] Where θ h and θ v 、Pix h and Pix v , GSD h and GSD v are the horizontal and vertical field of view angles, pixel pitch, and ground sampling distance of the sensor respectively; w tg and h tg are the width and height of the target respectively; r is the slant distance from the lens to the target; δ look N is the viewing angle under the lens; cyc is the number of scan line pairs that pass through the projection of the target on the sensor; when N cyc,50 When the values ​​are 0.75, 3.0 and 6.0 respectively, they correspond to the number of scan line pairs required for detection, recognition and identification with a probability of 50%, GSD avg is the average ground sampling distance; d c is the target feature size; P d (N cyc ) is the target static detection probability.

[0173] For details, please refer to Figure 7 The process of establishing the aircraft agent state transition model in step S4 of this embodiment includes:

[0174] Step S41: When penetrating a carrier-based aircraft that operates in coordination with a ship, an airborne laser directed energy integrated operation system is established. This system consists of aircraft, airborne laser directed energy systems, frigates, air defense systems, etc. Typical scenarios are shown in the attached figure. Figure 5 shown.

[0175] Step S42: After establishing Figure 5 The typical scenario shown is followed by Figure 7This is the state transition process of the Red Force aircraft in a dynamic environment. The Red Force aircraft's state transition begins in the standby state. After receiving the departure command, the aircraft enters the mission flight state, using onboard electro-optical sensors to actively monitor the airspace and search for potential targets. After detecting the target entering the range, the target is locked, and a dynamic three-way decision-making mechanism based on an intuitive fuzzy environment is used to evaluate and rank the threats of multiple targets. After completing the target assessment, the framework uses a rule-based target allocation method to assign the airborne laser directed energy system to the target that needs to be processed first. After each laser directed energy intercept, the system performs a target damage assessment to determine the interception effect and dynamically updates the aircraft status based on the assessment results. If the aircraft is destroyed, it is removed from the simulation system.

[0176] Specifically, the specific process of establishing a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information and evaluating and quantifying target threats in step S5 of this embodiment includes:

[0177] In the dynamic comprehensive evaluation information matrix, the priority of the targets quantifies their potential impact and effect on the aircraft. The factors affecting the target threat priority include the distance l between the target and the aircraft, the target's flight altitude h, the target's flight speed v and the target's expected arrival time e, as shown in the attached figure. Figure 8 As shown. At time t k When , the evaluation index formula of the distance l between the target and the aircraft, the target's flight altitude h, the target's flight speed v and the target's expected arrival time e is as follows:

[0178]

[0179] In the formula, c1, c2, c3, c4, b are all constants representing priority levels. Take c1 = 10 -3 ,c2=10 -8 ,c3=2×10 -6 ,c4=10 -7 ,b=-0.005. l is the distance between the target and the aircraft, h is the target's flight altitude, v is the target's flight speed, e is the target's expected arrival time, t k For the moment.

[0180] Assuming that m targets are detected and each target has n evaluation attributes, the above indicators are converted into dynamic membership and non-membership of intuitionistic fuzzy numbers as follows:

[0181] (1) Benefit-oriented indicators

[0182]

[0183] (2) Cost-based indicators

[0184]

[0185] Where η∈[0,1] is the additional fuzzy factor, μ ij is the intuitionistic fuzzy number membership, v ij is the non-membership degree of the intuitionistic fuzzy number, a ij It is a threat value evaluation indicator.

[0186] At a certain time t in a typical scenario, the aircraft detects four targets using a photoelectric sensor, denoted as T(t) = {T1, T2, T3, T4}. The four evaluation attributes of the target A = {Distance, Height, Speed, Time} can be obtained, denoted as A = {A1, A2, A3, A4}. The priority membership of the four targets at time t is shown in Table 1. Based on the priority membership table in Table 1, the multi-attribute intuitionistic fuzzy evaluation matrix Z = (z ij ) m×n =(μ ij ,v ij ) m×n As shown in Table 2. According to the normalization process, the evaluation indicators can be adjusted to benefit-type or cost-type indicators.

[0187] Table 1: Priority membership of the four goals at time t

[0188]

[0189]

[0190] Table 2: Multi-attribute intuitionistic fuzzy evaluation information

[0191]

[0192] For details, please refer to Figure 9 The process of establishing the target conditional probability calculation model based on the VIKOR method in step S6 of this embodiment includes:

[0193] On the basis of the dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information, the VIKOR method is used to calculate the compromise ranking value and estimate the dynamic conditional probability of the target. Specifically, the evaluation attribute set is considered as a whole, and the state set In the above equation, A(t k ) represents the target that needs to be processed first by laser directed energy, and its estimate corresponds to the positive ideal solution Indicates a target that does not need to be prioritized, and its estimated value corresponds to the negative ideal solution Z - (t k ). The smaller the compromise ranking value of the target, the greater the probability that the target belongs to A. Action set {a P ,a B ,aN} represent three actions: accepting an event, delaying decision, and rejecting an event.

[0194] The main calculation process is as follows:

[0195] (1) Determine the positive ideal solution Z + (t k ) and the negative ideal solution Z - (t k ):

[0196]

[0197] Among them, the benefit indicators are:

[0198]

[0199] Cost indicators are:

[0200]

[0201] Among them, the benefit indicators are:

[0202]

[0203] Cost indicators are:

[0204]

[0205] (2) Calculate the group utility value (S i ), individual regret value (R i ) and the compromise ranking value (Q i ):

[0206]

[0207] in,

[0208] (3) Calculate the target dynamic conditional probability:

[0209] Pr(A|T i (t k ))=1-Q i (t k ).

[0210] For details, please refer to Figure 10 The specific process of determining the maximum target threat assessment value under the assessment attribute in the intuitionistic fuzzy decision environment in step S7, constructing the target loss function matrix under the assessment attribute through the assessment value, and then constructing the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-branch decision includes:

[0211] Step S71: In the intuitionistic fuzzy decision environment, first determine the maximum value of the target threat assessment value under the assessment attribute, and dynamically update the loss function parameters in combination with real-time environmental changes;

[0212] Step S72: Construct the loss function matrix of each target under each evaluation attribute through the evaluation value:

[0213]

[0214] Where, σ is the risk aversion coefficient, which satisfies 0≤σ<0.5; and are the maximum and minimum values ​​of the jth evaluation attribute respectively; d is the distance calculation function.

[0215] Based on the above loss function matrix, the decision thresholds α and β corresponding to each external target are expressed as:

[0216]

[0217] Where w j is the attribute weight, λ PP ,λ BP and λ NP Respectively indicate that when the target is in state A, action a is taken P 、a B and a N The loss brought by the action set {a P ,a B ,a N} respectively represent the three actions of accepting an event, delaying decision and rejecting an event. PN ,λ BN and λ NN They represent the losses caused by taking three actions when the target does not belong to state A, and the loss relationship satisfies: 0≤λ PP ≤λ BP ≤λ NP and 0≤λ NN ≤λ BN ≤λ PN .

[0218] Specifically, the specific process of combining the comprehensive threshold obtained in step S7 with the three-branch decision rule in step S8 of this embodiment to obtain an objective and accurate classification and ranking result of the multi-target threat assessment includes:

[0219] Step S81: Combine the comprehensive threshold obtained in step S72 with the three-branch decision rule to obtain an objective and accurate classification ranking of the multi-objective priority evaluation.

[0220] Among them, the three decision rules are:

[0221] (P) If Pr(C|[x]) ≥ a, then x∈POS(C);

[0222] (B) If Pr(C|[x])≤α and Pr(C|[x])≥β, then x∈BND(C);

[0223] (N) If Pr(C|[x])≤β, then x∈NEG(C).

[0224] Step S82: The decision mechanism coefficient κ is set to 0.5 to reflect the preference between the overall attribute and individual attributes in the trade-off. The three classification results obtained are: targets that require priority, targets that do not require priority, and targets that require more information to determine whether they require priority. Table 3 shows the threat rankings obtained by the algorithm proposed in this embodiment under different decision mechanism coefficients. Given κ = 0.5 and σ = 0.45, the target decision thresholds obtained are shown in Table 4.

[0225] Table 3: Priority ranking of the algorithm proposed in this embodiment under different κ

[0226]

[0227]

[0228] Table 4: Target conditional probability and decision thresholds under κ = 0.5 and σ = 0.45

[0229]

[0230] According to the three decision rules, the target classification results are as follows:

[0231] POS(A)={T2,T4};

[0232] BND(A)={T1};

[0233] NEG(A)={T3}.

[0234] The results show that T2 and T4 are high-priority targets for the laser directed energy system, T1 is a low-priority target, and T3 requires additional information to determine its priority.

[0235] Specifically, in step S9 of this embodiment, based on the classification and sorting results obtained in step S8, a rule-based target allocation method is used to allocate airborne laser directed energy resources to incoming targets that require priority processing. The specific process of completing the autonomous decision-making of airborne laser directed energy on targets in a dynamic environment includes:

[0236] Based on the three classification results, it is necessary to determine the friendly aircraft to deal with the incoming target, using the distance-based advantage index method. Assuming that there are g incoming targets that need to be handled first acting on u aircraft, the advantage index P of the jth aircraft over the i-th target is calculated using the following formula: ij :

[0237]

[0238] Where: k1, k2 are threat level constants, k1 = 10, k2 = 1; R ij is the distance between the jth aircraft and the ith incoming target; R rj is the distance between the jth aircraft and the target point. Using the above formula, we obtain each aircraft's advantage index for each incoming target, forming a g × u-order advantage matrix P. Let the number of aircraft handling each incoming target be 2, and determine the aircraft that will handle the target. This ensures that the airborne laser direction-of-sight can quickly lock onto the target within its effective range, effectively determining the target for the airborne laser direction-of-sight in dynamic environments.

[0239] Example 2:

[0240] Unlike the first embodiment described above, this embodiment provides a VIKOR-based dynamic decision-making system for an airborne directed energy system. This system is implemented based on the VIKOR-based dynamic decision-making method for an airborne directed energy system described in the first embodiment. It includes a mathematical model for the transmission of laser directed energy in the atmosphere, a calculation model for the destructive capability of airborne laser directed energy against typical targets, a physical model for functional units in the airborne laser directed energy system, an aircraft agent state transition model, and a target conditional probability calculation model. Specifically:

[0241] The mathematical model of laser directed energy transmission in the atmosphere is used to describe the attenuation of laser directed energy energy during the transmission process of the laser directed energy in the atmosphere, so as to obtain the power density of the airborne laser directed energy to the target;

[0242] The calculation model of the damage capability of airborne laser directed energy to typical targets is used to describe the damage logic between target components and the entire target after the interaction between laser directed energy and the target, and to analyze the damage capability under the interaction between the laser and the target vulnerability model;

[0243] The physical model of the functional unit in the airborne laser directed energy system is used to simulate the use process of the airborne laser directed energy, including the functional unit motion model, radar detection model, guidance control model and optoelectronic sensor model;

[0244] The aircraft agent state transition model is used to describe the state transition of the aircraft in a dynamic environment and dynamically update the aircraft state according to the evaluation results;

[0245] The proposed target conditional probability calculation model calculates the compromise ranking value based on the VIKOR method to obtain the dynamic conditional probability of external targets. At the same time, the loss function matrix of each target attribute is constructed using intuitionistic fuzzy information, and the dynamic comprehensive loss function and decision threshold of each target are calculated through matrix aggregation. Finally, by calculating the comprehensive threshold and formulating decision rules that meet the requirements of the airborne laser directed energy system, the autonomous decision-making of the airborne laser directed energy on the target in a dynamic environment is completed.

[0246] Specifically, the mathematical model of the laser directional energy transmission in the atmosphere includes an atmospheric absorption and scattering effect model, an atmospheric diffraction effect model, an atmospheric turbulence effect model, a beam jitter effect model, a thermal blooming effect model, an optical compensation effect model, and a target-to-target power density effect model; wherein:

[0247] The atmospheric absorption and scattering effect model is used to describe the relationship between laser transmittance and the height of the laser directed energy system, the height of the target and the atmospheric transmittance during laser transmission, and calculate the atmospheric transmittance during laser transmission;

[0248] The atmospheric diffraction effect model is used to describe the relationship between the diffraction effect of the laser beam and the laser wavelength and the laser emission mirror, and to calculate the diffraction angle of the laser beam;

[0249] The atmospheric turbulence effect model is used to describe the expansion, bending and drift of the laser beam under the influence of turbulence effects, and calculate the atmospheric refractive index;

[0250] The beam jitter effect model is used to describe and calculate the angular offset of the laser beam caused by atmospheric disturbance and the expansion of the equivalent radius of the light spot caused by the beam jitter;

[0251] The thermal blooming effect model is used to calculate the thermal blooming distortion parameters of a collimated beam or a Gaussian beam with nonlinear effects;

[0252] The optical compensation effect model is used to measure the compensation capability of the adaptive optical system by the ratio of the spot expansion radius before and after compensation, and to calculate the spot expansion radius caused by turbulence after compensation;

[0253] The target-to-target power density effect model is used to calculate the equivalent spot power density.

[0254] Specifically, the physical model of the functional unit in the airborne laser directed energy system includes a functional unit motion model, a radar detection model, a guidance and control model, and an optoelectronic sensor model; wherein:

[0255] The functional unit motion model is used to describe the aircraft motion process and calculate the overload in the track coordinate system;

[0256] The radar detection model is used to calculate the detection probability of the radar;

[0257] The guidance control model is used to calculate the target line of sight rotation angular velocity and the relative speed between the target and the aircraft;

[0258] The photoelectric sensor model is used to estimate the target detection probability using the Johnson criterion.

[0259] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic decision-making method based on VIKOR in an airborne directed energy system, characterized by: include: Step S1: In an airborne directed energy system, a mathematical model for the transmission of laser directed energy in the atmosphere is established to obtain the power density of the airborne laser directed energy to the target; Step S2: Based on the damage logic between the target components and the whole after the laser interacts with the target, a calculation model for the damage capability of airborne laser directed energy to typical targets is established, and the damage capability under the interaction of the laser and target vulnerability model is calculated and analyzed based on the power density of the laser directed energy to the target and the calculation model for the damage capability of the airborne laser directed energy to typical targets; Step S3: During the aircraft movement, a physical model of the functional units in the airborne laser directed energy system is established; Step S4: Modeling the aircraft state transition in a dynamic environment based on the physical model of the functional unit in the airborne laser directed energy system, and establishing an aircraft agent state transition model; Step S5: Based on the calculation model of the damage capability of airborne laser directed energy to typical targets applied in step S2 and the aircraft agent state transition model in step S4, a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information is established to evaluate and quantify the target threat; Step S6: On the basis of the information matrix, a target conditional probability calculation model based on the VIKOR method is established, and the compromise ranking value is calculated using the VIKOR method to obtain the dynamic conditional probability of the external target; Step S7: Based on the target conditional probability calculation model in step S6, in the intuitive fuzzy decision environment, determine the maximum value of the target threat assessment value under the assessment attribute, construct the target loss function matrix under the assessment attribute through the assessment value, and then construct the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-way decision; Step S8: combining the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain a classification ranking result of the multi-target threat assessment; Step S9: Based on the classification and sorting results obtained in step S8, a rule-based target allocation method is used to allocate airborne laser directed energy resources to incoming targets that need to be processed first, thereby completing autonomous decision-making of airborne laser directed energy on targets in a dynamic environment.

2. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 1, wherein: The mathematical models of laser directed energy transmission in the atmosphere include atmospheric absorption and scattering effect model, atmospheric diffraction effect model, atmospheric turbulence effect model, beam jitter effect model, thermal blooming effect model, optical compensation effect model and target-to-target power density effect model.

3. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 2, wherein: The atmospheric absorption and scattering effect model is: Where, τ a is the atmospheric transmittance during laser oblique transmission; H a is the height of the laser directed energy system; H b is the height of the target; K is a constant that depends on the aerosol type; V M is the atmospheric visibility; θ is the zenith angle; The atmospheric diffraction effect model is: Where θ0 is the diffraction angle of the light beam, L is the distance between the laser emitting mirror and the target, λ is the wavelength of the laser, D is the diameter of the laser emitting mirror, and β0 is the beam quality factor. The atmospheric turbulence effect model is: Where, is the atmospheric refractive index structure constant, w is the average wind speed at a height of 5 to 20 km above the ground; z is the altitude; is the typical value of the near-surface refractive index structure constant; The beam jitter effect model is: Where C n is the calculation parameter of the air refractive index; ω0 is the beam waist radius, φ is the angular deviation of the laser beam caused by atmospheric disturbance; S0 is a constant; The heat blooming effect model is: Where N c is the thermal blooming distortion parameter, n T is the rate of change of atmospheric refractive index with temperature; μ a is the atmospheric absorption coefficient; P0 is the initial laser power; R is the transmission distance; n0 is the atmospheric refractive index; ρ is the air density; c p is the heat capacity of air; v is the wind speed; The optical compensation effect model is: a tAO =(1-β AO )a t ; Where a tAO To compensate for the spot expansion radius caused by the turbulence; a t To compensate for the spot expansion radius caused by the front turbulence; β AO is the compensation coefficient of the adaptive optical system; The target-to-target power density effect model is: Where, I is the equivalent spot power density; a d is the beam radius expansion caused by the diffraction effect; a j is the beam radius expansion caused by beam jitter; a t To compensate for the expansion of the spot radius caused by the front turbulence.

4. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 1, wherein: The process of establishing a calculation model for the damage capability of airborne laser directed energy against typical targets includes: Step S21: Modeling the intersection of laser and target; Describe the damage logic between target components and the whole after the laser interacts with the target, and analyze the damage capability under the interaction of the laser and target vulnerability model; Step S22: target vulnerability modeling and laser damage mode modeling of target components; To damage an external target, laser directed energy must first interact with the target. The interaction model is used as input to constrain the target vulnerability model, thereby qualitatively describing the damage logic between the target components and the entire system after the laser interacts with the target. Afterwards, a damage model of key components is established based on the damage logic to determine whether the components are damaged under laser irradiation; Taking the laser-to-target parameters as input, the damage capability under the interaction of laser and target vulnerability model is quantitatively analyzed.

5. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 3, wherein: The physical model of the functional unit in the airborne laser directed energy system includes the functional unit motion model, radar detection model, guidance and control model and optoelectronic sensor model.

6. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 5, characterized in that: The functional unit motion model is: Where v is the velocity scalar of the aircraft entity; χ is the pitch angle of the aircraft entity; γ is the yaw angle of the aircraft entity; g is the acceleration of gravity; The radar detection model is: Where, P D is the detection probability; N CA is the number of samples in the reference window; SNR is the signal-to-noise ratio; α CA is the constant false alarm processing constant; The guidance control model is: Where: R r is the relative distance between the target and the aircraft; V r is the relative speed between the target and the aircraft; a is the command acceleration; N G is the proportional guidance coefficient; V m is the target's velocity vector; ω is the target's line of sight rotation angular velocity; The photoelectric sensor model is: Where θ h and θ v 、Pix h and Pix v , GSD h and GSD v are the horizontal and vertical field of view angles, pixel pitch, and ground sampling distance of the sensor respectively; w tg and h tg are the target width and height respectively; r is the slant distance from the lens to the target; δ look N is the viewing angle under the lens; cyc is the number of scan line pairs that pass through the projection of the target on the sensor; GSD avg is the average ground sampling distance; d c is the target feature size; P d (N cyc ) is the target static detection probability; N cyc,50 The number of scan line pairs required for a 50% detection probability.

7. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 6, characterized in that: Step S5 establishes a dynamic comprehensive evaluation information matrix based on intuitionistic fuzzy information. The specific process of evaluating and quantifying target threats includes: Assume that at time t k At time t, the evaluation indicators of the distance l between the target and the aircraft, the target's flight altitude h, the target's flight speed v, and the target's expected arrival time e are as follows: Where c1, c2, c3, c4, and b are all constants representing priority levels; Assume that m targets are detected and each target has n evaluation attributes. The above indicators are converted into dynamic membership and non-membership of intuitionistic fuzzy numbers as follows: (1) Benefit indicators: (2) Cost indicators: Where η∈[0,1] is the additional fuzzy factor, μ ij is the intuitionistic fuzzy number membership, v ij is the non-membership degree of the intuitionistic fuzzy number, a ij It is a threat value evaluation indicator.

8. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 7, wherein: The specific process of step S7 constructing the comprehensive loss matrix of the target under the attribute set and the comprehensive threshold of the target three-branch decision includes: Step S71: In the intuitive fuzzy decision environment, determine the maximum value of the target threat assessment value under the assessment attribute, and dynamically update the loss function parameters in combination with real-time environmental changes; Step S72: Construct the loss function matrix of each target under each evaluation attribute through the evaluation value: Where, σ is the risk aversion coefficient; and are the maximum and minimum values ​​of the j-th evaluation attribute, respectively, and d is the distance calculation function; Based on the above loss function matrix, the decision threshold corresponding to each external target is obtained as: Where w j is the attribute weight, λ PP ,λ BP and λ NP Respectively indicate that when the target is in state A, action a is taken P 、a B and a N The loss brought by the action set {a P ,a B ,a N } represent three actions: accepting an event, delaying decision, and rejecting an event; PN ,λ BN and λ NN They represent the losses caused by taking three actions when the target does not belong to state A, and the loss relationship satisfies: 0≤λ PP ≤λ BP ≤λ NP and 0≤λ NN ≤λ BN ≤λ PN .

9. The VIKOR-based dynamic decision-making method in an airborne directed energy system according to claim 1, wherein: Step S8 combines the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain the classification and ranking results of the multi-target threat assessment. The specific process includes: Step S81: combining the comprehensive threshold obtained in step S7 with the three-branch decision rule to obtain a classification ranking of the multi-objective priority evaluation; Step S82: obtaining a classification result by setting the value of the decision mechanism coefficient k to reflect the preference between the overall attribute and the individual attributes of the compromise; Among them, the classification results are three classification results, including targets that need to be prioritized, targets that do not need to be prioritized, and targets that require more information to determine whether they need to be prioritized.

10. A VIKOR-based dynamic decision-making system for airborne directed energy systems, characterized by: include: The mathematical model of laser directed energy transmission in the atmosphere is used to describe the attenuation of laser directed energy during its transmission in the atmosphere, so as to obtain the power density of airborne laser directed energy to the target; A calculation model for the damage capability of airborne laser directed energy against typical targets is used to describe the damage logic between target components and the entire system after the interaction between laser directed energy and the target, and to analyze the damage capability under the interaction between laser and target vulnerability model; The physical model of the functional units in the airborne laser directed energy system is used to simulate the use of airborne laser directed energy, including the functional unit motion model, radar detection model, guidance and control model, and optoelectronic sensor model; The aircraft agent state transition model is used to describe the state transition of the aircraft in a dynamic environment and dynamically update the aircraft state based on the evaluation results; The target conditional probability calculation model calculates the compromise ranking value based on the VIKOR method to obtain the dynamic conditional probability of external targets. Simultaneously, a loss function matrix for each target attribute is constructed using intuitionistic fuzzy information, and the dynamic comprehensive loss function and decision threshold for each target are calculated through matrix aggregation. Finally, by calculating the comprehensive threshold and formulating decision rules that meet the requirements of the airborne laser directed energy system, autonomous decision-making of airborne laser directed energy on targets in dynamic environments is achieved. Among them, the mathematical model of laser directed energy transmission in the atmosphere, the calculation model of the damage capability of airborne laser directed energy to typical targets, the physical model of the functional units in the airborne laser directed energy system, the aircraft agent state transition model and the target conditional probability calculation model are all implemented based on the method according to any one of claims 1 to 9.

Images