A closed-loop adaptive defense decision support method

Abstract

The present application belongs to the field of modern energy action device and automatic defense technology, and discloses a closed-loop adaptive defense decision support method, comprising: obtaining target dynamic parameters, using analytic hierarchy process and membership function to evaluate threats, combining expert scoring and K-means clustering to complete target classification, constructing a directional energy device damage effectiveness function, screening potential attack strategies with high hit and low energy consumption, using meta-heuristic algorithm for device deployment, energy distribution, power selection and target allocation decision optimization, evaluating structural and functional damage through high-definition high-speed camera and damage sensor data, and feeding damage and environmental data back to the evaluation and effectiveness model to realize closed-loop adaptive optimization. Compared with the prior art, the present application can improve the attack efficiency and energy utilization rate, reduce misjudgment and resource waste, enhance the adaptability and robustness to complex environment and interference conditions, and enhance the defense capability of the directional energy device defense system in complex operation environment.

Description

Technical Field

[0001] This invention relates to the fields of target defense, directional energy device applications, and intelligent decision-making technology, and particularly to a closed-loop adaptive defense decision support method. Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, the number of targets in various operational scenarios has increased significantly. The characteristics of swarm flight, collaborative operations, and low-cost group penetration make target groups a complex and flexible new threat. When targets are performing reconnaissance, communication relay, or carrying payloads to carry out sabotage, their small size, low altitude and low speed, ability to disperse in formation, and high maneuverability pose significant challenges to existing monitoring, identification, and interception systems. Especially in dense or multi-target environments, how to quickly and accurately assess the threat and target value has become a critical issue.

[0003] Directed energy systems are considered a powerful tool against target groups due to their advantages such as rapid strike capability, high accuracy, reusability, and low cost per unit of damage. Compared to traditional kinetic or explosive interceptors, directed energy systems do not require a physical medium, have a fast response time, and can achieve precise directed energy release. However, their operational effectiveness is limited by factors such as optical transmission attenuation, atmospheric disturbance, energy supply, and system stability. In practical applications, technical indicators such as directed energy power management, aiming accuracy, environmental adaptability, and anti-jamming capabilities directly affect the final damage effect and adaptability to various scenarios.

[0004] Current research on decision support for directed energy devices (TEEs) used against target swarms mainly focuses on the effectiveness of TEEs in striking targets, the global deployment of TEEs, the control of individual devices, and several modularizations. Most of these studies involve offline simulations or static decision-making, and lack real-time closed-loop feedback mechanisms between devices and modules, making it difficult to balance dynamic environments, sensor feedback, and online optimization and adjustment of operational effects.

[0005] Therefore, existing research and systems have several shortcomings when facing complex and dynamic target groups: first, threat identification and value assessment are difficult to link with actual combat damage feedback; second, directed energy allocation, power selection, and target allocation cannot be globally optimally scheduled under multiple constraints; and third, environmental factors and sensor observation errors do not adequately correct for performance evaluation. Based on this, this application proposes an integrated, closed-loop adaptive decision support method to achieve unified optimization and iterative improvement based on real-time monitoring, performance evaluation, and post-operation feedback. This method not only has strong practicality but also provides a new direction for the future development of target swarm defense technology. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention proposes a closed-loop adaptive defense decision support method. First, based on real-time monitoring and acquisition of target dynamic parameters, threat assessment is performed using the analytic hierarchy process (AHP) and membership functions, combined with expert scoring and K-means clustering to classify and categorize target value. Second, a directed energy damage effectiveness function is constructed, and a set of potential high-damage, low-energy-consumption damage strategies is screened using Monte Carlo simulation. Subsequently, simulated annealing is employed to optimize device deployment, prioritize energy allocation, and use a genetic algorithm for target allocation to maximize overall strike effectiveness. Finally, high-definition high-speed cameras and damage sensors are used to assess structural and functional damage, and damage and environmental data are fed back. This aims to improve strike efficiency and energy utilization, reduce misjudgments and resource waste, and enhance environmental adaptability and robustness.

[0007] The technical solution of this invention is implemented as follows: A closed-loop adaptive defense decision support method, characterized by comprising the following steps: Step 1: In the monitoring and evaluation system, the target cluster is assessed for threat and value based on information from the real-time monitoring system, and the target is classified accordingly. Step 2: In the damage effectiveness assessment system for directed energy devices, the threat assessment and value assessment classification results of the target cluster in Step 1 are combined with the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the directed energy device. The destructive effects of different strategies are evaluated through Monte Carlo simulation to form a set of potential destructive strategies. Step 3: In the resource management and device control system, based on the potential damage strategy set in Step 2, optimize device deployment, energy allocation and power selection, and target allocation decisions. Step 4: In the target damage effect assessment system, the operation is carried out according to the various strategies formulated in Step 3. Target data is collected in real time through high-definition high-speed cameras and damage detection sensors to calculate the degree of structural damage, functional damage, and the impact of environmental factors on the target. Step 5: Based on the target damage effect obtained after the operation in Step 4, the structural damage level and functional damage level of the target are fed back to the monitoring and evaluation system to optimize the threat assessment and value classification of the target cluster. The impact of environmental factors is fed back to the directional energy damage effectiveness evaluation system to optimize the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the directional energy device.

[0008] Preferably, step 1 specifically includes: The real-time monitoring system collects dynamic parameters of the target cluster, including at least the number of targets, model, flight altitude, flight speed, flight trajectory, payload type, signal characteristics, and signal strength. A threat assessment model is constructed based on the analytic hierarchy process, and dynamic parameters are used as assessment indicators and assigned weights. Establish membership functions for each indicator, generate a membership matrix to calculate threat assessment values, and classify the threat levels of each target into high, medium, and low levels based on thresholds; The value of the target cluster is evaluated by scoring, including engineering value, perceived value, and overall value; a value evaluation model is constructed to calculate the value score. Combining threat level and value score, the target cluster is divided into core targets, important targets and general targets using the K-means clustering algorithm.

[0009] Preferably, step 2 specifically includes: Using the target classification results obtained in step 1 as input, a directional energy damage effectiveness evaluation function is established. The damage effectiveness under different proposed strategies is iteratively calculated using the Monte Carlo simulation method. Strategies with greater damage effectiveness and lower energy consumption are selected to form a potential damage strategy set.

[0010] Preferably, based on the optimal destruction effect in step 2, the device deployment, energy allocation and power selection, and target allocation are carried out according to the proposed strategy. The specific method is as follows: The deployment of the device adopts the simulated annealing algorithm, expanding outward from the protected key area to obtain the maximum deployment range of the device, and establishing the strike effectiveness equation as the objective function for device deployment; Energy allocation adopts a priority allocation strategy based on the target classification results; The target allocation adopts a genetic algorithm. Based on the potential destruction strategy set, device deployment and energy allocation in step 2, an initial solution is formed. The strike effectiveness function is established as the target function and iterated to maximize the total destruction effect.

[0011] Preferably, the operation is carried out according to the strategies formulated in step 3 to obtain the degree of damage, damage range, and influence of environmental factors on targets of different types and materials. The specific method is as follows: During the operation, target data is collected in real time using high-definition high-speed cameras and damage detection sensors; The degree of structural damage is classified as mild, moderate, or severe based on the proportion of the damaged area of ​​the target aircraft. Functional damage is determined by detecting the interruption rate of the target's communication link, navigation and positioning errors, and flight attitude stability. Environmental factors affect the recording of visibility, humidity, and wind speed parameters.

[0012] Preferably, the specific method of step 5 is as follows: the structural damage level and functional damage level data obtained in step 4 are fed back to the monitoring and evaluation system to optimize the threat assessment and value classification of the target cluster; Environmental factors impact data are fed back to the directed energy damage performance assessment system to optimize the energy consumption, accuracy, and stability of directed energy devices, update the calculation model of environmental correction factors, and improve the environmental adaptability and anti-interference capability of directed energy devices during operation.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a closed-loop adaptive defense decision support method. By organically integrating real-time monitoring, threat and value assessment, effectiveness simulation, and deployment strike process, it significantly improves the timeliness and accuracy of operational decisions. An evaluation method based on hierarchical analysis and fuzzy membership, combined with K-means clustering, enables more refined target classification; Monte Carlo simulation screening and weighted effectiveness functions reduce energy consumption while ensuring high damage output, thereby improving the energy utilization rate and single-operation efficiency of directed energy strikes.

[0014] This invention constructs a closed-loop operational feedback mechanism that can feed back structural and functional damage data, as well as environmental factors, collected during actual combat to the monitoring and evaluation and performance assessment modules in real time, enabling adaptive correction and iterative optimization under different environmental and interference conditions. This closed-loop mechanism enhances the system's robustness to sensor errors, atmospheric disturbances, and electromagnetic interference, reducing the false positive rate and improving the reliability and stability of decision-making.

[0015] This invention provides a basis for operation and maintenance personnel to make operational decisions, and the optimized decision results can also serve as an important reference for operation and maintenance personnel to make decisions on the anti-target cluster operation of directional energy devices when directing operations. Attached Figure Description

[0016] Figure 1 This is a flowchart of the closed-loop adaptive defense decision support method of the present invention; Figure 2 This is a schematic diagram of the monitoring and evaluation system of the present invention; Figure 3 This is a schematic diagram of the directional energy damage performance evaluation system of the present invention; Figure 4 This is a schematic diagram of the resource management and device control system of the present invention; Figure 5 This is a schematic diagram of the target damage effect evaluation system of the present invention. Detailed Implementation

[0017] The present invention will now be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0018] The present invention provides a closed-loop adaptive defense decision support method, such as... Figure 1 As shown, it includes the following 5 steps: Step 11: In the monitoring and evaluation system, based on the information from the real-time monitoring system, a weight parameter vector and a membership function matrix are established, and the target cluster is assessed for threat and classified for value using K-means. Step 12: In the targeted energy device damage effectiveness assessment system, combined with the threat assessment and value classification results of the target cluster in Step 11, the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the targeted energy device are considered. The damage probability and effect obtained by different energy allocation and attack strategies are evaluated through Monte Carlo simulation, and the potential damage strategy set is calculated. Step 13: In the resource management and device control system, based on the optimal damage probability and effect in Step 12, and on the basis of the potential damage strategy set, the device deployment problem is solved by the simulation degradation algorithm, and energy allocation and power selection are carried out according to the priority of the targets, and the strike effectiveness function is established for target allocation. Step 14: In the target damage effect assessment system, the operation is carried out according to the various strategies formulated in Step 13. Target data is collected in real time through high-definition high-speed cameras and damage detection sensors, and the degree of structural damage, functional damage, and the impact of environmental factors on the target are calculated. Step 15: Based on the target damage effect obtained after the operation in Step 14, the structural damage degree and functional damage degree of the target are fed back to the monitoring and evaluation system to optimize the threat assessment and value classification of the target cluster. The impact of environmental factors is fed back to the directional energy damage effectiveness assessment system to optimize the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the directional energy device.

[0019] like Figure 2 As shown, the specific implementation methods for monitoring and evaluation are as follows: Step 21: The real-time monitoring system collects dynamic parameters of the target cluster. These dynamic parameters include at least the number of targets N, model M, flight altitude H, flight speed V, and flight trajectory coordinates. The dynamic parameters are cleaned by analyzing the payload type L (functional carrier / sensing device / signal transmission component, etc.), signal characteristics F (frequency hopping pattern / modulation method), and signal strength S. Step 22: Construct a judgment matrix using expert scoring and build a three-level assessment index system based on the analytic hierarchy process (AHP), with the target level being the threat level. The criterion layer is the operating condition characteristics. Motion characteristics , signal characteristics The indicator layer consists of target dynamic parameters, and the dynamic parameter weight vector is calculated. The weight of payload type is greater than or equal to 0.25, and the weight of flight trajectory matching degree with key area is greater than or equal to 0.15. Step 23: Use the fuzzy comprehensive evaluation method to establish the membership functions of each dynamic parameter. Generate the membership matrix ; Step 24: Calculate the threat assessment value based on steps 22 and 23: , Based on threshold Threat levels are classified into three levels: high, medium, and low. Step 25: Evaluate the value of the target cluster by scoring, including engineering value, perceived value, and overall value. Step 26, Construct a value assessment model: , Among them, the value of the project Perceived value Overall Value According to the threshold The value is divided into three levels: high, medium, and low. Step 27: Combining threat level and value level, the K-means clustering algorithm is used, with cluster centers set as (high threat, high value), (medium threat, medium value), and (low threat, low value), to divide the target cluster into core targets, important targets, and general targets.

[0020] like Figure 3 As shown, the specific implementation method for directional energy damage effectiveness assessment is as follows: Step 31: Set the various parameters for the simulation, including: directional energy device parameters, target cluster target parameters, environmental parameters, etc. Step 32: Define multiple sets of candidate directed energy strategies, that is, target allocation strategies for different devices under different energy conditions; Step 33: Based on the core objectives, important objectives, and general objectives output in Step 27, set the objective classification coefficient T: , Step 34, calculate the various model parameters: (1) Energy formula per unit area: , in, The energy conversion efficiency of the directional energy device. For target irradiated area , For directional energy irradiation time, Directional energy output power (kW). Atmospheric transmission attenuation coefficient, Atmospheric extinction coefficient (km, sunny day) Smoggy days ), Working distance (km); (2) Formulas for the aiming and tracking performance, accuracy, and stability parameters of the quantification directional energy device: , in, This represents the actual aiming error (mrad). The actual tracking error (mrad) has a range of values. The closer the value is to 1, the better the accuracy and stability. (3) Considering the combined effects of environment and anti-interference, the calculation formulas for environmental adaptability and anti-interference capability parameters are as follows: , in, Visibility correction factor (when visibility is 10km) 6km 4km ), Wind speed correction factor (when wind speed ≤ 5m / s) At 10 m / s ), Electromagnetic interference correction factor (when there is no interference) Strong interference ); Directional energy damage effectiveness assessment function: , in: The weights are 0.4, 0.2, 0.2, and 0.2, respectively. The target classification coefficient; Energy consumption; This represents the device's maximum single-cycle energy output. For accuracy and stability parameters; Environmental correction factor; To facilitate the expectation calculation in Monte Carlo, environmental disturbances are modeled in each experiment to obtain effective accuracy. and actual energy The hit probability is determined by... The sigmoid function determines the probability of destruction under the hit condition. The sigmoid function relative to the target energy threshold determines the probability of success in a single trial. The probability of success in a single trial equals the probability of hit multiplied by the probability of destruction; the average of this probability over a large number of Monte Carlo trials yields an estimate of P. Step 35: Sort all iteration results in descending order of P value, select strategies with P≥85% and energy consumption E in the lower 30% range to form a potential destructive strategy set.

[0021] like Figure 4 As shown, the specific implementation methods for resource management and device control are as follows: Step 41: Expand the radius R outward from the protected key location to obtain the maximum deployment range of the device, and establish the strike effectiveness equation. The total strike effectiveness of the directed energy device along a certain attack path of the target can be obtained according to the following formula: , It is possible The column vector of damage sustained by each flight path: , Establish weighted arrays for each direction of attack:

[0022] in: , , For a certain goal The possibility of attacking along the flight path, the importance of protecting key locations from different directions, or the defensive focus from each direction; pass This represents the weighted strike effectiveness or effective defense of the deployment strategy against targets along each path. To ensure that the deployment strategy provides some effective defense against any direction of attack, the weighted strike effectiveness in the direction with the lowest value is taken as the evaluation of the strategy, i.e.: , Make maximize: , The device deployment employs a simulated annealing algorithm, in order to Given the objective function, a polar coordinate solution vector is deployed by constructing a device. Solve the problem; Step 42: Based on the target classification in step 27, consider all targets within the strike range of the directed energy device, and allocate energy according to the priority allocation strategy based on the target classification results; Step 43: Find the intersection of the potential destruction strategy set in step 35, the device deployment result in step 41, and the energy allocation strategy in step 3b to form an initial solution; Step 44: Establish a strike effectiveness function as the objective function based on the target damage: , in, , These refer to the directional energy device and the number of targets. As a decision factor, when the first The directed energy device struck the first When there is a target, let ,otherwise , For the first The directed energy device struck the first The damage probability of a target reference can be calculated based on the effective strike time of the directed energy device on the target or the intersection of the ranges of the two. Step 45: Use a genetic algorithm to iterate and output the optimization result to maximize the overall destructive effect.

[0023] like Figure 5 As shown, the specific implementation method for target damage effect assessment is as follows: Step 51: Real-time acquisition of target images and target parameter data using a high-definition high-speed camera and a damage detection sensor, respectively; Step 52: Calculate the degree of structural damage and the degree of functional damage based on Step 51. Structural damage... Based on the percentage of the target body's damaged area: , in, For the target illuminated area, For the total surface area of ​​the machine body, set For mild structural damage, For moderate structural damage, It constitutes severe structural damage; Step 53: Based on step 51, calculate functional impairment by detecting the target communication link interruption rate, navigation and positioning error, and flight attitude stability. : , in, For communication interruption rate, This is the normalized value of the navigation error. To set the attitude stability deviation, For mild functional impairment, For moderate functional impairment, It is a severe functional impairment; Step 54: Record the impact of environmental factors, including visibility, humidity, and wind speed. Using visibility, humidity, and wind speed under clear weather conditions as a baseline, calculate the correction factors for visibility, humidity, and wind speed respectively. , , , in, , , These are the baseline values ​​for visibility, humidity, and wind speed under clear weather conditions. These are the actual values ​​of visibility, humidity, and wind speed under the working weather conditions; Environmental impact coefficient obtained: , pass Correcting structural damage Functional impairment .

[0024] Step 55: Based on the structural and functional damage data from steps 52 and 53, optimize the threat assessment and value classification of the target cluster in the monitoring and evaluation system, and classify the structural damage levels accordingly. and degree of functional impairment According to the severity levels (mild, moderate, severe) and the target's threat assessment value. and valuation The revised threat assessment value of the target was calculated. and valuation : , , Based on the environmental impact data from step 54, optimize the directed energy damage effectiveness assessment system, optimize the energy consumption, accuracy, and stability of the directed energy device, and eliminate the impact deviation of the device caused by environmental factors: Corrected beam energy per unit area reached : , Corrected accuracy and stability parameters for: , The calculation model for the environmental correction factor J was updated to improve the environmental adaptability and anti-interference capability parameters of the directional energy device during operation. , in, Visibility correction factor (when visibility is 10km) 6km 4km ), Wind speed correction factor (when wind speed ≤ 5m / s) At 10 m / s ), Electromagnetic interference correction factor (when there is no interference) Strong interference ).

[0025] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the description above. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, using the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A closed-loop adaptive defense decision support method, characterized in that, Includes the following steps: Step 1: In the monitoring and evaluation system, the target cluster is assessed for threat and value based on information from the real-time monitoring system, and the target is classified accordingly. Step 2: In the damage effectiveness assessment system for directed energy devices, the threat assessment and value assessment classification results of the target cluster in Step 1 are combined with the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the directed energy device. The destructive effects of different strategies are evaluated through Monte Carlo simulation to form a set of potential destructive strategies. Step 3: In the resource management and device control system, based on the potential damage strategy set in Step 2, optimize device deployment, energy allocation and power selection, and target allocation decisions. Step 4: In the target damage effect assessment system, the operation is carried out according to the various strategies formulated in Step 3. Target data is collected in real time through high-definition high-speed cameras and damage detection sensors to calculate the degree of structural damage, functional damage, and the impact of environmental factors on the target. Step 5: Based on the target damage effect obtained after the operation in Step 4, the structural damage level and functional damage level of the target are fed back to the monitoring and evaluation system to optimize the threat assessment and value classification of the target cluster. The impact of environmental factors is fed back to the directional energy damage effectiveness evaluation system to optimize the energy consumption, accuracy and stability, environmental adaptability and anti-interference capability of the directional energy device.

2. The closed-loop adaptive defense decision support method according to claim 1, characterized in that, Step 1 is as follows: The real-time monitoring system collects dynamic parameters of the target cluster, including at least the number of targets, model, flight altitude, flight speed, flight trajectory, payload type, signal characteristics, and signal strength. A threat assessment model is constructed based on the analytic hierarchy process, and dynamic parameters are used as assessment indicators and assigned weights. Establish membership functions for each indicator, generate a membership matrix to calculate threat assessment values, and classify the threat levels of each target into high, medium, and low levels based on thresholds; The value of the target cluster is evaluated by scoring, including engineering value, perceived value, and overall value; a value evaluation model is constructed to calculate the value score. Combining threat level and value score, the target cluster is divided into core targets, important targets and general targets using the K-means clustering algorithm.

3. The closed-loop adaptive defense decision support method according to claim 1, characterized in that, Step 2 is as follows: Using the target classification results obtained in step 1 as input, a directional energy damage effectiveness evaluation function is established. The damage effectiveness under different proposed strategies is iteratively calculated using the Monte Carlo simulation method. Strategies with greater damage effectiveness and lower energy consumption are selected to form a potential damage strategy set.

4. The closed-loop adaptive defense decision support method according to claim 2 or 3, characterized in that, Based on the optimal damage effect in step 2, the deployment of the device, energy allocation and power selection of the device, and target allocation are carried out on the basis of the proposed strategy. The specific method is as follows: The deployment of the device adopts the simulated annealing algorithm, expanding outward from the protected key area to obtain the maximum deployment range of the device, and establishing the strike effectiveness equation as the objective function for device deployment; Energy allocation adopts a priority allocation strategy based on the target classification results; The target allocation adopts a genetic algorithm. Based on the potential destruction strategy set, device deployment and energy allocation in step 2, an initial solution is formed. The strike effectiveness function is established as the target function and iterated to maximize the total destruction effect.

5. The closed-loop adaptive defense decision support method according to claim 2, characterized in that, The operation is carried out according to the strategies formulated in step 3 to obtain the degree of damage, damage range, and influence of environmental factors on targets of different types and materials. The specific method is as follows: During the operation, target data is collected in real time using high-definition high-speed cameras and damage detection sensors; The degree of structural damage is classified as mild, moderate, or severe based on the proportion of the damaged area of ​​the target aircraft. Functional damage is determined by detecting the interruption rate of the target's communication link, navigation and positioning errors, and flight attitude stability. Environmental factors affect the recording of visibility, humidity, and wind speed parameters.

6. The closed-loop adaptive defense decision support method according to claim 1, characterized in that, The specific method for step 5 is as follows: The structural and functional damage data obtained in step 4 are fed back to the monitoring and evaluation system to optimize the threat assessment and value classification of the target cluster. Environmental factors are fed back into the directed energy damage performance assessment system to optimize the energy consumption, accuracy, and stability of directed energy devices, update the calculation model of environmental correction factors, and improve the environmental adaptability and anti-interference ability of directed energy devices during operation.

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