A real area-based multi-target aiming point optimization method and system

Abstract

The present application relates to a kind of multi-target aiming point optimization method and system based on real area, belong to ammunition firepower distribution technical field, solve the low timeliness of aiming point calculation method in prior art, the problem of low accuracy.Based on the attack task set, the feasible aiming area corresponding to each attack task is calculated, based on the target to be attacked, a candidate target combination set is generated, based on the candidate target combination set, a candidate scheme set is generated, based on the preset cost benefit function, each candidate scheme in the candidate scheme set is screened to obtain a target scheme set, based on the particle swarm optimization algorithm, each target scheme in the target scheme set is optimized, and the optimal ammunition aiming point corresponding to each target scheme is obtained.A multi-target aiming point optimization method and system with high timeliness and high accuracy are realized.

Description

Technical Field

[0001] This invention relates to the field of ammunition fire distribution technology, and in particular to a method and system for optimizing multi-target aiming points based on real-world areas. Background Technology

[0002] In the damage-based aiming point optimization problem, dealing with diverse ammunition types (e.g., different attributes such as power, accuracy, and damage mechanism), varied target encounter conditions (e.g., impact velocity, impact angle, azimuth angle), and the exponential matching combinations between the two, damage calculation (i.e., damage effect and probability) needs to simultaneously satisfy nonlinear damage coupling and time-coordinated constraints in a high-dimensional solution space. Therefore, how to balance the influence of various factors, propose effective damage calculation methods, and achieve both scientific rigor and timeliness in aiming point optimization calculation has become a key issue.

[0003] At present, most traditional ammunition aiming point optimization methods only consider the impact of the uncertainty of the impact point on the damage effect. Although this has certain practical value, it still has the following problems: (1) The aiming point optimization method based on CEP mainly improves the optimization algorithm itself to meet the computational timeliness requirements in actual use. This type of method does not consider the endpoint damage effect, and the optimization result lacks damage effect support, which reduces its application value; (2) In recent years, with the deepening of damage research, some optimization algorithms have begun to consider the damage effect and optimize through the damage matrix. However, this type of method simplifies the damage consideration, does not consider the impact of intersection conditions, and assumes that the damage area of ​​the ammunition to the target remains unchanged, which weakens the scientific nature of the optimization method.

[0004] Therefore, there is a need to provide a multi-target aiming point optimization method and system that is timely and accurate. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for optimizing multi-target aiming points based on real areas, in order to solve the problems of low timeliness and low accuracy of existing aiming point calculation methods.

[0006] On one hand, embodiments of the present invention provide a method for optimizing multi-target aiming points based on real-world regions, the method comprising: Obtain a set of strike missions, wherein each strike mission in the set includes a type of munition and a target to be struck; Calculate the feasible targeting area for each strike mission; A set of candidate target combinations is generated based on the targets to be attacked; For each candidate target combination in the candidate target combination set, the strike mission corresponding to each candidate target in the candidate target combination is obtained based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is the same as the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under the ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The combination of each candidate solution in the candidate solution set is selected based on a preset cost-benefit function to obtain the target solution set; The particle swarm optimization algorithm is used to optimize each target scheme in the target scheme set to obtain the optimal aiming point of the ammunition for each target scheme.

[0007] Based on further improvements to the above method, the strike mission also includes the quantity of munitions, the target interaction conditions, and the damage level; The calculation of the feasible targeting area for each strike mission includes: Based on the damage level, obtain the relatively effective damage area of ​​the target to be hit under the ammunition type where the damage probability is not lower than a preset damage probability threshold. Then, rotate and translate the relatively effective damage area to obtain a feasible aiming area.

[0008] A further improvement to the above method, the step of obtaining the relatively effective damage area of ​​the target under the ammunition type with a damage probability not lower than a preset damage probability threshold based on the damage level, includes: Obtain the power field data model corresponding to the ammunition type, and the target vulnerability data model corresponding to the target to be hit; Based on the power field data model, the target vulnerability data model, and the projectile-target interaction conditions, the damage probability matrix corresponding to the ammunition type is calculated, where each element value in the damage probability matrix represents a damage probability value. A binary damage matrix is ​​obtained based on the preset damage probability threshold and the damage probability matrix; After marking the connected components of the binary damage matrix, the ordered outer boundary is obtained using the Moore neighborhood boundary tracking method. The relatively effective damage area is constructed based on the ordered outer boundary.

[0009] A further improvement to the above method is that generating a candidate target combination set based on the target to be attacked includes: generating the candidate target combination set using an enumeration method or a clustering method; The calculation of the shared feasible aiming area based on the feasible aiming areas of each target under this ammunition type includes: calculating the intersection of the feasible aiming areas corresponding to each target under this ammunition type, and taking the result of the intersection as the shared feasible aiming area.

[0010] Based on a further improvement of the above method, the preset cost-benefit function is: , , , , Where j is the j-th candidate target in the current candidate scheme combination, j=1,2,3,…,N, and N is the number of candidate targets in the current candidate scheme combination; The value weight of the j-th candidate objective. It is a binary variable. This indicates that the j-th candidate objective is covered by at least one candidate scheme in the current combination of candidate schemes. , indicating that the j-th candidate target is not covered by at least one candidate scheme in the current combination of candidate schemes; The single-unit cost weight of the ammunition type h is given by [the relevant factor]. Let h be the number of ammunition of type h required in the q-th candidate scheme. The type of ammunition in the q-th candidate scheme; It is a binary variable. This indicates that the q-th candidate solution in the current candidate solution set is selected as one of the candidate solutions in the candidate solution combination. , which means not selecting the q-th candidate solution in the current candidate solution set to form a candidate solution combination; Let q be the number of candidate targets in the candidate scheme q, where q = 1, 2, 3, ..., Q, and Q is the number of candidate schemes in the combination of candidate schemes; The minimum target coverage ratio; The number of ammunition types h corresponds to the number of ammunition types h.

[0011] Based on a further improvement to the above method, the step of optimizing each target scheme in the target scheme set using the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition corresponding to each target scheme includes: N1: Initialize the particle swarm. Each particle vector contains the aiming point coordinates corresponding to each target scheme, and each particle can only move within the shared feasible aiming area corresponding to its target scheme. N2: Initial velocities are randomly generated for each particle; N3: Calculate the fitness function value for each particle; N4: Update the individual's historical best position and global best position based on the fitness function value of each particle; N5: Update the velocity and position of each particle based on the individual's historical best position and the global best position, and perform feasible domain boundary verification on the updated position; N6: Determine whether the preset convergence condition has been met. If not, return to step N3. If it has been met, use the global optimal position as the optimal aiming point of the ammunition.

[0012] Based on a further improvement of the above method, the fitness function in the particle swarm optimization algorithm is: , , , , in, Let the target weight be the target weight corresponding to the j-th candidate target. Let J be the overall damage probability corresponding to the j-th candidate target. , , For the weight function, Penalties for violating aiming point constraints As a penalty for incidental damage; Let K be the shortest distance from the kth aiming point to the corresponding feasible aiming area, where k = 1, 2, 3, ..., K, and K is the total number of aiming points. Indicates a non-target area. This indicates that the damage level is reached at the aiming point (x, y). The probability of 100% or higher.

[0013] Based on a further improvement to the above method, the overall damage probability corresponding to the j-th candidate target is calculated as follows: Obtain the target schemes containing the j-th candidate target and construct a target strike scheme set; For each target strike scheme in the set of target strike schemes, calculate the single target destruction probability corresponding to that target strike scheme; The overall damage probability is calculated based on the damage probabilities of all individual targets.

[0014] On the other hand, embodiments of the present invention provide a multi-target aiming point optimization system based on a real-world region, the system comprising: The acquisition module acquires a set of strike missions, wherein each strike mission in the set includes a type of ammunition and a target to be struck. The calculation module calculates the feasible aiming area corresponding to each strike mission and generates a set of candidate target combinations based on the target to be struck. The candidate scheme generation module, for each candidate target combination in the candidate target combination set, obtains the strike mission corresponding to each candidate target in the candidate target combination based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is consistent with the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under that ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The target solution generation module filters the combinations of each candidate solution in the candidate solution set based on a preset cost-benefit function to obtain the target solution set; The aiming point generation module optimizes each target scheme in the target scheme set based on the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition corresponding to each target scheme.

[0015] Based on further improvements to the aforementioned system, the strike mission also includes the quantity of munitions, the interaction conditions between the munition and the target, and the damage level.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: This invention provides a method and system for optimizing multi-target aiming points based on real-world regions. It optimizes aiming points by constructing a damage probability matrix between the ammunition and the target, offering strong physical interpretability and improving the scientific rigor of the aiming point results. Calculating the damage range matrix based on ammunition-target intersection conditions is more reasonable than traditional coverage-based optimization using a fixed radius of power, thus improving the accuracy of the optimization results. Through intersection operations of the damage matrix, the optimization area for the aiming point can be determined in advance, eliminating the need for traversing the entire target area for aiming point optimization calculations. This reduces the search area, decreases the amount of computation, and significantly improves the timeliness of the optimization.

[0017] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0018] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 An example diagram illustrating a multi-target aiming point optimization method based on a real-world region, provided by an embodiment of the present invention; Figure 2 An example diagram of a force field data model provided in an embodiment of the present invention; Figure 3 An example diagram of a damaged tree at a minor damage level provided in an embodiment of the present invention; Figure 4 An example diagram of a local coordinate system for a vehicle target provided in an embodiment of the present invention; Figure 5 This is an example diagram illustrating the matrixing of the power field data of two types of explosive warheads and the extraction of the true damage contour, provided as an embodiment of the present invention. Detailed Implementation

[0019] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0020] High-explosive fragmentation warheads (HIF warheads) are currently one of the primary means of destroying ground personnel and lightly armored targets. In combat, these munitions detonate near the target, relying on high-speed fragmentation fields and shock waves to destroy the target, and are characterized by their wide blast radius.

[0021] The real area in this invention is essentially a real damage area. This area is not limited to the traditional circular radius, but refers to the effective damage boundary of the explosive warhead in different directions, determined by simulation, test or measured data. This boundary can be represented as a non-circular closed polygon, or it can be further converted into an equivalent damage area function that varies with the angle.

[0022] CEP (Circular Error Probable) is an important accuracy indicator for ammunition / weapon systems. Generally, the CEP value can be determined by identifying the type of ammunition, and this value can also be obtained by querying the ammunition attribute database.

[0023] Example 1 A specific embodiment of the present invention discloses a multi-target aiming point optimization method based on a real-world region, such as... Figure 1 As shown, the method includes: Obtain a set of strike missions, wherein each strike mission in the set includes a type of munition and a target to be struck; Calculate the feasible targeting area for each strike mission; A set of candidate target combinations is generated based on the targets to be attacked; For each candidate target combination in the candidate target combination set, the strike mission corresponding to each candidate target in the candidate target combination is obtained based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is the same as the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under the ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The combination of each candidate solution in the candidate solution set is selected based on a preset cost-benefit function to obtain the target solution set; The particle swarm optimization algorithm is used to optimize each target scheme in the target scheme set to obtain the optimal aiming point of the ammunition for each target scheme.

[0024] This invention provides a multi-target aiming point optimization method based on real-world regions. It enables optimization of aiming points for multiple projectiles against multiple targets. When obtaining the optimal aiming point, a damage probability matrix of the real damage area is introduced, further improving the accuracy and timeliness of aiming point optimization. Furthermore, the calculation considers the half-regions of damage to different targets by different projectiles under different intersection conditions, making the proposed aiming point optimization method more reasonable and more closely aligned with real-world situations.

[0025] The following section will provide a detailed explanation of the implementation of a multi-target aiming point optimization method based on real-world regions.

[0026] S1: Obtain a set of strike missions, wherein each strike mission in the set includes a type of ammunition and a target to be struck.

[0027] The strike mission includes ammunition type, target, ammunition quantity, target-discovery interaction conditions, and damage level. Ammunition type can include high-explosive fragmentation rounds, cluster munitions, and fragmentation rounds. Targets can be vehicles or other types of objects (e.g., buildings). Ammunition quantity is a discrete value and can be set according to actual needs. Damage level includes minor, moderate, and severe damage.

[0028] For example, if the target to be attacked is a vehicle, then it is necessary to obtain the type of vehicle target and the corresponding number of vehicle targets in the current scenario.

[0029] For example, the bullet-target interaction condition is represented as follows: , in, H represents the target-missile interaction vector between the i-th munition and the j-th target; i To fry high, vi For the falling velocity, γ i Let ψ be the angle of fall. ij ρ is the relative azimuth angle between the projectile and the target. i Let α be the roll angle. i The angle of attack. In practical applications, one or more parameters can be selected to construct the projectile-target interaction conditions according to actual needs.

[0030] S2: Calculate the feasible targeting area for each strike mission, including: S21: Based on the damage level, obtain the relatively effective damage area of ​​the target to be attacked, where the damage probability under the given ammunition type is not lower than a preset damage probability threshold. This step includes: S211: Obtain the power field data model corresponding to the ammunition type, and the target vulnerability data model corresponding to the target to be hit.

[0031] The power field data model is constructed based on the inherent properties of the munition and is used to represent the power caused by the shock wave overpressure and fragmentation generated by a high-explosive fragmentation warhead (including munitions). This power can be obtained through simulation and experimentation. The power field data of the munition consists of the shock wave overpressure field and fragmentation field centered on the munition, such as... Figure 2 As shown, a fragment of mass m is ejected outward with an initial velocity v. The shock wave overpressure time history data at the dot can be represented by the small graph in the upper right corner of the figure, where Pmax is the peak overpressure and ta is the duration of the positive pressure.

[0032] The target vulnerability data model is constructed based on the target's inherent properties and is used to indicate the damage level corresponding to each component of the target when damaged. The target vulnerability data model can be represented by a damage tree, where the bottom layer of the damage tree represents the target components and the top layer represents the damage level; that is, one damage level corresponds to one damage tree, and the damage tree structure differs for the same damage level on different targets. For example... Figure 3 The diagram illustrates an example damage tree for a minor damage level. The target consists of System 1, System 2, and System 3. Damage to any subsystem will cause minor damage to the target. Specifically, damage to System 1 requires damage to both components A1 and A2. Damage to either component B or C in System 2 will damage System 2. System 3 has only one component, D; damage to D will result in damage to System 3. It should be noted that the target's damage tree includes all components; however, under different target-missile encounter conditions, only the vulnerable components in the damage tree need to be considered.

[0033] For example, when the relative positions of the target and the munition are different, the vulnerable parts of the target are different. When performing target-missile rendezvous calculations, the vulnerability data of the target can be represented as follows: Let the j-th vehicle target be represented as: , Among them, t j φ is the target reference point. j Let g be the target azimuth angle. j For the target type, K j R is an optional set of vulnerable key points. j This is a set of optional vulnerable polygon regions. The set of vulnerable key points is defined as follows: , Where, r jk Let w be the position of the k-th vulnerable part of the j-th target in the target's local coordinate system. jk Assign weights to the corresponding parts, and preferably satisfy Σ. k w jk =1. The set of vulnerable regions R j Composed of one or more local polygons R jk composition.

[0034] In fact, technicians can develop and set up a database of ammunition power field corresponding to the power field data model and a database of target vulnerability corresponding to the target vulnerability data model in advance. Then, when using it, the corresponding model can be directly called according to the ammunition type and the target to be hit.

[0035] This step essentially involves retrieving the corresponding power field data model from the ammunition power field database based on the type and quantity of ammunition in the strike mission, and retrieving the corresponding target vulnerability data model from the target vulnerability database based on the target to be struck.

[0036] S212: Calculate the damage probability matrix corresponding to the ammunition type based on the power field data model, the target vulnerability data model, and the projectile-target interaction conditions. Each element value in the damage probability matrix represents a damage probability value.

[0037] First, establish a coordinate system representation of the target to be attacked. That is, set a discrete grid of the relative position of the warhead in the target's local coordinate system. The relative coordinate of the warhead at the m-th row and n-th column grid point is denoted as d. mn =(x m , y n ),like Figure 4 As shown, a local coordinate system for the target is established with the direction of the vehicle's front as the positive y-axis, and the side length of a single grid can be set to 1 to 5 meters.

[0038] Then, given the preset conditions of ammunition type h, target type g, and target rendezvous condition c, for each relative position d mnNMC Monte Carlo simulations are performed. Each simulation considers the randomness of fragment dispersion, the dispersion of warhead parameters, the dispersion of material parameters, detonation error, and other random factors, and records the final damage level achieved by the target. Let the damage level of the s-th simulation be: , In this system, 0 indicates no effective damage, 1 indicates minor damage, 2 indicates moderate damage, and 3 indicates severe damage. The damage level thresholds are as follows: The number of samples that reached this level or above is: , The corresponding grid location has reached the damage level. The probability of damage under conditions of 1 and above is: , Take respectively =1, 2, 3, construct the damage probability matrix for mild damage and above, moderate damage and above, and severe damage: .

[0039] Because it adopts the cumulative definition of "reaching the corresponding level or above", it theoretically satisfies If the monotonicity is not satisfied due to statistical fluctuations in a finite sample, the Monte Carlo sample size can be increased, or monotonicity constraint correction can be applied.

[0040] When the conditional damage probability matrix has already been obtained from simulation or experimentation using a complete vehicle model, this probability matrix already includes the target type and vulnerability effects. Subsequent vehicle damage probabilities can be directly obtained from this probability matrix without repeatedly mapping probabilities using geometric coverage weights. That is, when the probabilities of minor damage and above, moderate damage and above, and severe damage at each grid location under the same computational conditions have already been calculated (i.e., already exist in the database), the aforementioned probability matrix can be directly constructed without repeated calculations. Storing existing data in the database further improves the efficiency of obtaining the optimal aiming point.

[0041] This invention constructs three damage probability matrices: mild, moderate, and severe conditional damage probability matrices. This approach preserves the probability differences at different spatial locations, preventing the mixing of probability information with binary overlay results. Furthermore, the true power field of the high-explosive warhead is used for aiming point optimization, avoiding evaluation bias caused by a fixed circular damage area.

[0042] S213: Obtain a binary damage matrix based on the preset damage probability threshold and the damage probability matrix.

[0043] Among them, the preset conditional damage probability threshold is used to indicate the location d. mnThe probability of damage occurring. A probability threshold τ is set for each damage level. The same threshold can be used for each damage level, for example, τ1=τ2=τ3=0.8; different thresholds can also be set according to the mission requirements.

[0044] For damage level The conditional damage probability matrix is ​​converted into a binary damage matrix according to the following formula: , , The damage probability matrix can be converted into a binary matrix through the above calculations. In the binary matrix, a grid cell with a value of 1 indicates that the target has reached a damage level at the corresponding relative position. The probability of τ and above is not lower than the threshold τ. .

[0045] The binary damage matrix and its effective damage region are mainly used for candidate aiming region generation and geometric pre-screening; the original conditional damage probability matrix is ​​retained for subsequent continuous probability calculation.

[0046] For example, taking the optimization of aiming points for eight vehicle targets and two types of munitions as a premise, the conditional damage probability data and probability threshold under this premise are calculated. Two types of munitions are set up, with 22×22 warhead relative position sampling points set for each type of munition under fixed target type and fixed projectile-target intersection conditions, totaling 484 spatial sampling positions; for a total of 968 spatial sampling positions for both types of munitions. NMC Monte Carlo simulations are performed at each spatial sampling position, and the probabilities of minor damage and above, moderate damage and above, and severe damage are calculated separately. Minor damage and above is used as the geometric pre-screening level, and the probability threshold is set to τ1=0.8. For each type of munition, when the probability of minor damage and above at a certain grid position is not less than 0.8, the corresponding binary matrix element is set to 1; otherwise, it is set to 0.

[0047] S214: After marking the connected components of the binary damage matrix, the ordered outer boundary is obtained by Moore neighborhood boundary tracking method.

[0048] After obtaining the binary damage matrix, cells with a value of 1 are grouped using a four-neighbor or eight-neighbor connected component labeling method to obtain one or more independent effective damage sub-regions. Subsequently, the Moore neighborhood boundary tracking method is used to extract the ordered outer boundary for each connected component. If a closed region formed by cells with a value of 0 exists within a connected component, it is identified as an inner hole and its inner boundary is saved. Saving the inner boundary preserves the complete topological structure of the effective damage region, avoiding misclassification of regions with actual damage probabilities below the threshold as effective regions during subsequent region intersection, candidate aiming point selection, and area calculation.

[0049] In extracting ordered outer boundaries, the Marching Squares method or a polygonization method based on grid cell edges can also be used.

[0050] Figure 5 The figure shows the threshold binary matrix and effective damage area boundary for two types of ammunition. In the figure, 484 represents the number of spatial sampling locations, not the total number of Monte Carlo samples. As can be seen from the figure, neither type of power field is a regular circle. The first type of power field is generally longitudinally striped with a narrow lateral range; the second type of power field has a more pronounced lateral expansion and exhibits local depressions and discontinuous boundaries. Therefore, this embodiment uses the actual damage contour as the basis for subsequent aiming point optimization, rather than using a uniform circular damage area approximation. Comparing the two types of actual damage contours, it is evident that different power field types have significant differences in lateral, longitudinal, and local boundary morphology. Failure to distinguish between power field types will lead to deviations in multi-target coverage relationships and aiming point optimization results.

[0051] S215: Construct the relatively effective damage area based on the ordered outer boundary.

[0052] The intersection of the multiple outer boundary regions (i.e., effective damage sub-regions) obtained in step S214 yields the relatively effective damage region. Step S215 can be expressed as: , Where S is the number of mutually unconnected effective damaged sub-regions, which is automatically counted by the connected component labeling algorithm. It is the number of mutually unconnected effective damaged sub-regions that meet the damage probability threshold requirements. This represents the s-th effective damage sub-region. For cases with depressions, holes, or multiple discontinuous regions, each outer and inner boundary is preserved separately, without forcibly connecting them into a single simple polygon. This method of setting irregular damage boundaries can adapt to damage differences under varying azimuth angles, impact angles, blast heights, and velocities.

[0053] S22: Rotate and translate the relatively effective damage area to obtain a feasible aiming area.

[0054] For example, the global relative coordinates between the i-th ammunition and the j-th target can be converted to the target's local coordinates as follows: Let the global coordinates of the candidate aiming point or detonation point of the i-th ammunition be ai. Let the global coordinates of the reference point of the j-th target be tj. 。 This invention defines the projectile-target relative coordinates as "the position of the warhead aiming point relative to the target reference point," then the global relative coordinates between the i-th projectile and the j-th target are: .

[0055] Let φ be the azimuth angle of the j-th target. j Transforming the global relative coordinates to the target local coordinate system, we have: , , Where R(φ) is a two-dimensional rotation matrix. Target azimuth angle φ j Used only for geometric transformations between the target's local and global coordinate systems; the relative azimuth angle ψ between the projectile and the target. ij It belongs to the projectile-target intersection condition, and is used to select or interpolate to obtain the corresponding conditional damage probability matrix. The two are not interchangeable.

[0056] Specifically, for the i-th ammunition, let its type be h. i For the j-th target, let its type be g. j The position is t j The azimuth angle is φ j The condition for missile-target rendezvous is c. ij The relatively effective damage area in the target's local coordinate system is denoted as... Rotate the relatively effective damage area according to the target azimuth angle and translate it to the target reference point to obtain the feasible aiming area of ​​the i-th munition against the j-th target: , Equivalent land: , Among them, Ω ij ( ,τ This indicates that when the aiming point of the i-th ammunition is within this area, the j-th target reaches the damage level. The probability of τ and above is not lower than the threshold τ. It is evident that this area is simultaneously related to ammunition type, target type, target location, target azimuth, target-missile encounter conditions, damage level, and probability threshold.

[0057] Therefore, for any point (x, y) on the boundary of the relatively effective damage area, the transformed global coordinates are: , .

[0058] S3: Generate a set of candidate target combinations based on the target to be attacked, including: generating the set of candidate target combinations using enumeration or clustering methods.

[0059] Each candidate target combination is an arbitrary combination of targets to be engaged. First, candidate target combinations are generated based on target spatial distance, effective damage area overlap, or other geometric constraints. Then, the intersection of the shared feasible targeting area is calculated for each target combination; combinations with empty intersections are discarded. This allows for the generation of target combinations consisting of two or more targets, and also allows for the retention of single-target candidate areas for targets that have not entered any multi-target combinations.

[0060] For example, for the enumeration method, firstly, a subset of all targets with a number greater than or equal to 2 is generated, and then the target subset is filtered based on the target spatial distance constraint to finally obtain a set of candidate target combinations. For the clustering method, firstly, a graph model is constructed (i.e., each target is a node, and targets that satisfy the distance constraint are connected by edges), and then all maximal cliques in the graph model are solved (each maximal clique corresponds to a candidate target combination), and a clique that includes at least 2 targets is taken as a candidate target combination.

[0061] When obtaining candidate target combinations, if a single target cannot meet the distance constraint, the target is treated as an independent candidate target combination and added to the candidate target combination set.

[0062] After obtaining the set of candidate target combinations, a redundancy removal operation is required. Specifically, during candidate scheme redundancy removal, only schemes with completely identical target combinations and identical or highly overlapping geometric regions are merged. A scheme should not be directly deleted simply because it is a subset of another target combination, as a candidate region covering fewer targets may have a larger area, a higher probability of damage, or stronger robustness against firing errors. Only when a candidate scheme is not superior to another candidate scheme in terms of target benefit, ammunition cost, feasible area, and robustness can it be deleted as the dominant scheme.

[0063] Redundancy Removal Example 1: Complete Duplication: If two schemes use the same ammunition type, target the same combination of targets, share the same feasible aiming area, and provide no difference in combat capability, then only one scheme needs to be retained, and the other is deleted as a duplicate scheme. Scheme A: Ammunition type: 1 Type A ammunition; Target: {T1,T2}; Area: Shared feasible aiming area is ΓA. Scheme B: Ammunition type: 1 Type A ammunition; Target: {T1,T2}; Area: Shared feasible aiming area is also ΓA.

[0064] Redundancy Removal Example 2: Same Target Combination, Different Feasible Area Sizes: Two schemes target the same combination of targets and use the same ammunition type. However, Scheme A has a larger shared feasible aiming area, offering better hit tolerance and robustness in the presence of shooting errors. Therefore, given similar evaluation metrics, Scheme A should be retained, and Scheme B should be deleted. Scheme A: Ammunition Type: 1 Type A ammunition; Target: {T1,T2}; Area: Shared feasible aiming area is approximately 20m². Scheme B: Ammunition Type: 1 Type A ammunition; Target: {T1,T2}; Area: Shared feasible aiming area is approximately 8m².

[0065] Example 3 of Redundancy Removal: Target Combinations Have Inclusion Relationships but Cannot Be Directly Deleted: Although Scheme B covers more targets, the area where the three targets jointly meet the damage probability requirement is significantly reduced due to the addition of target T3, leaving only a very small area. Scheme A, although covering fewer targets, has a larger feasible area, higher shooting error tolerance, and better engineering feasibility. Therefore, Scheme A should not be deleted simply because its target combination is a subset of Scheme B; both schemes should be retained for subsequent optimization. Scheme A: Projectile Type: 1 Type A ammunition; Targets: {T1, T2}; Area: Shared feasible aiming area is approximately 15m². Scheme B: Projectile Type: 1 Type A ammunition; Targets: {T1, T2, T3}; Area: Shared feasible aiming area is approximately 0.5m².

[0066] After the above operations, a set of target combinations that satisfy preset target spatial distance, effective damage area overlap relationship, or other geometric constraints can be obtained.

[0067] S4: For each candidate target combination in the candidate target combination set, obtain the strike mission corresponding to each candidate target in the candidate target combination based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is consistent with the number of candidate targets in the candidate target combination, then calculate a common feasible aiming area based on the feasible aiming area of ​​each target under the ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set.

[0068] Specifically, for each candidate target combination in the candidate target combination set, the strike missions corresponding to each candidate target in the candidate target combination are obtained based on the strike mission set to form a candidate strike mission set. The research object of this step is each candidate target, which is essentially determining all strike missions corresponding to the current candidate target combination.

[0069] If the number of identical munitions in the candidate strike mission set matches the number of candidate targets in the candidate target combination, a shared feasible aiming area is calculated based on the feasible aiming areas of each target under that munition type. Essentially, this step determines whether there exists a single type of munition in the candidate strike mission set capable of engaging all candidate targets in the candidate target combination. This maximizes the value of a single munition, allowing it to engage all targets in the target combination. If the number of identical munitions in the candidate strike mission set does not match the number of candidate targets in the candidate target combination, a shared feasible aiming area is not calculated.

[0070] The calculation of a shared feasible aiming area based on the feasible aiming areas of each target under this ammunition type includes: calculating the intersection of the feasible aiming areas corresponding to each target under this ammunition type, and using the intersection result as the shared feasible aiming area. For a target combination, its shared feasible aiming area is calculated under the corresponding ammunition type. If the shared feasible aiming area is not empty, a candidate scheme is formed. Each candidate scheme corresponds to an implementable multi-target engagement method, and each candidate scheme q includes at least the ammunition type hq, the target combination Cq, and the shared feasible aiming area Γq. This scheme does not require a single candidate scheme to cover all targets.

[0071] For example, for the same ammunition type h and target combination C, the feasible aiming regions corresponding to each target in the combination are intersected by polygons to obtain a shared feasible aiming region that simultaneously satisfies the damage probability threshold of multiple targets for that ammunition type: , when When not empty, it indicates that there is at least one aiming point such that the same ammunition of type h achieves damage level against each target in target combination C. The probabilities of all of the above are not lower than the threshold τ. .

[0072] For example, for both Type I and Type II munitions, the feasible aiming areas of two or more targets are combined and intersected. Each non-empty intersection generates a candidate scheme, which records the munition type, target set, and shared feasible aiming area. For targets that do not enter any multi-target non-empty intersection, single-target candidate schemes are retained. That is, when the number of the same munition type in the current candidate strike task set is the same as the number of candidate targets in the candidate target combination, the feasible aiming areas of the targets do not intersect. In this case, the feasible aiming area corresponding to each candidate target is taken as a candidate scheme. This embodiment requires that the proportion of targets with a comprehensive probability of minor damage or above reaching a specified threshold is not less than 80%, that is, the number of effectively damaged targets is not less than: 0.8×8 =7. Select several schemes from the candidate schemes using weighted set coverage or 0-1 integer programming, so that at least 7 objectives are covered by the candidate schemes, while reducing the number of ammunition used while meeting the objective benefit requirements.

[0073] In this way, as long as the ammunition hits any point in the shared feasible aiming area, it can destroy all targets in the candidate target combination.

[0074] S5: Based on a preset cost-benefit function, filter the combinations of each candidate solution in the candidate solution set to obtain the target solution set.

[0075] The preset cost-benefit function is a cost estimation function. Specifically, the preset cost-benefit function is: , , , , Where j is the j-th candidate target in the current candidate scheme combination, j=1,2,3,…,N, and N is the number of candidate targets in the current candidate scheme combination; The value weight of the j-th candidate objective. It is a binary variable. This indicates that the j-th candidate objective is covered by at least one candidate scheme in the current combination of candidate schemes. , indicating that the j-th candidate target is not covered by at least one candidate scheme in the current combination of candidate schemes; The single-unit cost weight of the ammunition type h is given by [the relevant factor]. Let h be the number of ammunition of type h required in the q-th candidate scheme. The type of ammunition in the q-th candidate scheme; It is a binary variable. This indicates that the q-th candidate solution in the current candidate solution set is selected as one of the candidate solutions in the candidate solution combination. , which means not selecting the q-th candidate solution in the current candidate solution set to form a candidate solution combination; Let q be the number of candidate targets in the candidate scheme q, where q = 1, 2, 3, ..., Q, and Q is the number of candidate schemes in the combination of candidate schemes; The minimum target coverage ratio; The number of ammunition types h corresponds to the number of ammunition types h.

[0076] For example, the specified damage level is achieved and the overall damage probability is not lower than the target threshold τ. j ( The effective number of targets damaged is defined as: , The effective damage target ratio is: , When the required effective damage target ratio is not less than When, the constraint is: , It should be noted that the number of geometrically covered targets, the number of effectively damaged targets with a probability threshold, and the proportion of effectively damaged targets are different concepts and are calculated separately in the entire text, and are not interchangeable.

[0077] For example, suppose a total of Q candidate solutions are generated, and a binary variable y is defined. q ∈{0,1}, where y q =1 indicates that the q-th candidate solution is selected, y q =0 indicates no selection. Define u. j ∈{0,1} indicates whether target j is covered by at least one candidate solution. The q-th candidate solution is represented as: q=( h q , C q ,Γ q ),in, h q For ammunition type, C q To strike the target combination, Γ q To determine the feasible aiming area. qh ∈Z≥0 represents the number of ammunition types h assigned to the q-th candidate scheme. Candidate scheme selection can employ 0-1 integer programming, weighted set covering, greedy set covering, genetic algorithms, discrete particle swarm optimization, or other discrete optimization methods. Here, we illustrate the implementation using 0-1 integer programming: assuming the candidate scheme set includes 4 candidate schemes, as shown in Table 1 below.

[0078] Table 1

[0079] Let the target value weights be w1=0.8, w2=0.6, w3=0.7, w4=1.0, and define the decision variable y. q ={1,0}, the objective function is:

[0080] Where: the first term represents the benefit gained from covering the target; the second term represents the cost incurred by the consumed ammunition. There are currently 4 ammunitions: 2 Type A ammunitions, 1 Type B ammunition, and 1 Type C ammunition. Therefore: Type A ammunition: 1y1 + 2y3 ≤ 2, Type B ammunition: 1y2 ≤ 1, Type C ammunition: 1y4 ≤ 1. If the requirement is to cover at least 75% of all targets, then (u1 + u2 + u3 + u4) / 4 ≥ 0.75. Solution process: Use a 0-1 integer programming solver (such as Branch and Bound or Branch and Cut algorithm) to solve the model. If the optimal result is: y1 = 1, y2 = 0, y3 = 0, y4 = 1, it indicates that schemes q1 and q4 are selected. In this case: use 1 Type A ammunition and 1 Type C ammunition; cover targets T1, T2, T3, and T4. This satisfies the coverage requirement, while maximizing target benefit and minimizing overall cost. By incorporating the vulnerable parts of the vehicle target and the target value weight into the optimization process, the scientific nature of the target selection is improved.

[0081] After selection, if K candidate schemes are selected, then K also represents the number of ammunition used in the current scheme. Each selected scheme has determined the ammunition type and target combination. Subsequent particle swarm optimization only optimizes the continuous coordinates of the aiming point, avoiding directly substituting discrete ammunition types into the ordinary continuous particle swarm update formula.

[0082] S6: Optimize each target scheme in the target scheme set based on the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition corresponding to each target scheme, including: N1: Initialize the particle swarm. Each particle vector contains the aiming point coordinates corresponding to each target scheme, and each particle can only move within the shared feasible aiming area corresponding to its target scheme.

[0083] For the selected K candidate schemes, the particle encoding is as follows: , in, Let Γi represent the aiming point of the k-th ammunition (or the k-th candidate scheme). Therefore, each coordinate has its own feasible region. For example, scheme 1 corresponds to Γ1, scheme 2 to Γ2, and scheme 3 to Γ3. Then, the particle actually represents: the aiming point of particle i. In this context, A1 can only be searched in Γ1, A2 can only be searched in Γ2, and A3 can only be searched in Γ3.

[0084] The k-th aiming point is initialized within the corresponding candidate shared feasible aiming region Γk. To avoid excessive exclusion of probabilistically optimal points by geometric pre-screening, Γk can be expanded outward by a preset buffer distance, or a certain proportion of global exploration particles can be retained. If a global rectangular search boundary needs to be constructed, the horizontal and vertical expansion amounts are calculated based on all effective damage areas respectively: , Δx and Δy are used to account for target size, firing error, or safety margin, and do not employ a fixed extension distance unrelated to the actual effective damage area. The lateral and longitudinal extension amounts Δx and Δy can be determined using the formulas Δx = Lx,max / 2 + kσx + δs and Δy = Ly,max / 2 + kσy + δs, respectively. Here, Lx,max and Ly,max represent the maximum target size, σx and σy are the standard deviations of the firing error, k is the error coverage coefficient (typically 2-3), and δs is the safety margin, often 0.5m or 1m in engineering. This ensures the search space covers the true optimal aiming point while avoiding the problems of an excessively large or small search range caused by using a fixed extension distance.

[0085] N2: Initial velocities are randomly generated for each particle.

[0086] N3: Calculate the fitness function value for each particle.

[0087] The fitness function is: , , , , in, Let the target weight be the target weight corresponding to the j-th candidate target. Let J be the overall damage probability corresponding to the j-th candidate target. , , For the weight function, Penalties for violating aiming point constraints As a penalty for incidental damage; Let K be the shortest distance from the kth aiming point to the corresponding feasible aiming area, where k = 1, 2, 3, ..., K, and K is the total number of aiming points. Indicates a non-target area. This indicates that the damage level is reached at the aiming point (x, y). The probability of 100% or higher.

[0088] For example, the positions and azimuths of the eight vehicle targets are shown in Table 2, with coordinates in meters. The eight targets can be set to the same target type and have the same target value weight. If the target types or values ​​are different, then the corresponding conditional damage probability matrix is ​​selected and W is set accordingly. j .

[0089] Table 2

[0090] The overall damage probability corresponding to the j-th candidate target is calculated as follows: M1: Obtain the target scheme containing the j-th candidate target and construct a target strike scheme set. For each target strike scheme in the target strike scheme set, calculate the single target destruction probability corresponding to the target strike scheme. For the i-th candidate aiming point in the particle a i For the j-th target, calculate the local relative coordinates of the target: , According to ammunition type h i Target type g j Conditions for missile-target rendezvous c ij and relative coordinates The damage probability of a single missile to a single target is obtained from the corresponding conditional damage probability matrix using bilinear interpolation, nearest neighbor interpolation, or other spatial interpolation methods. , in, The interpolation operator can be bilinear interpolation, nearest neighbor interpolation, or other spatial interpolation methods, i.e., using... As an interpolation lookup table, As interpolation coordinates. During geometric pre-screening, this can be achieved through a... i ∈ Determine whether the probability threshold has been reached; for fine-grained optimization, continuous probability is used. The purpose of geometric pre-screening is to reduce the computational complexity of probability calculations for large-scale ammunition-target combinations. Since the target damage probability is closely related to the ammunition's position relative to the target, only candidate impact points located within the target's effective area of ​​effect can generate the required damage probability. Therefore, before probability calculation, it is first determined whether candidate points enter the target's effective area of ​​effect based on the relative geometric relationship between the projectile and the target. This eliminates spatial areas that clearly cannot meet the damage requirements, thus avoiding interpolation calculations for a large number of low-probability areas. If the current aiming point... a i If the target is not within its effective range, spatial interpolation is not performed; instead, the probability of damage for the missile-target combination is assigned the lowest penalty value (e.g., 0 or 10). -4 ).

[0091] M2: The overall damage probability is calculated based on the damage probabilities of all individual targets.

[0092] Under the condition that the damage events of each ammunition to the same target are independent, after the combined action of multiple ammunitions, the j-th target reaches the damage level. The combined damage probability of 2000 and above is: , Where K represents the actual number of munitions used to strike the same target, and M... max This indicates the maximum number of ammunition that the system is allowed to configure; the two have different meanings. In actual mission planning, the actual number of ammunition used satisfies: K ≤ M. max If the maximum number of available ammunition is M max Further settings for ammunition activation variable z i If ∈{0,1}, then it can be written as: , Introducing a binary decision variable z i Afterwards, the ammunition selection problem within the largest candidate ammunition set can be uniformly described. When z i =1 indicates that the i-th ammunition is activated, when z i A value of 0 indicates that the ammunition was not selected. In this case, the formula is equivalent to automatically selecting K ammunition from the maximum available ammunition set to participate in the attack.

[0093] When the impact point errors of each ammunition are correlated, share a common systemic bias, or the damage of the previous shot changes the conditions of the subsequent shot, the above independent probability fusion formula should be replaced by a joint Monte Carlo, conditional probability, or cumulative damage model.

[0094] For example, instead of using a vehicle-wide conditional damage probability matrix, conditional failure probability fields are established for components such as the power compartment, crew compartment, communication equipment, and running gear. Taking a certain type of vehicle as an example, the value weights of each component can be set to 0.35, 0.30, 0.20, and 0.15, respectively. For the i-th ammunition, the j-th target, and the k-th component, the component failure probability p is obtained from the corresponding component-level probability field. ijk The probability of vehicle damage is then calculated based on the vehicle's functional logic, fault tree, reliability block diagram, or a test-calibrated mapping model. If an approximation of a parallel system with independent component failures is used, it can be written as:

[0095] Here, Kj represents the total number of vulnerable components included in the damage analysis for the j-th target. The 0.35 + 0.20 = 0.55 obtained from covering the power compartment and communication equipment only represents the weighted contribution of vulnerable parts and is not directly equivalent to the damage probability. Only through a component failure probability model or a calibrated contribution-damage probability mapping can the overall vehicle damage probability be obtained.

[0096] N4: Update the individual's historical best position and global best position based on the fitness function value of each particle.

[0097] N5: Update the velocity and position of each particle based on the individual's historical best position and the global best position, and perform feasible domain boundary verification on the updated position.

[0098] The particle velocity and position are updated in the following ways: , , in, s Number the particles. t For the number of iterations, ω For inertial weights, c 1 and c 2 represents the individual learning factor and the group learning factor, respectively. r 1,s and r 2,s It is a random vector between 0 and 1, and ⊙ represents element-wise multiplication.

[0099] For aiming points that exceed irregular candidate regions, they can be projected to the nearest feasible point in the corresponding region, or randomly generated again within the corresponding region; for regions with holes or multiple connections, the feasibility is determined using the multi-polygon point inclusion relationship.

[0100] N6: Determine whether the preset convergence condition has been met. If not, return to step N3. If it has been met, use the global optimal position as the optimal aiming point of the ammunition.

[0101] The preset convergence condition is reaching the maximum number of iterations, or the change in the optimal fitness of the population being less than the convergence threshold for several consecutive generations. For example, the maximum number of iterations can be set to 500, and the convergence threshold can be set to 10. -6 .

[0102] Particle swarm optimization (PSO) is a common algorithm in the field of machine learning. For any parts of the PSO implementation that are not mentioned, the standard settings used in machine learning can be applied to implement the algorithm.

[0103] For example, suppose K candidate schemes are selected in the discrete selection phase. Each candidate scheme corresponds to one ammunition and a shared feasible aiming area. The particle encoding contains K aiming point coordinates. Each aiming point is initialized within its corresponding area. In each iteration, the local relative coordinates of the aiming point with respect to eight targets are calculated. The single-projectile damage probability is obtained from the light, moderate, and heavy conditional damage probability matrices corresponding to the two types of ammunition, and then the multi-projectile combined probability and fitness are calculated. Optionally, a set of optional implementation parameters is: 50 particles, 200 maximum iterations, inertia weight linearly decreasing from 0.9 to 0.4, c1=c2=2.0, and the change in the population's optimal fitness is less than 10 over 20 consecutive iterations. -6Stop early. Project the aiming point that has gone out of the candidate area to the nearest feasible point.

[0104] After obtaining the optimal aiming point scheme, the damage probability can be further updated. That is, the optimal aiming point is determined under ideal hit conditions, but in actual firing, there are deviations in the point of impact. Therefore, it is necessary to further consider the impact of firing errors on the damage effect. By introducing a random point of impact offset that conforms to the characteristics of ammunition dispersion, the robustness evaluation of the optimized aiming scheme is performed to obtain the expected damage probability considering firing errors. When considering circular error... Let the aiming point of the i-th ammunition be a. i , for The horizontal and vertical errors are independent and follow a two-dimensional normal distribution with zero mean and homoscedasticity. Given a 50% circular error probability, the standard deviation is: , Let u1∈(0,1) and u2∈[0,1) be mutually independent random numbers, and the s-th random landing point is: , , For each random impact point, the probability of damage to each target is recalculated, and the average of S samples is taken as the average comprehensive probability of damage under the shooting error condition: .

[0105] Robustness indicators can also be used as damage probability quantiles, lower confidence limits, or the proportion of samples reaching a specified damage probability threshold. If the variances of the horizontal and vertical errors differ, or if there is correlation or systematic bias, the mean vector and covariance matrix can be used to describe the general two-dimensional error distribution. To reduce the computational burden caused by nested Monte Carlo methods, fixed random samples, common random numbers, offline pre-computation, parallel computation, or damage probability surrogate models can be used.

[0106] For example, the aiming point in each particle is according to Generate S random impact points. For each random impact point, recalculate the target's local relative coordinates and the probabilities of the three damage levels, and use the average comprehensive damage probability or a specified quantile as the fitness evaluation index. For example, S=1000 fixed random samples can be used, reusing the same set of standardized error samples in all particles and iterations to reduce fitness evaluation noise. If the optimization objective is to improve conservative damage effectiveness, the 5th percentile or 95% confidence lower limit of the comprehensive damage probability can be maximized. For cases with non-circular errors, different horizontal and vertical variances, or systematic biases, use a two-dimensional normal distribution or an empirical distribution obtained from measured scatter data to generate random impact points.

[0107] Compared with existing technologies, this embodiment provides a multi-target aiming point optimization method and system based on real-world regions. By constructing a missile-target damage probability matrix, it optimizes aiming points, offering strong physical interpretability and improving the scientific rigor of the aiming point results. Calculating the damage range matrix based on missile-target intersection conditions is more reasonable than traditional coverage-based optimization using a fixed radius of power, thus improving the accuracy of the optimization results. Through intersection operations of the damage matrix, the optimization area for the aiming point can be determined in advance, eliminating the need for traversing the entire target area for aiming point optimization calculations. This reduces the search area, decreases the amount of computation, and significantly improves the timeliness of the optimization.

[0108] Example 2 Another embodiment of the present invention discloses a multi-target aiming point optimization system based on a real-world region, thereby implementing the multi-target aiming point optimization method based on a real-world region described in Embodiment 1. The specific implementation of each module is described in the corresponding description in Embodiment 1. The system includes: The acquisition module acquires a set of strike missions, wherein each strike mission in the set includes a type of ammunition and a target to be struck. The calculation module calculates the feasible aiming area corresponding to each strike mission and generates a set of candidate target combinations based on the target to be struck. The candidate scheme generation module, for each candidate target combination in the candidate target combination set, obtains the strike mission corresponding to each candidate target in the candidate target combination based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is consistent with the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under that ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The target solution generation module filters the combinations of each candidate solution in the candidate solution set based on a preset cost-benefit function to obtain the target solution set; The aiming point generation module optimizes each target scheme in the target scheme set based on the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition corresponding to each target scheme.

[0109] The strike mission also includes the quantity of ammunition, the interaction conditions between the projectile and the target, and the damage level.

[0110] Since the relevant aspects of Embodiment 2 and Embodiment 1 described above can be referenced from each other, and are therefore repeated here, they will not be elaborated upon further. Because this embodiment of a multi-target aiming point optimization system based on a real region shares the same principle as the embodiment of the multi-target aiming point optimization method based on a real region described above, this system embodiment also possesses the corresponding technical effects of the aforementioned method embodiment.

[0111] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0112] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing multi-target aiming points based on real-world regions, characterized in that, The method includes: Obtain a set of strike missions, wherein each strike mission in the set includes a type of munition and a target to be struck; Calculate the feasible targeting area for each strike mission; A set of candidate target combinations is generated based on the targets to be attacked; For each candidate target combination in the candidate target combination set, the strike mission corresponding to each candidate target in the candidate target combination is obtained based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is the same as the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under the ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The combination of each candidate solution in the candidate solution set is selected based on a preset cost-benefit function to obtain the target solution set; The particle swarm optimization algorithm is used to optimize each target scheme in the target scheme set to obtain the optimal aiming point of the ammunition for each target scheme.

2. The multi-target aiming point optimization method based on a real-world region according to claim 1, characterized in that, The strike mission also includes the quantity of ammunition, the interaction conditions between the projectile and the target, and the damage level; The calculation of the feasible targeting area for each strike mission includes: Based on the damage level, obtain the relatively effective damage area of ​​the target to be hit under the ammunition type where the damage probability is not lower than a preset damage probability threshold. Then, rotate and translate the relatively effective damage area to obtain a feasible aiming area.

3. The method for optimizing multi-target aiming points based on real-world regions according to claim 2, characterized in that, The step of obtaining the relatively effective damage area of ​​the target under the specified ammunition type, based on the damage level, where the probability of damage is not lower than a preset damage probability threshold, includes: Obtain the power field data model corresponding to the ammunition type, and the target vulnerability data model corresponding to the target to be hit; Based on the power field data model, the target vulnerability data model, and the projectile-target interaction conditions, the damage probability matrix corresponding to the ammunition type is calculated, where each element value in the damage probability matrix represents a damage probability value. A binary damage matrix is ​​obtained based on the preset damage probability threshold and the damage probability matrix; After marking the connected components of the binary damage matrix, the ordered outer boundary is obtained using the Moore neighborhood boundary tracking method. The relatively effective damage area is constructed based on the ordered outer boundary.

4. The multi-target aiming point optimization method based on a real-world region according to claim 3, characterized in that, The step of generating a candidate target combination set based on the target to be attacked includes: generating the candidate target combination set using an enumeration method or a clustering method; The calculation of the shared feasible aiming area based on the feasible aiming areas of each target under this ammunition type includes: calculating the intersection of the feasible aiming areas corresponding to each target under this ammunition type, and taking the result of the intersection as the shared feasible aiming area.

5. The multi-target aiming point optimization method based on a real-world region according to claim 4, characterized in that, The preset cost-benefit function is: ， ， ， ， Where j is the j-th candidate target in the current candidate scheme combination, j=1,2,3,…,N, and N is the number of candidate targets in the current candidate scheme combination; The value weight of the j-th candidate objective. It is a binary variable. This indicates that the j-th candidate target is covered by at least one candidate scheme in the current combination of candidate schemes. This indicates that the j-th candidate target is not covered by at least one candidate scheme in the current combination of candidate schemes; The single-unit cost weight of the ammunition type h is given by [the relevant factor]. Let h be the number of ammunition of type h required in the q-th candidate scheme. The type of ammunition in the q-th candidate scheme; It is a binary variable. This indicates that the q-th candidate solution in the current candidate solution set is selected as one of the candidate solutions in the candidate solution combination. , which means not selecting the q-th candidate solution in the current candidate solution set to form a candidate solution combination; Let q be the number of candidate targets in the candidate scheme q, where q = 1, 2, 3, ..., Q, and Q is the number of candidate schemes in the combination of candidate schemes; The minimum target coverage ratio; The number of ammunition types h corresponds to the number of ammunition types h.

6. The multi-target aiming point optimization method based on a real-world region according to claim 4, characterized in that, The process of optimizing each target scheme in the target scheme set based on the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition for each target scheme includes: N1: Initialize the particle swarm. Each particle vector contains the aiming point coordinates corresponding to each target scheme, and each particle can only move within the shared feasible aiming area corresponding to its target scheme. N2: Initial velocities are randomly generated for each particle; N3: Calculate the fitness function value for each particle; N4: Update the individual's historical best position and global best position based on the fitness function value of each particle; N5: Update the velocity and position of each particle based on the individual's historical best position and the global best position, and perform feasible domain boundary verification on the updated position; N6: Determine whether the preset convergence condition has been met. If not, return to step N3. If it has been met, use the global optimal position as the optimal aiming point of the ammunition.

7. The method for optimizing multi-target aiming points based on a real-world region according to claim 6, characterized in that, The fitness function in the particle swarm optimization algorithm is: ， ， ， ， in, Let the target weight be the target weight corresponding to the j-th candidate target. Let J be the overall damage probability corresponding to the j-th candidate target. , , For the weight function, Penalties for violating aiming point constraints As a penalty for incidental damage; Let K be the shortest distance from the kth aiming point to the corresponding feasible aiming area, where k = 1, 2, 3, ..., K, and K is the total number of aiming points. Indicates a non-target area. This indicates that the damage level is reached at the aiming point (x, y). The probability of 100% or higher.

8. The method for optimizing multi-target aiming points based on a real-world region according to claim 7, characterized in that, The overall damage probability corresponding to the j-th candidate target is calculated in the following manner: Obtain the target schemes containing the j-th candidate target and construct a target strike scheme set; For each target strike scheme in the set of target strike schemes, calculate the single target destruction probability corresponding to that target strike scheme; The overall damage probability is calculated based on the damage probabilities of all individual targets.

9. A multi-target aiming point optimization system based on a real-world region, characterized in that, The system includes: The acquisition module acquires a set of strike missions, wherein each strike mission in the set includes a type of ammunition and a target to be struck. The calculation module calculates the feasible aiming area corresponding to each strike mission and generates a set of candidate target combinations based on the target to be struck. The candidate scheme generation module, for each candidate target combination in the candidate target combination set, obtains the strike mission corresponding to each candidate target in the candidate target combination based on the strike mission set to form a candidate strike mission set. If the number of the same ammunition type in the candidate strike mission set is consistent with the number of candidate targets in the candidate target combination, then a common feasible aiming area is calculated based on the feasible aiming area of ​​each target under that ammunition type. If the common feasible aiming area exists, then the ammunition type, the target combination, and the common feasible aiming area are taken as a candidate scheme in the candidate scheme set. The target solution generation module filters the combinations of each candidate solution in the candidate solution set based on a preset cost-benefit function to obtain the target solution set; The aiming point generation module optimizes each target scheme in the target scheme set based on the particle swarm optimization algorithm to obtain the optimal aiming point of the ammunition corresponding to each target scheme.

10. A multi-target aiming point optimization system based on a real-world region according to claim 9, characterized in that, The strike mission also includes the quantity of ammunition, the interaction conditions between the projectile and the target, and the damage level.

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