CN114330862A - Air defense weapon combat deployment algorithm - Google Patents
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Abstract
The invention provides an air defense weapon combat deployment algorithm, which comprises the following steps: firstly, constructing a loss function; determining a geographic model; determining a deployment model; and solving the deployment model. According to the method, constraint conditions such as battlefield environment, deployment position and shielding capability of the air defense weapon can be comprehensively considered, the optimal deployment scheme is obtained as a target, the air defense weapon deployment optimization model is given, the purpose of comprehensive and maximum shielding on multiple places is achieved, the optimal solution is solved for the model, the air defense weapon combat deployment scheme is obtained, an effective air defense weapon deployment display diagram is automatically given, the method is beneficial to rapidly providing the initial optimal deployment scheme of the air defense weapon system for a commander, and the manual operation error probability is reduced.
Description
Technical Field
The invention relates to an air defense weapon combat deployment algorithm, and belongs to the field of air defense back guidance command control technology.
Background
In modern air defense combat, the aim of air defense weapon combat deployment is to create a combat situation which is beneficial to air defense and air attack under certain combat conditions, so as to form an optimized firepower structure and a detection structure in the combat, and occupy certain space-time advantages, thereby fully playing the advantages and the advantages of air defense firepower, restraining the power of air attack weapons and achieving the aim of protecting the public. The good deployment situation can enable air defense weaponry with different performances to exert synergistic advantages under systematic countermeasures. Therefore, as an important link for planning ahead of war, the reasonable deployment of the air defense weapon is particularly important. At present, the air defense weapon combat deployment is generally manually configured initially based on experience, is evaluated and verified through simulation deduction, is optimized through repeated iterative adjustment, and is long in overall time consumption.
Disclosure of Invention
In order to solve the technical problems, the invention provides an air defense weapon combat deployment algorithm which can comprehensively consider the constraint conditions of battlefield environment, deployment position, shielding capability and the like of an air defense weapon, so as to obtain an optimal deployment scheme as a target and provide an air defense weapon deployment optimization model to realize the aim of comprehensive maximum shielding of multiple areas.
The invention is realized by the following technical scheme.
The invention provides an air defense weapon combat deployment algorithm, which comprises the following steps:
firstly, constructing a loss function: constructing a shielding loss function of the air defense weapon to the guard ground according to the shielding range of the air defense weapon to the guard ground;
determining a geographic model: constructing a geographic space model, wherein the geographic space model distinguishes a deployable area and a non-deployable area based on grids;
determining a deployment model: the method comprises the steps of establishing an air defense weapon deployment optimization model by taking a geographic space model as a constraint and minimizing the calculated value of a shield loss function of a plurality of air defense weapons on a guard ground as a target;
solving a deployment model: and solving the air defense weapon deployment optimization model by adopting a genetic algorithm or a particle swarm algorithm to obtain an optimal deployment scheme.
The protective ground shielding range of the air defense weapon is calculated according to the performance parameters of the air defense weapon, and the performance parameters comprise the radius r of a projectile ringtfDistance r between air defense weapon and ground centerbMaximum radius of killing RmaxMaximum airway shortcut Pmax。
The air defense weapon has a constraint condition on the shielding range of the defense place, and the constraint condition is calculated by adopting the following formula:
wherein the air attack azimuth angle alpha belongs to (-pi, pi)]The included angle between the airborne direction and the datum line is adopted, and the airway shortcut P is the shortest distance from the ground air defense weapon to the horizontal plane projection of the airborne airway; shield depth RnThe distance of the aerial fire protection ring passing through the ground before the carrier reaches the projectile ring is designated;
to cover the area.
The protective shield of the air defence weapon takes the following form:
wherein,
respectively, a long-range comprehensive shield value vector, a medium-range comprehensive shield value vector and a short-range comprehensive shield value vector, Loss (YH)i,j) K is the maximum index after discretizing the 360 DEG direction of the defending ground for the loss function of the weapon i to be deployed to the ground j.
The protective coverage of the air defence weapon is in the following form when the number of the protective areas is more than one:
wherein, M is the number of the above,
function of the shield loss of m for all air weapons pairs, ηmWeighted by importance of key m, LossYH_sumAll the necessary shield loss functions for all air weapons.
The geospatial model is expanded in two dimensions and is divided according to grids.
In the geospatial model, deployable areas and undeployable areas are identified by grids.
In the fourth step, a genetic algorithm is adopted, and the calculation and solving are carried out according to the following steps:
A. randomly generating a population, determining individual fitness, and determining the cumulative probability of an individual by using a roulette strategy;
B. generating a new individual according to a certain mutation probability and a mutation method, judging whether the individual is illegally solved, if so, carrying out mutation or crossover again, and if not, continuing;
C. and judging whether the optimization criterion is met, and if so, outputting the optimal individual and the optimal solution thereof.
The invention has the beneficial effects that: constraint conditions such as battlefield environment, deployment position and shielding capability of the air defense weapon can be comprehensively considered, the optimal deployment scheme is obtained as a target, an air defense weapon deployment optimization model is given, the purpose of achieving comprehensive and omnibearing maximum shielding on multiple places is achieved, the optimal solution is solved for the model, the air defense weapon combat deployment scheme is obtained, an effective air defense weapon deployment display diagram is automatically given, the air defense weapon deployment display diagram is beneficial to rapidly providing the initial optimal deployment scheme of the air defense weapon system for a commander, and the manual operation error probability is reduced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating a simulation scenario environment model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing the result of weapon configuration scheme 1 of the present invention;
FIG. 4 is a schematic representation of the weapon configuration scheme 2 results of the present invention;
FIG. 5 is a schematic representation of the weapon configuration scheme 3 results of the present invention;
FIG. 6 is a schematic representation of the weapon configuration scheme 4 results of the present invention;
FIG. 7 is a schematic representation of the result of weapon configuration scheme 5 of the present invention;
fig. 8 is a schematic representation of the weapon configuration scheme 6 results of the present invention.
Detailed Description
The technical solution of the present invention is further described below, but the scope of the claimed invention is not limited to the described.
Example 1
An air defense weapon combat deployment algorithm as shown in fig. 1-8 comprises the following steps:
firstly, constructing a loss function: constructing a shielding loss function of the air defense weapon to the guard ground according to the shielding range of the air defense weapon to the guard ground;
determining a geographic model: constructing a geographic space model, wherein the geographic space model distinguishes a deployable area and a non-deployable area based on grids;
determining a deployment model: the method comprises the steps of establishing an air defense weapon deployment optimization model by taking a geographic space model as a constraint and minimizing the calculated value of a shield loss function of a plurality of air defense weapons on a guard ground as a target;
solving a deployment model: and solving the air defense weapon deployment optimization model by adopting a genetic algorithm or a particle swarm algorithm to obtain an optimal deployment scheme.
Example 2
Based on the embodiment 1, the protective ground shielding range of the air defense weapon is calculated according to the performance parameters of the air defense weapon, and the performance parameters comprise the radius r of a projectile ringtfDistance r between air defense weapon and ground centerbMaximum radius of killing RmaxMaximum airway shortcut Pmax。
Example 3
Based on example 2, the air defense weapon has a constraint condition on the shield range of the guard, and the constraint condition is calculated by adopting the following formula:
wherein the air attack azimuth angle alpha belongs to (-pi, pi)]The included angle between the airborne direction and the datum line is adopted, and the airway shortcut P is the shortest distance from the ground air defense weapon to the horizontal plane projection of the airborne airway; shield depth RnThe distance of the aerial fire protection ring passing through the ground before the carrier reaches the projectile ring is designated;
to cover the area.
Example 4
Based on example 1, the protective coverage of the air defence weapon is of the following form:
wherein,
respectively, a long-range comprehensive shield value vector, a medium-range comprehensive shield value vector and a short-range comprehensive shield value vector, Loss (YH)i,j) K is the maximum index after discretizing the 360 DEG direction of the defending ground for the loss function of the weapon i to be deployed to the ground j.
Example 5
Based on example 4, the protective coverage of the air defence weapon on the guard is in the following form when the number of guard areas is multiple:
wherein, M is the number of the above,
function of the shield loss of m for all air weapons pairs, ηmWeighted by importance of key m, LossYH_sumAll the necessary shield loss functions for all air weapons.
Example 6
Based on example 1, the geospatial model is two-dimensionally expanded and gridded.
Example 7
Based on embodiment 6, in the geospatial model, both deployable and undeployable areas are identified by grids.
Example 8
Based on the embodiment 1, in the step (iv), the genetic algorithm is adopted to carry out calculation and solution by the following steps:
A. randomly generating a population, determining individual fitness, and determining the cumulative probability of an individual by using a roulette strategy;
B. generating a new individual according to a certain mutation probability and a mutation method, judging whether the individual is illegally solved, if so, carrying out mutation or crossover again, and if not, continuing;
C. and judging whether the optimization criterion is met, and if so, outputting the optimal individual and the optimal solution thereof.
Example 9
Based on the above embodiment, the following steps are specifically adopted:
step S101, determining and quantifying the shielding range of the air defense weapon on the guard land, designing a shielding loss function of the air defense weapon on a certain important place, and further acquiring a shielding loss function of multiple important places;
step S102, determining a geographic environment model, and giving an environment model and a constraint environment thereof;
step S103, determining an air defense weapon deployment optimization model, solving the air defense weapon deployment model by adopting a genetic algorithm based on a grid map, and automatically generating an optimal deployment display diagram under the current weapon configuration condition by setting the number of weapon configurations of various types and an environment model.
Further, determining the protective coverage of the air defense weapon comprises:
(1) when R ismax>rtf、0≤rb≤rtf-RmaxWhen the temperature of the water is higher than the set temperature,
(2) when R ismax>rtf、rb>rtf-RmaxAnd theta isp＝θjOr thetap＝π-θjWhen the temperature of the water is higher than the set temperature,
(3) when R ismax>rtf、0≤rb≤Rmax-rtfWhen the temperature of the water is higher than the set temperature,
(4) when R ismax>rtf、rb>Rmax-rtf、rb≤PmaxWhen the temperature of the water is higher than the set temperature,
(5) when R ismax>rtf、rb>Rmax-rtf、
When the temperature of the water is higher than the set temperature,
wherein,
rtfis the radius of the throwing ring rbDistance between air defense weapon and ground center, RmaxIs the maximum killing radius, PmaxIs the maximum airway shortcut.
Discretizing the 360-degree direction of the ground to 360 degrees, namely K is 360, and calculating a basic safety vector
The calculation method comprises the following steps:
wherein c is corresponding to the direction of attack
A subscript of (T), (k) is
The distance from subscript to c is in the range of 0, K/2]The value before discretization is theta and the range is 0,180 DEG](ii) a up, low, a and b are all settable parameters, up is the upper limit of the basic safety degree, and low is the lower limit of the basic safety degree.
Further, according to r in the shield rangetfEstablishing a defense radius for long-range, medium-range and short-range air defense weapons
And separately calculate
Wherein YHi,jCalculating a remote shield value vector of each place for the shield vector of the weapon i to the place j
Medium range mask value vector
Short range mask value vector
The calculation formula is as follows:
wherein e is1、e2∈[0,1]Are parameters that can be set in the algorithm,
in order to take account of the degree of security on the basis,
the method refers to a shield value vector synthesis operation, and the calculation principle is as follows:
considering the above factors together, the loss function for establishing weapon i to be deployed to aim j is designed as follows:
considered remote shield value vector
Medium range mask value vector
Short range mask value vector
The mask loss function for dug j is:
further, in consideration of the actual scene that the air defense weapon needs to cover a plurality of areas, the most important shielding loss function is designed as follows:
wherein, M is the number of the above,
function of the shield loss of m for all air weapons pairs, ηmWeighted by importance of key m, LossYH_sumAll the necessary shield loss functions for all air weapons.
Further, a geographic environment model is determined, and a grid quantification method is adopted to quantify the deployment environment of the air defense weapon, and the method comprises the following steps: the environment quantization is expanded in a two-dimensional space, and the defense area is divided into n multiplied by n discrete areas by using a grid formed by horizontal lines and vertical lines; each grid unit is a deployed basic unit, namely, the ground weapon and the air defense weapon are both positioned on a certain grid; undeployable areas such as rivers, elevations and the like exist in the environment model.
Further, aiming at the deployment condition I, determining an air defense weapon deployment optimization model, and establishing a target optimization model as follows:
an objective function:
constraint conditions are as follows: s.t. different air defense weapons may not be in the same grid cell; air weapons are undeployable in undeployable areas.
Solving the air defense weapon deployment optimization model by adopting a genetic algorithm based on a grid map, and automatically generating an optimal deployment display diagram under the current weapon configuration condition by setting the number of weapon configurations of various models and an environment model. The algorithm flow is as follows:
(1) encoding the deployment position of the air defense weapon by using the coordinates;
(2) randomly generating population according to coding method, and determining individual fitness
fitness(I)＝-LossYH_sum(I)-mini(-LossYH_sum(Ii))
(3) Determining cumulative probabilities of individuals using roulette strategy
(4) Generating new individuals according to the position uniform crossing and neighbor mutation method, judging whether the new individuals are illegally solved, if so, carrying out mutation or crossing again, and if not, continuing;
(5) evaluating fitness of all chromosomes in the population, and updating the optimal chromosomes;
(6) and judging whether the termination criterion is met, and if so, outputting the optimal individual and the optimal solution.
Example 10
Based on example 9, the specific steps are as follows:
(1) determining and quantifying the shielding range of the air defense weapon on the guard land, designing a shielding loss function of the air defense weapon on a certain important land, and further acquiring a shielding loss function of multiple areas;
(2) determining a geographic environment model;
(3) determining an air defense weapon deployment optimization model based on the multiple ground loss function in the step (1);
(4) and (3) solving the determined air defense weapon deployment optimization model determined in the step (3) under the condition of the environment model determined in the step (2) by using a grid map-based genetic algorithm.
A 70 × 70 mesh is given as a simulation scene in which the starting point of the incoming direction is (70,70), i.e., the incoming direction of each destination is the direction from the point to the destination. The simulation scenario includes two main grounds, whose positions are (33,32), (21,37), respectively.
The common parameter settings for both algorithms are as follows: orientation discretization degree: 360, namely discretizing 360-degree azimuth of the intended ground into 360 parts; defense radius: the distance is 200km, the middle range is 100km, and the short range is 40 km; up 0.6, low 0.1, a 70, b 10, e1＝0.3，e 20, pxd, 0.7; the population number is as follows: 1500; cross probability: 0.9; the mutation probability: 0.9; maximum algebra: 200 of a carrier; maximum number of stalled generations (termination condition): the algorithm is terminated 20, i.e. when the objective function is stalled for 20 generations.
The weapon performance is:
type 1 (distance): the maximum route shortcut is 130km, the maximum firepower range is 150km, the weapon efficiency is 0.8, and the electromagnetic compatibility distance is 1 km;
type 2 (medium): the maximum navigation route shortcut is 70km, the maximum firepower range is 90km, the weapon efficiency is 0.8, and the electromagnetic compatibility distance is 1 km;
type 3 (near): the maximum air route shortcut is 40km, the maximum firepower range is 50km, the weapon efficiency is 0.8, and the electromagnetic compatibility distance is 1 km.
As a specific example, given a number of weapon configurations established, it includes:
weapon configuration 1: type 1 (distal) 1, type 2 (middle) 6, type 3 (proximal) 6;
weapon configuration 2: type 1 (distal) 2, type 2 (middle) 6, type 3 (proximal) 6;
weapon configuration 3: type 1 (distal) 2, type 2 (middle) 5, type 3 (proximal) 6;
weapon configuration 4: type 1 (distal) 2, type 2 (middle) 7, type 3 (proximal) 6;
weapon configuration 5: type 1 (distal) 2, type 2 (middle) 6, type 3 (proximal) 5; weapon configuration 6: type 1 (distal) 2, type 2 (middle) 6, type 3 (proximal) 7.
Claims (8)
1. An air defense weapon combat deployment algorithm, which is characterized in that: the method comprises the following steps:
firstly, constructing a loss function: constructing a shielding loss function of the air defense weapon to the guard ground according to the shielding range of the air defense weapon to the guard ground;
determining a geographic model: constructing a geographic space model, wherein the geographic space model distinguishes a deployable area and a non-deployable area based on grids;
determining a deployment model: the method comprises the steps of establishing an air defense weapon deployment optimization model by taking a geographic space model as a constraint and minimizing the calculated value of a shield loss function of a plurality of air defense weapons on a guard ground as a target;
solving a deployment model: and solving the air defense weapon deployment optimization model by adopting a genetic algorithm or a particle swarm algorithm to obtain an optimal deployment scheme.
2. The air defense weapons combat deployment algorithm of claim 1 wherein: the air defence weapon being based on the extent of protection of the ground of defenceCalculating performance parameters of the empty weapon, wherein the performance parameters comprise the radius r of a projectile ringtfDistance r between air defense weapon and ground centerbMaximum radius of killing RmaxMaximum airway shortcut Pmax。
3. The air defense weapons combat deployment algorithm of claim 2 wherein: the air defense weapon has a constraint condition on the shielding range of the defense place, and the constraint condition is calculated by adopting the following formula:
wherein the air attack azimuth angle alpha belongs to (-pi, pi)]The included angle between the airborne direction and the datum line is adopted, and the airway shortcut P is the shortest distance from the ground air defense weapon to the horizontal plane projection of the airborne airway; shield depth RnThe distance of the aerial fire protection ring passing through the ground before the carrier reaches the projectile ring is designated;
to cover the area.
4. The air defense weapons combat deployment algorithm of claim 1 wherein: the protective shield of the air defence weapon takes the following form:
wherein,
respectively a long-range comprehensive shield value vector, a medium-range comprehensive shield value vector and a short rangeComprehensive mask value vector, Loss (YH)i,j) K is the maximum index after discretizing the 360 DEG direction of the defending ground for the loss function of the weapon i to be deployed to the ground j.
5. The air defense weapons combat deployment algorithm of claim 4 wherein: the protective coverage of the air defence weapon is in the following form when the number of the protective areas is more than one:
wherein, M is the number of the above,
function of the shield loss of m for all air weapons pairs, ηmWeighted by importance of key m, LossYH_sumAll the necessary shield loss functions for all air weapons.
6. The air defense weapons combat deployment algorithm of claim 1 wherein: the geospatial model is expanded in two dimensions and is divided according to grids.
7. The air defense weapons combat deployment algorithm of claim 6 wherein: in the geospatial model, deployable areas and undeployable areas are identified by grids.
8. The air defense weapons combat deployment algorithm of claim 1 wherein: in the fourth step, a genetic algorithm is adopted, and the calculation and solving are carried out according to the following steps:
A. randomly generating a population, determining individual fitness, and determining the cumulative probability of an individual by using a roulette strategy;
B. generating a new individual according to a certain mutation probability and a mutation method, judging whether the individual is illegally solved, if so, carrying out mutation or crossover again, and if not, continuing;
C. and judging whether the optimization criterion is met, and if so, outputting the optimal individual and the optimal solution thereof.
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Inventor after: Wang Yuqian Inventor after: Gao Jian Inventor after: Hu Rongpu Inventor after: Wu Changhan Inventor after: Yang Hang Inventor after: Liu Li Inventor after: Wu Jianshe Inventor before: Wang Yuqian Inventor before: Gao Jian Inventor before: Hu Rongpu Inventor before: Wu Changhan Inventor before: Yang Hang Inventor before: Liu Li Inventor before: Wu Jianshe Inventor before: Wu Jing |