A method and device for calculating the parameters of a guided shell, equipment and medium

Abstract

The present application relates to the technical field of guided shells, and discloses a method, device, equipment and medium for calculating the parameters of a guided shell, the method comprising: constructing an initial particle group and performing initialization processing on the initial particle group, wherein the initial particle group comprises at least one initial particle, and the initial particle comprises at least one variable in a launch pitch angle and an attack angle; obtaining initial particle information of the initial particle group, wherein the initial particle information comprises speed information and position information of a plurality of initial particles; substituting the initial particle information into a trajectory model and calculating a target function value of each initial particle in the initial particle group; the target function value is used to represent an entry terminal guidance time of the initial particle after moving according to the trajectory model; and based on the target function value of each initial particle, a target particle is determined to obtain parameter information of the target particle. The present application can optimize the entry terminal guidance time of the guided shell.

Description

Technical Field

[0001] The present invention relates to the technical field of guided artillery shells, and in particular to a method, device, equipment and medium for calculating various parameters of guided artillery shells. Background Art

[0002] Guided artillery shells use their own guidance devices to control and guide them at the end of their trajectory after launch, and have high strike accuracy.

[0003] Laser terminal-guided artillery shells offer advantages such as high-precision strikes and strong anti-interference capabilities. Related technologies primarily optimize the field of view of the artillery shell's seeker, but fail to fully consider the time it takes for the guided artillery shell to enter terminal guidance. This results in a long terminal guidance time, delayed target acquisition by the laser seeker, and a weakened ability to strike moving targets.

[0004] In view of this, a method for calculating the parameters of a guided projectile that can optimize the time to enter terminal guidance is needed. Summary of the Invention

[0005] In view of this, the present invention provides a method for calculating various parameters of a guided artillery shell to optimize the time when the guided artillery shell enters terminal guidance.

[0006] In a first aspect, the present invention provides a method for calculating various parameters of a guided artillery shell, the method comprising: constructing an initial particle group and initializing the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of a launch pitch angle and an angle of attack; obtaining initial particle information of the initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles; substituting the initial particle information into a ballistic model, and calculating the objective function value of each initial particle in the initial particle group; the objective function value is used to characterize the time of the initial particle entering the terminal guidance after moving according to the ballistic model; and determining the target particle based on the objective function value of each initial particle to obtain various parameter information of the target particle.

[0007] In this embodiment, the stability of the method can be improved by initializing the initial particles. According to the objective function value of each initial particle, the target particles and the corresponding information can be obtained, which can improve the efficiency of the method, obtain the information corresponding to the target particles with the optimal final guidance time, and improve the performance of the guided projectile.

[0008] In an optional embodiment, constructing an initial particle population and initializing the initial particle population includes: obtaining the initial particle population; obtaining initial mapping parameters, wherein the initial mapping parameters include a first initial value and a first control parameter, the first initial value is the position of the individual particle, and the first control parameter is a parameter for controlling the degree of chaos; performing chaotic mapping on the initial mapping parameters to generate a chaotic sequence, wherein the chaotic sequence includes several chaotic sequence values; and assigning the chaotic sequence values ​​to the initial particles in the initial particle population to determine the velocity information and position information of the initial particles.

[0009] In this embodiment, the initial particles are initialized by using a chaotic mapping method, which can improve the population diversity of the initial particle group.

[0010] In an optional embodiment, substituting the initial particle information into the ballistic model to obtain the objective function value of each initial particle includes: substituting the initial particle information into the ballistic model to obtain the ballistic parameter information of each initial particle; inputting the ballistic parameter information into the seeker capture model to determine the time when the initial particle enters the terminal guidance; and obtaining the objective function value of the initial particle based on the ballistic parameter information and the time when the initial particle enters the terminal guidance.

[0011] In this embodiment, the objective function value of the particle is obtained through the ballistic parameter information of the initial particle and the time to enter the terminal guidance, so that the initial particle can be quantitatively evaluated.

[0012] In an optional embodiment, obtaining the objective function value of the initial particle based on the ballistic parameter information and the time to enter terminal guidance includes: determining the miss distance of the guided projectile based on the ballistic parameter information; constructing a target sequence including the miss distance and the time to enter terminal guidance, and obtaining a weight coefficient matching the target sequence; and obtaining the objective function value of the initial particle based on the target sequence and the weight coefficient.

[0013] In this embodiment, by setting a target sequence and a weight coefficient that matches the target sequence to obtain an objective function value, the flexibility of the algorithm can be improved.

[0014] In an optional embodiment, the target particle is determined based on the objective function value of each initial particle, including: calculating the particle fitness of each initial particle based on the objective function value of each initial particle; determining the initial particle with the best particle fitness as a candidate particle, and adjusting the velocity parameter and position parameter of the candidate particle according to preset boundary conditions; if the adjusted candidate particle meets the termination condition, determining the candidate particle as the target particle.

[0015] In this embodiment, the particle fitness of each initial particle is determined by the objective function value, and the initial particle with the best particle fitness is determined as the candidate particle, which can improve the efficiency of particle search. The parameters of the candidate particle are adjusted according to the preset boundary conditions, which can improve the stability of the method.

[0016] In an optional embodiment, the method further includes: if the adjusted candidate particle does not meet the termination condition, determining the particle fitness of the candidate particle and the average particle fitness of the initial particle population; obtaining a fitness ratio of the particle fitness of the candidate particle to the average particle fitness; and updating the parameters of each initial particle based on the fitness ratio to recalculate the objective function value of each initial particle after the updated parameters.

[0017] In this embodiment, by obtaining the ratio of the particle fitness of the candidate particle to the average particle fitness of the initial particle group to update the initial particle parameters, the consumption of computing resources can be reduced and the flexibility of the algorithm can be improved.

[0018] In an optional embodiment, before substituting the initial particle information into the ballistic model, it also includes: obtaining ballistic forces, wherein the ballistic forces include at least one of gravity, aerodynamics, and launch thrust; based on the ballistic forces, performing ballistic analysis on the guided artillery projectile to construct a ballistic model, wherein the ballistic model includes at least one of the artillery projectile dynamics equation, the artillery projectile kinematics equation, and the artillery projectile mass change equation.

[0019] In this embodiment, by performing trajectory analysis on the guided projectile and constructing a trajectory model, the guided projectile can be predicted, thereby improving the accuracy of subsequent particle evaluation.

[0020] In a second aspect, the present invention provides a device for calculating various parameters of a guided artillery shell, the device comprising: an initial particle group construction module, for constructing an initial particle group and performing initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of the launch pitch angle and the angle of attack; an initial particle information acquisition module, for acquiring the initial particle information of the initial particle group, wherein the initial particle information includes speed information and position information of several initial particles; an objective function value acquisition module, for substituting the initial particle information into a ballistic model and calculating the objective function value of each initial particle in the initial particle group; the objective function value is used to characterize the time when the initial particle enters the terminal guidance after moving according to the ballistic model; and an information acquisition module for determining the target particle based on the objective function value of each initial particle to obtain the information of the target particle.

[0021] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the method for calculating various parameters of a guided artillery projectile according to the first aspect or any corresponding embodiment thereof.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for calculating various parameters of a guided artillery projectile according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 1 is a flow chart of a method for calculating various parameters of a guided projectile according to an embodiment of the present invention;

[0025] Figure 2 is a flow chart of another method for calculating parameters of a guided projectile according to an embodiment of the present invention;

[0026] Figure 3 is a chaotic sequence distribution diagram according to an embodiment of the present invention;

[0027] Figure 4 is a dynamic inertia weight map according to an embodiment of the present invention;

[0028] Figure 5 is a flow chart of another method for calculating parameters of a guided projectile according to an embodiment of the present invention;

[0029] Figure 6 is a seeker capture domain diagram according to an embodiment of the present invention;

[0030] Figure 7 is a flow chart of another method for calculating parameters of a guided projectile according to an embodiment of the present invention;

[0031] Figure 8 is a structural block diagram of another device according to an embodiment of the present invention;

[0032] Figure 9 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0034] This invention provides a method for calculating various parameters for guided projectiles, optimizing the time to terminal guidance as one of the optimization objectives to improve the ability to strike moving targets. Through group initialization, weight optimization, and adaptive parameter information adjustment, the performance and efficiency of various parameters are improved.

[0035] According to an embodiment of the present invention, an embodiment of a method for calculating various parameters of a guided artillery shell is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] In this embodiment, a method for calculating various parameters of a guided artillery shell is provided. Figure 1 Flowchart of the method for calculating the various parameters of the guided projectile according to the embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0037] Step S101 : constructing an initial particle group and performing initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of a launch pitch angle and an angle of attack.

[0038] The construction method of the initial particle swarm can be determined based on actual conditions. The initial positions and initial velocities of the particles are randomly generated within a feasible domain, where the feasible domain can be determined based on the constraints. There is no limit on the number of initial particles in the initial particle swarm, nor is there a limit on the dimensionality of each initial particle.

[0039] The initialization method is not limited and may be random initialization, uniform distribution initialization, etc. According to the actual situation, a suitable initialization method may be selected to initialize the initial particles in the initial particle group.

[0040] In a practical application, the initial particle includes D variables, where the D variables are the launch pitch angle and D-1 attack angles, which can be expressed as follows:

[0041]

[0042] in, is the initial particle, is the first angle of attack, is the second angle of attack, is the D-1th angle of attack, is the launch pitch angle. The number of variables can be determined by the staff according to the actual situation.

[0043] Step S102 : obtaining initial particle information of an initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles.

[0044] Among them, the initial particle information can be directly obtained based on the initial particles.

[0045] Step S103 , substituting the initial particle information into the trajectory model, and calculating the objective function value of each initial particle in the initial particle group; the objective function value is used to characterize the time of the initial particle entering the terminal guidance after moving according to the trajectory model.

[0046] The trajectory model can be a linear time-varying model for establishing the terminal guidance process of a guided projectile. Using this linear time-varying model, the time at which the initial particle enters the terminal guidance phase after following the trajectory model is determined. The trajectory model can be constructed using relevant software. The objective function value can be determined based on actual conditions, and based on these features, the corresponding eigenvalues ​​of the initial particles are extracted. These eigenvalues ​​are then fused to obtain the final objective function value. The data fusion method is not limited and can include weighted fusion, for example.

[0047] In a practical application, it is necessary to optimize the terminal guidance time and accuracy of the guided projectile. The objective function can be expressed as follows:

[0048]

[0049] in, is the objective function value, is the first weight coefficient, is the target location, For the point where the shells landed, is the second weight coefficient, The first weight coefficient and the second weight coefficient can be determined by the staff according to the actual situation.

[0050] Step S104: determining target particles based on the objective function values ​​of the initial particles to obtain various element information of the target particles.

[0051] By comparing the objective function values ​​of each initial particle and selecting the one with the smallest objective function value as the target particle, the target particle information is substituted back into the trajectory model to obtain the corresponding parameters. These parameters include basic parameters and correction parameters. Basic parameters include the launch pitch angle of the guided projectile and the laser power-on time. The laser power-on time can be calculated by entering the terminal guidance time. Correction parameters include the maximum trajectory height, landing angle, landing speed, and the guided projectile's flight time.

[0052] In some optional embodiments, after obtaining an initial particle with a small objective function value, it is necessary to determine whether the initial particle information of the initial particle is within the feasible region. If it is not within the feasible region, it is necessary to update the initial particle information of the initial particle and recalculate the objective function value.

[0053] In some optional embodiments, after obtaining the objective function value of the initial particle, the individual optimal value and the global optimal value can be determined based on its objective function value. The velocity and position information of the current particle are updated based on parameters such as the inertia weight, the individual optimal value, and the global optimal value. The objective function value of each particle is then compared with the individual historical optimal objective function value. If the objective function value of the current particle is smaller, the individual optimal value is updated. The minimum objective function value of the current particle is obtained and compared with the global historical minimum objective function value. If the current objective function value is smaller, the global optimal value is updated. The steps of updating the particle's velocity and position and updating the individual optimal value and the global optimal value are repeated until a termination condition is met. The termination condition can be reaching the maximum number of iterations or finding an optimal solution that meets the requirements.

[0054] Among them, the velocity and position of the particle can be updated according to the following formula:

[0055]

[0056]

[0057] in, is the velocity of the dth variable of the i-th particle at the k+1th iteration, is the inertia weight, is the velocity of the dth variable of the i-th particle at the k-th iteration, is the first learning factor, is the second learning factor, are random numbers that are independently and uniformly distributed in the interval from zero to one, are random numbers that are independently and uniformly distributed in the interval from zero to one, is the current local optimal value of a single particle, is the current global optimal value of a single particle, is the position of the dth variable of the ith particle at the k+1th iteration, is the position of the d-th variable of the ith particle at the k-th iteration.

[0058] The number of particle variables can be determined when constructing the initial particle population. The number of iterations can be determined by the staff. The first learning factor and the second learning factor can be directly determined by the staff.

[0059] The method for calculating the various parameters of a guided projectile provided in this embodiment can improve the stability of the method by initializing the initial particles. The target particles and corresponding various parameter information are obtained based on the objective function value of each initial particle, thereby improving the efficiency of the method, obtaining the various parameter information corresponding to the target particles with the optimal final guidance time, and improving the performance of the guided projectile.

[0060] In this embodiment, a method for calculating various parameters of a guided artillery shell is provided. Figure 2 Flowchart of the method for calculating the various parameters of the guided projectile according to the embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0061] Step S201: construct an initial particle group and perform initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of the launch pitch angle and the attack angle.

[0062] Specifically, the above step S201 includes:

[0063] Step S2011, obtaining an initial particle group.

[0064] In the initial particle swarm, a target number of initial particles can be randomly generated in the search space according to the variables to be optimized to construct the initial particle swarm. The search space can be determined according to the value range of the variables to be optimized, and the target number can be determined according to the number of variables to be optimized.

[0065] Step S2012: obtaining initial mapping parameters, wherein the initial mapping parameters include a first initial value and a first control parameter, the first initial value is the position of the individual particle, and the first control parameter is a parameter for controlling the degree of chaos.

[0066] When randomly generating initial particles in the search space, a first initial value of the initial particles can be directly obtained. The first initial value is within the range of the search space. The first control parameter can be selected as an appropriate value based on actual conditions. In a practical application, several sets of chaos degree control parameters are selected and experiments are conducted. Based on the experimental results, one set of the several sets of chaos degree control parameters is selected as the first control parameter.

[0067] Step S2013: Perform chaotic mapping on the initial mapping parameters to generate a chaotic sequence, wherein the chaotic sequence includes a plurality of chaotic sequence values.

[0068] The chaotic map can be a Circle map, a Tent map, or other types. An initial particle is then selected from the initial particle population as the first input of the chaotic map. The initial particle information of the initial particle is then iterated to generate a series of initial particle information. Each iteration uses the result of the previous iteration as the input for the next. During the iteration process, the generated initial particle information is collected to form a chaotic sequence.

[0069] In a practical application, Circle mapping is selected and the expression of chaotic sequence is shown as follows:

[0070]

[0071] in, is the i-th iteration value of the chaotic map, a is the first control parameter, and b is the second control parameter. The first control parameter and the second control parameter can be determined by the staff according to the actual situation. In a practical application, a is 0.2, b is The chaotic sequence generated by 1000 iterations of Circle mapping is as follows: Figure 3 shown.

[0072] Step S2014: assigning the chaotic sequence value to the initial particles in the initial particle group to determine the velocity information and position information of the initial particles.

[0073] After obtaining several groups of chaotic sequence values, the chaotic sequence values ​​can be randomly assigned to other initial particles in the initial particle group, and the velocity information and position information of the initial particles in the initial particle group can be updated according to the chaotic sequence values.

[0074] Step S202 : obtaining initial particle information of the initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles.

[0075] For details, please see Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0076] Step S203 , substituting the initial particle information into the trajectory model, and calculating the objective function value of each initial particle in the initial particle group; the objective function value is used to characterize the time of the initial particle entering the terminal guidance after moving according to the trajectory model.

[0077] For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0078] Step S204: determining target particles based on the objective function values ​​of the initial particles to obtain various element information of the target particles.

[0079] For details, please see Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0080] In some optional embodiments, the target particles are determined by constructing a nonlinear decreasing inertia weight model.

[0081] The inertia weight model can be constructed using a nonlinear function, where the nonlinear function can be an exponential function, etc. Then, an initial function is set to determine algorithm-related parameters such as the initial maximum inertia weight, where the parameters can also include a deceleration rate, a search space range, etc.

[0082] The inertia weight model can be expressed as follows:

[0083]

[0084] in, is the current inertia weight, is the minimum inertia weight, is the maximum inertia weight, is the minimum number of iterations, is the maximum number of iterations, is the expansion constant of the curve. The minimum inertia weight, maximum inertia weight, minimum number of iterations, maximum number of iterations and the expansion constant of the curve can all be set directly by the staff. In a practical application, as the number of iterations increases, the inertia weight changes as follows: Figure 4 shown.

[0085] After obtaining the objective function value of the initial particle, the individual and global optimal values ​​can be determined based on its objective function value. The velocity and position information of the current particle are updated based on its inertia weight, individual optimal value, and global optimal value based on the current number of iterations. Each particle's objective function value is then compared with the individual historical optimal objective function value. If the current particle's objective function value is smaller, the individual optimal value is updated. The minimum objective function value of the current particle is obtained and compared with the global historical minimum objective function value. If the current objective function value is smaller, the global optimal value is updated. The steps of updating the particle's velocity and position, as well as the individual and global optimal values, are repeated until a termination condition is met. The termination condition can be reaching the maximum number of iterations or finding an optimal solution that meets the requirements.

[0086] After the iteration is completed, the particle corresponding to the global optimal value is the target particle.

[0087] The inertia weight can balance the algorithm's global development capabilities and local exploration capabilities. In the early stages of an iteration, a larger inertia weight can expand the search space and determine the optimal initial particle as the target particle. As the algorithm iterates, the inertia weight gradually decreases. In the later stages of the iteration, when the algorithm approaches the optimal solution, a relatively small inertia weight coefficient is used to weaken the retained information of the previous generation of particles, helping particles escape the local optimal solution and obtain better target particles.

[0088] The method for calculating various parameters of a guided projectile provided in this embodiment performs initialization processing on initial particles by means of chaotic mapping, thereby improving the population diversity of the initial particle group.

[0089] In this embodiment, a method for calculating various parameters of a guided artillery shell is provided. Figure 5 Flowchart of the method for calculating the various parameters of the guided projectile according to the embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0090] Step S301: construct an initial particle group and perform initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of the launch pitch angle and the attack angle.

[0091] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0092] Step S302 : obtaining initial particle information of the initial particle group, wherein the initial particle information includes velocity information and position information of a number of initial particles.

[0093] For details, please see Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0094] Step S303 , substituting the initial particle information into the trajectory model, and calculating the objective function value of each initial particle in the initial particle group; the objective function value is used to characterize the time of the initial particle entering the terminal guidance after moving according to the trajectory model.

[0095] Specifically, the above step S303 includes:

[0096] Step S3031: Substitute the initial particle information into the trajectory model to obtain the trajectory parameter information of each initial particle.

[0097] The trajectory model can be selected based on the actual situation. For simple trajectories, the standard projectile motion equation can be used. For complex trajectories, a trajectory model can be constructed based on the projectile motion equation, taking into account multiple factors such as air resistance and wind speed.

[0098] After substituting the initial particle information into the ballistic model, the ballistic parameters of the initial particle can be obtained by calculating the ballistic parameters. The ballistic parameters include maximum height, range, landing time, etc.

[0099] In some optional implementations, before step S3031, the following steps may also be performed:

[0100] Step a1, obtaining the ballistic force, wherein the ballistic force includes at least one of gravity, aerodynamic force and engine thrust.

[0101] The ballistic forces can be determined by analyzing the forces acting on the guided projectile. Gravity can be determined based on the mass and altitude of the guided projectile. Aerodynamic forces can be calculated using relevant fluid dynamics software. Engine thrust can be obtained using an engine thrust model.

[0102] In a practical application, gravity can be obtained as follows:

[0103]

[0104] in, is gravity, For quality, is the gravitational acceleration on the Earth's surface, is the radius of the Earth, The height of a guided projectile above the Earth's surface. Generally , Generally km.

[0105] In a practical application, the total aerodynamic force acting on a guided projectile is decomposed into three components in the projectile coordinate system, as shown in the following equation:

[0106]

[0107] Where X is the axial force, is the axial force coefficient, is the dynamic pressure, is the reference area of ​​the projectile, is the normal force, is the normal force coefficient, is the lateral force, is the lateral force coefficient.

[0108] According to the total aerodynamic force on the guided projectile, the aerodynamic force coefficient in the velocity coordinate system can be obtained, as shown in the following formula:

[0109]

[0110] Among them, CA is the drag coefficient, CL is the lift coefficient, CZ is the side force coefficient, is the attack angle of the guided projectile, is the sideslip angle of the guided projectile.

[0111] In a practical application, the engine thrust model is as follows:

[0112]

[0113] in, For the total rush, is the average thrust per unit time of the engine, The engine continues working time.

[0114] In another practical application, the average thrust per unit time of the engine can be expressed as follows:

[0115]

[0116] in, is the component of the engine's average thrust per unit time on the X-axis of the missile body coordinate system, is the component of the engine's average thrust per unit time on the Y axis of the missile body coordinate system, It is the component of the engine's average thrust per unit time on the Z axis of the projectile coordinate system.

[0117] Step a2: Based on the ballistic force, a ballistic analysis is performed on the guided projectile to construct a ballistic model, wherein the ballistic model includes at least one of a projectile dynamic equation, a projectile kinematic equation, and a projectile mass change equation.

[0118] The ballistic forces are used to analyze the ballistic characteristics under different flight conditions to obtain a ballistic model. The trajectory of a guided projectile can be divided into the launch phase, the free flight phase, and the terminal guidance phase. By establishing corresponding mathematical models for each phase, the ballistic model can be obtained. These mathematical models include the projectile dynamic equations, the projectile kinematic equations, and the projectile mass change equations.

[0119] In a practical application, the dynamic equation of a projectile is as follows:

[0120]

[0121]

[0122]

[0123] wherein, is the projectile flight speed, is the average engine thrust per unit time, is the projectile angle of attack, is the projectile side slip angle, is the axial force, is the trajectory inclination angle, is the speed inclination angle, is the normal force, is the lateral force, is the trajectory deflection angle.

[0124] In one practical application, the projectile kinematics equation is as follows:

[0125]

[0126]

[0127]

[0128] wherein, , , is the position coordinate of the projectile flight, is the axial flight speed of the projectile, is the normal flight speed of the projectile, is the lateral flight speed of the projectile, is the projectile flight speed, is the trajectory inclination angle, is the trajectory deflection angle.

[0129] In one practical application, the projectile mass change equation is as follows:

[0130]

[0131] wherein, is the projectile mass, is the trajectory deflection angle, is the fuel mass second flow per unit time of the projectile.

[0132] By analyzing the trajectory of the guided projectile and constructing a trajectory model, the guided projectile can be predicted, and the accuracy of subsequent particle evaluation can be improved.

[0133] Step S3032, input the trajectory parameter information to the seeker capture model to determine the initial particle entering terminal guidance time.

[0134] The seeker acquisition model can be constructed based on the conditions for the seeker to capture the target. By inputting the ballistic parameter information and the initial particles into the seeker acquisition model, the flight trajectory of the projectile corresponding to the initial particles can be obtained, thereby determining the projectile's terminal guidance time.

[0135] The conditions for the seeker to capture the target can be divided into ballistic constraints, effective range constraints and field of view constraints. If these three constraints are met at the same time, it can be guaranteed that the seeker can detect the target.

[0136] The trajectory of a guided projectile is relatively curved, and the direction of the seeker's optical axis is basically on the horizontal plane. Therefore, the seeker can only capture the target after passing the trajectory apex. The trajectory constraint formula is shown below:

[0137]

[0138] in, is the ballistic inclination angle.

[0139] The effective range of the seeker is usually limited by energy and has a limited effective range, so the effective range of the trajectory is less than the maximum effective range of the seeker.

[0140] The field of view angle is the angle between the missile-target line of sight and the seeker optical axis. The field of view angle is less than or equal to the maximum field of view angle of the seeker.

[0141] In a practical application, Figure 6 As shown, the coordinate system shown is the ground coordinate system, and the origin o is the launching point of the guided projectile. is the current center of mass position of the projectile, The target point position is the target point position. Based on the characteristics that the optical axis of the strapdown laser seeker coincides with the missile axis, its detection area forms a conical surface, and the optical axis intersects the ground at point , 、 They represent the angles of the seeker optical axis in the pitch and yaw directions in the geodetic coordinate system, The angle between the missile-target line of sight and the seeker optical axis is called the field of view angle.

[0142] The field of view can be obtained by the following formula:

[0143]

[0144] in, is the field of view, is the distance between the current center of mass of the projectile and the target point, that is, the projectile-target distance, is the current center of mass position of the projectile and point distance, for point The distance to the target point.

[0145] Among them, it can be obtained by the following formula:

[0146]

[0147]

[0148]

[0149] in, is the x-coordinate value of the target point in the ground coordinate system, is the x-coordinate value of the current center of mass of the projectile in the ground coordinate system, is the y coordinate value of the current center of mass of the projectile in the ground coordinate system, is the z coordinate value of the target point in the ground coordinate system, is the z coordinate value of the current center of mass of the projectile in the ground coordinate system, for point The x-coordinate value in the ground coordinate system, for point The z-coordinate value in the ground coordinate system.

[0150] The constraints of the field of view can be expressed as follows:

[0151]

[0152] in, is the field of view, is the maximum field of view of the seeker.

[0153] Step S3033: Based on the trajectory parameter information and the time to enter the terminal guidance, the objective function value of the initial particle is obtained.

[0154] According to the actual situation, the features to be optimized can be selected to construct the objective function. In addition to the time to enter the terminal guidance, the features to be optimized can also include the relative position between the projectile and the target, the angular deviation between the projectile and the target, etc.

[0155] In some optional implementations, the above step S3033 includes:

[0156] Step b1: determining the miss distance of the guided projectile based on the ballistic parameter information.

[0157] Based on the ballistic parameter information, the theoretical landing point of the guided artillery projectile can be obtained, and the miss distance of the guided artillery projectile can be determined by calculating the difference between the theoretical landing point and the actual landing point.

[0158] Step b2: construct a target sequence including the miss distance and the time to enter terminal guidance, and obtain a weight coefficient that matches the target sequence.

[0159] The target sequence can include a plurality of characteristics to be optimized in addition to the off-target amount and the time to terminal guidance. The weight coefficient is determined according to the importance of the different characteristics to be optimized, and can be directly specified by a worker or determined using an algorithm such as an entropy method.

[0160] In step b3, the target function value of the initial particle is obtained based on the target sequence and the weight coefficient.

[0161] The target function value of the initial particle can be obtained by multiplying the feature data included in the target sequence by the corresponding weight coefficient and summing the results.

[0162] By setting the target sequence and the weight coefficient matching the target sequence to obtain the target function value, the flexibility of the algorithm can be improved.

[0163] In step S304, the target particle is determined based on the target function value of each initial particle to obtain the coordinate information of the target particle.

[0164] Specifically, step S304 includes:

[0165] In step S3041, the particle fitness of each initial particle is calculated based on the target function value of each initial particle.

[0166] The particle fitness information of the initial particle can be obtained by standardizing the target function value of each initial particle, so that the fitness values of all initial particles are on the same scale, and the convergence speed of the algorithm is improved.

[0167] In step S3042, the initial particle with the optimal particle fitness is determined as the candidate particle, and the speed parameter and the position parameter of the candidate particle are adjusted according to the preset boundary condition.

[0168] The initial particle with the maximum particle fitness can be selected as the candidate particle by comparison, and whether the candidate particle is within the boundary condition is determined according to the preset boundary condition. If the candidate particle is within the boundary condition, the speed parameter and the position parameter of the candidate particle are not adjusted. If the candidate particle is outside the boundary, a random number is selected within the boundary condition to adjust and update the speed parameter or the position parameter of the candidate particle.

[0169] In an actual application, the speed parameter of the candidate particle exceeds the preset boundary value, and the speed parameter of the candidate particle is adjusted according to the following formula:

[0170]

[0171] wherein, is the adjusted speed parameter of the candidate particle, 0 to ( ) in the range of random numbers, is the preset maximum speed value, It is the preset minimum speed value.

[0172] In another practical application, if the position parameters of the candidate particles exceed the preset boundary value, the position parameters of the candidate particles are adjusted according to the following formula:

[0173]

[0174] in, is the adjusted position parameter of the candidate particle, 0 to ( ) in the range of random numbers, is the preset maximum position value, is the preset minimum position value.

[0175] Step S3043: If the adjusted candidate particle meets the termination condition, the candidate particle is determined as the target particle.

[0176] The termination condition can be determined based on actual conditions. In a practical application, the termination conditions include a convergence threshold and a maximum number of iterations. If a candidate particle meets the convergence threshold, the candidate particle is designated as the target particle. If the candidate particle does not meet the convergence threshold, the particle information of the candidate particle is adjusted and updated, and the determination is repeated until the convergence threshold is met or the maximum number of iterations is reached.

[0177] In a practical application, the convergence threshold is 0.00001 and the maximum number of iterations is ten.

[0178] In some optional implementations, the above step S3043 further includes:

[0179] Step c1: If the adjusted candidate particle does not meet the termination condition, the particle fitness of the candidate particle and the average particle fitness of the initial particle group are determined.

[0180] Among them, the average particle fitness of the initial particle group can be directly calculated.

[0181] Step c2: Obtain the fitness ratio of the candidate particle's particle fitness to the average particle fitness.

[0182] Among them, the fitness ratio can be expressed as follows:

[0183]

[0184] in, is the fitness ratio, is the particle fitness of the candidate particle, is the average particle fitness of the initial particle population.

[0185] Step c3: Based on the fitness ratio, the parameters of each initial particle are updated to recalculate the objective function value of each initial particle after the parameters are updated.

[0186] Among them, a threshold parameter can be set, the fitness ratio and the threshold parameter can be compared, and the parameters of each initial particle can be updated based on the comparison result.

[0187] When the fitness ratio of the initial particles is greater than the threshold parameter, it means that the global search capability of the algorithm needs to be improved and the parameters of the initial particles need to be updated, as shown in the following formula:

[0188]

[0189]

[0190] in, is the velocity of the dth variable of the i-th particle at the k+1th iteration, is the inertia weight, is the velocity of the dth variable of the i-th particle at the k-th iteration, is the first learning factor, is the second learning factor, are random numbers that are independently and uniformly distributed in the interval from zero to one, are random numbers that are independently and uniformly distributed in the interval from zero to one, is the current local optimal value of a single particle, is the current global optimal value of a single particle, is the position of the dth variable of the ith particle at the k+1th iteration, is the position of the d-th variable of the ith particle at the k-th iteration.

[0191] The number of particle variables can be determined when constructing the initial particle population. The number of iterations can be determined by the staff. The first learning factor and the second learning factor can be directly determined by the staff.

[0192] If the fitness ratio of the initial particles is less than the threshold parameter, it means that the local exploration capability of the algorithm needs to be improved to increase the convergence speed of the algorithm. As shown in the following formula:

[0193]

[0194]

[0195] in, is the average value of all variables of all particles in the kth iteration.

[0196] In a practical application, it can be obtained by the following formula:

[0197]

[0198] in, is the variable number of particles, is the position of the d-th variable of the ith particle at the k-th iteration.

[0199] By obtaining the ratio of the particle fitness of the candidate particle and the average particle fitness of the initial particle group to update the initial particle parameters, the consumption of computing resources can be reduced and the flexibility of the algorithm can be improved.

[0200] In some optional implementations, when the parameters of the particle are updated, a penalty term is added to the objective function.

[0201] To ensure the normal flight of the projectile, it is necessary to constrain the information of the particles. By adding a penalty term to the objective function, the particles can be prevented from diffusing outside the constraint conditions during the iteration process. The constraint penalty term can be expressed as:

[0202]

[0203] in, is the penalty term for the i-th particle, is the deviation of the i-th particle from the x-th constraint. If the particle satisfies the x-th constraint, the penalty term is 0. If the particle does not satisfy the x-th constraint, the penalty term is the corresponding deviation value. The value of the penalty term increases with the increase in the deviation.

[0204] Constraints are not limited and can be set based on actual conditions. Constraints can be set on the angle of attack range to ensure the stability of the projectile's flight. Constraints can be set on the pitch angle to ensure the safety of the projectile's launch. Constraints can be set on the projectile's fall speed and angle to ensure the projectile's damage effectiveness. Constraints can be set on the maximum height of the trajectory. When a particle updates its position and velocity information, it can be brought back into the trajectory model to obtain its various metadata and determine whether these metadata meet the constraints.

[0205] In a practical application, the constraints are as follows:

[0206]

[0207] in, is the shell attack angle, is the minimum angle of attack of the projectile, is the maximum angle of attack of the projectile, is the launch pitch angle, is the minimum launch elevation angle of the projectile, is the maximum launch elevation angle of the projectile, is the falling velocity of the projectile, is the minimum falling velocity of the projectile, is the angle of impact of the shell, is the minimum angle of fall of the projectile, is the maximum height of the trajectory, The minimum value of the maximum height of the trajectory.

[0208] In some optional manners, when a penalty term is included in the objective function, when a particle updates its position information, the particle's historical position and current position are compared, and the particle position information is updated based on the comparison result.

[0209] Among them, when the particle's historical position and current position do not meet the constraints, their corresponding penalty items can be compared, and the position with the smaller penalty item can be selected to update the particle's position information; when one of the particle's historical position and current position meets the constraints, the position that meets the constraints can be selected to update the particle's position information; when the particle's historical position and current position both meet the constraints, their objective function values ​​can be compared, and the position with the smaller objective function value can be selected to update the particle's position information.

[0210] Step S3044, obtaining various element information of the target particle.

[0211] Among them, the target particles can be brought back to the ballistic model to output the ballistic model parameter information, and the target particle's various element information can be determined based on the ballistic model parameter information and the target position, where the various element information includes the artillery shell firing angle, artillery shell firing distance, etc.

[0212] The method for calculating the various parameters of a guided projectile provided in this embodiment uses the ballistic parameters of the initial particles and the time it enters terminal guidance to obtain the objective function value of the particles, allowing for quantitative evaluation of the initial particles. The objective function value is used to determine the particle fitness of each initial particle, and the initial particle with the best fitness is selected as the candidate particle. This improves the efficiency of particle search, and adjusts the parameters of the candidate particles based on preset boundary conditions, thereby enhancing the stability of the method.

[0213] Figure 7 The figure is a flow chart of a method for calculating various parameters of a guided artillery shell according to an exemplary embodiment.

[0214] The initialization parameters include the number of particles in the initial group as N and the dimension of each particle as D. Then the maximum number of iterations is initialized as T, the learning factors are c1 and c2; the maximum and minimum values ​​of the particle position are 、 The maximum and minimum particle speeds are 、 .

[0215] The maximum and minimum values ​​of inertia weight are 、 ;

[0216] When the inertia weight uses a nonlinear decreasing inertia weight model, it can be set according to the parameters in the model. In a practical application, the inertia weight model can be shown as follows:

[0217]

[0218] in, is the current inertia weight, is the minimum inertia weight, is the maximum inertia weight, is the minimum number of iterations, is the maximum number of iterations, is the expansion constant of the curve. The initialization parameters include the expansion constant of the inertia weight Gaussian decreasing curve is ; The maximum and minimum values ​​of inertia weight are 、 .

[0219] In some optional implementations, when the objective function includes a weight coefficient, the initialization parameter includes the weight coefficient of the objective function.

[0220] After initialization, the speed and position of the particles are randomly generated to obtain N random particles, namely , the initial position of the particle is , and randomly initialize the random velocity of the particle to ,Right now ;in are used to represent the 1st particle to the Nth particle respectively; is the first attack angle of the i-th particle, is the second angle of attack of the i-th particle... and so on is the D-1th attack angle of the i-th particle; is the pitch angle of the i-th particle; They are used to indicate the velocity of the i-th particle at the 1st angle of attack, the 2nd angle of attack...the D-1th angle of attack and the pitch angle, respectively.

[0221] N particles are then introduced into the projectile trajectory model, the trajectory parameters are solved, and the corresponding objective function values ​​are obtained. After obtaining the objective function values ​​of the particles, the fitness values ​​of each particle are calculated based on the objective function values, and the particle with the highest fitness value is selected as the individual optimal value. The historical fitness values ​​of each particle are compared, and the highest fitness value is selected as the global optimal value. The particle position and velocity are then updated, and boundary conditions are determined and processed. If the termination condition is met, the calculation stops and the optimal particle is output as the parameter value. Otherwise, the particle velocity and position are adjusted according to the local and global optimal objective function values, and the particle is substituted into the projectile trajectory model. The above steps are repeated until the termination condition is met.

[0222] After the termination conditions are met, various parameter files after trajectory optimization are output and the operation ends.

[0223] This embodiment also provides a device for calculating various parameters of a guided projectile. This device is used to implement the above-mentioned embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0224] This embodiment provides a device for calculating various parameters of a guided artillery shell. Figure 8 Shown, including:

[0225] An initial particle group construction module 401 is used to construct an initial particle group and perform initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of the launch pitch angle and the angle of attack;

[0226] An initial particle information acquisition module 402 is used to acquire initial particle information of an initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles;

[0227] The objective function value acquisition module 403 is used to substitute the initial particle information into the trajectory model and calculate the objective function value of each initial particle in the initial particle group; the objective function value is used to represent the time it takes for the initial particle to enter the terminal guidance after moving according to the trajectory model;

[0228] The various-element information acquisition module 404 is used to determine the target particle based on the objective function value of each initial particle to obtain various-element information of the target particle.

[0229] In some optional implementations, the initial particle population construction module 401 includes:

[0230] An initial particle group acquisition unit, used for acquiring an initial particle group;

[0231] An initial mapping parameter acquisition unit is used to acquire initial mapping parameters, wherein the initial mapping parameters include a first initial value and a first control parameter, the first initial value is the position of the particle individual, and the first control parameter is a parameter for controlling the degree of chaos;

[0232] A chaotic sequence generating unit is used to perform chaotic mapping on the initial mapping parameters to generate a chaotic sequence, wherein the chaotic sequence includes a plurality of chaotic sequence values;

[0233] The distribution unit is used to distribute the chaotic sequence value to the initial particles in the initial particle group to determine the speed information and position information of the initial particles.

[0234] In some optional implementations, the objective function value acquisition module 403 includes:

[0235] The trajectory parameter acquisition unit is used to substitute the initial particle information into the trajectory model to obtain the trajectory parameter information of each initial particle.

[0236] The terminal guidance entry time acquisition unit is used to input the ballistic parameter information into the seeker capture model to determine the terminal guidance entry time of the initial particle.

[0237] The objective function value acquisition unit is used to obtain the objective function value of the initial particle based on the trajectory parameter information and the time to enter the terminal guidance.

[0238] In some optional implementations, the objective function value acquisition module 403 further includes:

[0239] The ballistic force acquisition unit is used to acquire the ballistic force, wherein the ballistic force includes at least one of gravity, aerodynamic force and engine thrust.

[0240] The ballistic model construction unit is used to perform ballistic analysis on the guided projectile based on the ballistic force to construct a ballistic model, wherein the ballistic model includes at least one of a projectile dynamic equation, a projectile kinematic equation, and a projectile mass change equation.

[0241] In some optional implementations, the objective function value obtaining unit includes:

[0242] The miss distance determination subunit is used to determine the miss distance of the guided artillery projectile based on the ballistic parameter information.

[0243] The construction subunit is used to construct a target sequence including the miss distance and the time to enter the terminal guidance, and obtain a weight coefficient matching the target sequence.

[0244] The acquisition subunit is used to obtain the objective function value of the initial particle based on the target sequence and weight coefficient.

[0245] In some optional implementations, the metadata information acquisition module 404 includes:

[0246] The particle fitness calculation unit is used to calculate the particle fitness of each initial particle based on the objective function value of each initial particle.

[0247] The adjustment unit is used to determine the initial particle with the best particle fitness as the candidate particle, and adjust the speed parameter and position parameter of the candidate particle according to the preset boundary conditions.

[0248] The target particle determination unit is configured to determine the candidate particle as the target particle if the adjusted candidate particle meets the termination condition.

[0249] In some optional implementations, the metadata information acquisition module 404 further includes:

[0250] The fitness determination unit is used to determine the particle fitness of the candidate particle and the average particle fitness of the initial particle group if the adjusted candidate particle does not meet the termination condition.

[0251] The fitness ratio unit is used to calculate the fitness ratio of the particle fitness of the fitness ratio candidate particle to the average particle fitness.

[0252] The updating unit is used to update the update of each initial particle based on the fitness ratio to recalculate the objective function value of each initial particle after the parameters are updated.

[0253] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0254] The parameter calculation device of the guided artillery projectile in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0255] The embodiment of the present invention also provides a computer device having the above Figure 8 The parameter calculation device of the guided artillery shell shown.

[0256] See also Figure 9 , Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0257] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0258] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0259] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0260] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0261] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0262] The input device 30 can receive inputted digital or character information, and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), etc. The display device includes, but is not limited to, a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device can be a touch screen.

[0263] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded from a network and stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method shown in the above embodiments.

[0264] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, the operation of the computer can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source file, executable file, installation package file, etc. Correspondingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer executes the corresponding compiled program after compiling the instructions, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0265] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for calculating various parameters of a guided artillery shell, characterized in that: The method comprises: Constructing an initial particle group and performing initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of a launch pitch angle and an angle of attack; Acquiring initial particle information of the initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles; Substituting the initial particle information into a trajectory model, and calculating an objective function value of each initial particle in the initial particle group; the objective function value is used to represent the time it takes for the initial particle to enter terminal guidance after moving according to the trajectory model; Determining target particles based on the objective function values ​​of the initial particles to obtain various element information of the target particles; The step of substituting the initial particle information into the trajectory model and calculating the objective function value of each initial particle in the initial particle group includes: Substituting the initial particle information into the trajectory model to obtain the trajectory parameter information of each initial particle; Inputting the trajectory parameter information into a seeker acquisition model to determine the initial particle's entry terminal guidance time; Based on the trajectory parameter information and the time to enter terminal guidance, an objective function value of the initial particle is obtained.

2. The method according to claim 1, characterized in that The step of constructing an initial particle group and initializing the initial particle group includes: Get the initial particle population; Acquiring initial mapping parameters, wherein the initial mapping parameters include a first initial value and a first control parameter, the first initial value is the position of the individual particle, and the first control parameter is a parameter for controlling the degree of chaos; Performing chaotic mapping on the initial mapping parameters to generate a chaotic sequence, wherein the chaotic sequence includes a plurality of chaotic sequence values; The chaotic sequence value is distributed to the initial particles in the initial particle group to determine the velocity information and position information of the initial particles.

3. The method according to claim 1, characterized in that The obtaining of the objective function value of the initial particle based on the trajectory parameter information and the time to enter the terminal guidance comprises: Determining a miss distance of the guided projectile based on the ballistic parameter information; Constructing a target sequence including the miss distance and the time to enter terminal guidance, and obtaining a weight coefficient matching the target sequence; Based on the target sequence and the weight coefficient, the objective function value of the initial particle is obtained.

4. The method according to any one of claims 1 to 3, characterized in that: The step of determining target particles based on the objective function values ​​of the respective initial particles comprises: Calculating the particle fitness of each of the initial particles based on the objective function value of each of the initial particles; Determine the initial particle with the best particle fitness as a candidate particle, and adjust the velocity parameter and position parameter of the candidate particle according to the preset boundary conditions; If the adjusted candidate particle meets the termination condition, the candidate particle is determined as the target particle.

5. The method according to claim 4, characterized in that The method further comprises: If the adjusted candidate particle does not meet the termination condition, determining the particle fitness of the candidate particle and the average particle fitness of the initial particle population; Obtaining a fitness ratio of the particle fitness of the candidate particle to the average particle fitness; Based on the fitness ratio, the parameters of the initial particles are updated to recalculate the objective function value of the initial particles after the parameters are updated.

6. The method according to claim 1, characterized in that Before substituting the initial particle information into the trajectory model, the method further includes: Acquiring a ballistic force, wherein the ballistic force comprises at least one of gravity, aerodynamic force, and engine thrust; Based on the ballistic force, a ballistic analysis is performed on the guided projectile to construct a ballistic model, wherein the ballistic model includes at least one of a projectile dynamic equation, a projectile kinematic equation, and a projectile mass change equation.

7. A device for calculating various parameters of a guided artillery shell, characterized in that: The device comprises: An initial particle group construction module is used to construct an initial particle group and perform initialization processing on the initial particle group, wherein the initial particle group includes at least one initial particle, and the initial particle includes at least one variable of a launch pitch angle and an angle of attack; an initial particle information acquisition module, configured to acquire initial particle information of the initial particle group, wherein the initial particle information includes velocity information and position information of a plurality of initial particles; an objective function value acquisition module, configured to substitute the initial particle information into a trajectory model and calculate an objective function value for each initial particle in the initial particle group; the objective function value is used to characterize the time it takes for the initial particle to enter terminal guidance after moving according to the trajectory model; A module for acquiring various element information, configured to determine target particles based on the objective function values ​​of the initial particles, so as to acquire various element information of the target particles; Wherein, the objective function value acquisition module includes: a trajectory parameter acquisition unit, configured to substitute the initial particle information into a trajectory model to acquire trajectory parameter information of each initial particle; a terminal guidance entry time acquisition unit, configured to input the trajectory parameter information into a seeker capture model to determine the terminal guidance entry time of the initial particle; The objective function value acquisition unit is used to acquire the objective function value of the initial particle based on the trajectory parameter information and the time to enter the terminal guidance.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for calculating various parameters of a guided artillery projectile according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for calculating various parameters of a guided artillery projectile according to any one of claims 1 to 6.

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