Internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling

Abstract

The invention provides an internal and external ballistic parameter optimization method based on an NSGA-III algorithm and multi-factor coupling, and the method is used for optimizing an engine design scheme of an aircraft, and comprises the steps: obtaining an initial population containing a plurality of initial individuals, each initial individual representing a group of design variables of an engine; the initial individual is any one of missile appearance parameters or launching initial state parameters; for each initial individual, performing multi-factor coupling to obtain a plurality of aircraft state parameters corresponding to the initial individual and inner trajectory parameters corresponding to the aircraft state parameters; and performing parameter optimization on the initial individuals in the initial population by using an NSGA-III algorithm according to the plurality of aircraft state parameters and the respective corresponding inner ballistic parameters to obtain a parameter optimization result, thereby optimizing the engine design scheme on the premise of ensuring the optimal outer ballistic.

Description

Technical Field

[0001] This application relates to the field of ballistic optimization, and particularly to an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling. Background Art

[0002] Currently, traditional single-objective optimization methods or simple multi-objective optimization algorithms are often used to optimize internal and external ballistic parameters. When dealing with the optimization problem of internal and external ballistic parameters, these methods usually only consider a single objective or a few objectives, and often ignore the coupling relationship between multiple factors.

[0003] However, when optimizing internal and external ballistic parameters in the prior art, it is impossible to effectively balance the contradictions between multiple objectives, resulting in an unsatisfactory optimization result, and the influence of multi-factor coupling on ballistic performance cannot be fully considered, so that the optimized ballistic parameters may not achieve the expected effect in actual applications.

[0004] Therefore, how to optimize ballistic parameters more accurately is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] This application provides an internal and external ballistic parameter optimization method, device, equipment, medium and program based on the NSGA-Ⅲ algorithm and multi-factor coupling to solve the technical problem of insufficient accuracy of ballistic parameters.

[0006] In a first aspect, this application provides an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling for optimizing the engine design scheme of an aircraft,

[0007] The method includes:

[0008] Obtain an initial population including multiple initial individuals, and each of the initial individuals represents a set of design variables of the engine; the initial individual is either a missile shape parameter or a launch initial state parameter;

[0009] For each of the initial individuals, perform multi-factor coupling to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding internal ballistic parameters;

[0010] According to the multiple aircraft state parameters and their respective corresponding internal ballistic parameters, use the NSGA-Ⅲ algorithm to optimize the parameters of the initial individuals in the initial population to obtain a parameter optimization result, where the parameter optimization result characterizes the optimized engine design scheme.

[0011] Optionally, according to multiple said aircraft state parameters and their respective corresponding interior ballistic parameters, using the NSGA-Ⅲ algorithm, parameter optimization is performed on the initial individuals in the initial population to obtain a parameter optimization result, including:

[0012] According to multiple said aircraft state parameters and their respective corresponding interior ballistic parameters, non-dominated sorting is performed on the initial individuals in the initial population to obtain initial individual groups corresponding to multiple non-dominated ranks, and the non-dominated rank represents the quality degree of the corresponding initial individual;

[0013] For each said non-dominated rank, crowding degree calculation is performed on the initial individual group corresponding to this non-dominated rank to obtain the crowding distance corresponding to each said initial individual, and the crowding distance characterizes the distribution density of the initial individual in this non-dominated rank;

[0014] Obtain a reference point and a target density, and based on the reference point and the target density, perform region allocation on each said initial individual to obtain a reference region corresponding to each said initial individual, where the reference point represents the preset evolution direction of the population, and the reference region represents the association degree between the initial individual and the reference point;

[0015] For each said reference region, based on the crowding distance and non-dominated rank corresponding to the initial individual corresponding to this reference region, perform selection genetic operations on the initial individuals corresponding to this reference region to obtain target individuals corresponding to this reference region;

[0016] Generate a temporary population according to the target individuals, and based on the temporary population, obtain a parameter optimization result.

[0017] Optionally, generate a temporary population according to the target individuals, and based on the temporary population, obtain a parameter optimization result, including:

[0018] Perform crossover operations on the target individuals to obtain offspring individuals corresponding to the target individuals; perform mutation operations on the offspring individuals to obtain a group of offspring individuals corresponding to the target individuals;

[0019] Combine the group of offspring individuals and the initial population to obtain a temporary population; and re-perform non-dominated sorting and crowding degree calculation on the temporary population to perform the selection genetic operation to obtain new target individuals, obtain a new temporary population according to the new target individuals, and determine whether the new temporary population meets the convergence condition;

[0020] If so, decode and output the target individuals in the new temporary population to obtain the parameter optimization result output; if not, use the new temporary population as the new initial population, and re - execute the step of performing non - dominated sorting on the initial individuals in the initial population to obtain the initial individual groups corresponding to multiple non - dominated levels until the new temporary population meets the convergence condition.

[0021] Optionally, for each of the initial individuals, perform multi - factor coupling to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters, including:

[0022] Obtain the first input conditions for interior ballistic calculation, missile shape parameters, and launch initial state parameters;

[0023] Based on the atmospheric model, the launch initial state parameters, and the missile shape parameters, obtain the second input conditions for exterior ballistic calculation;

[0024] Obtain the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving with time;

[0025] According to the combustion curve, the first input conditions, and the second input conditions, perform interior and exterior ballistic calculations to obtain the aircraft state parameters of the aircraft;

[0026] According to the aircraft state parameters, determine whether the aircraft reaches the target position;

[0027] If not, re - execute the step of obtaining the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving with time, until the aircraft reaches the target position.

[0028] Optionally, the first input conditions include charge structure parameters, propellant parameters, nozzle characteristic parameters, mass characteristic parameters, and time step.

[0029] Optionally, according to the combustion curve, the first input conditions, and the second input conditions, perform interior and exterior ballistic calculations to obtain the aircraft state parameters of the aircraft, including:

[0030] According to the combustion curve and the first input conditions, perform interior ballistic calculation to obtain the real - time thrust value and mass center of mass characteristics of the engine;

[0031] According to the thrust value, the mass center of mass characteristics, and the second input conditions, perform exterior ballistic calculation to obtain the aircraft state parameters.

[0032] In a second aspect, the present application provides an interior and exterior ballistic parameter optimization method device based on the NSGA - Ⅲ algorithm and multi - factor coupling, which is used to execute the method described in any item of the first aspect.

[0033] The device includes:

[0034] An acquisition module, configured to acquire an initial population including a plurality of initial individuals, each of the initial individuals representing a set of design variables of an engine; the initial individual is any one of missile shape parameters or launch initial state parameters;

[0035] A first processing module, configured to perform multi-factor coupling on each of the initial individuals to obtain a plurality of aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters;

[0036] A second processing module, configured to perform parameter optimization on the initial individuals in the initial population by using the NSGA-III algorithm according to the plurality of aircraft state parameters and their respective corresponding interior ballistic parameters, to obtain a parameter optimization result, wherein the parameter optimization result characterizes an optimized engine design scheme.

[0037] Optionally, when the second processing module performs parameter optimization on the initial individuals in the initial population by using the NSGA-III algorithm according to the plurality of aircraft state parameters and their respective corresponding interior ballistic parameters to obtain a parameter optimization result, it is configured to:

[0038] Perform non-dominated sorting on the initial individuals in the initial population according to the plurality of aircraft state parameters and their respective corresponding interior ballistic parameters to obtain initial individual groups corresponding to a plurality of non-dominated ranks, where the non-dominated rank represents the quality degree of the corresponding initial individual;

[0039] For each of the non-dominated ranks, calculate the crowding degree of the initial individual group corresponding to the non-dominated rank to obtain the crowding distance corresponding to each initial individual, where the crowding distance characterizes the distribution density of the initial individual in the non-dominated rank;

[0040] Obtain a reference point and a target density, and based on the reference point and the target density, perform region allocation on each initial individual to obtain a reference region corresponding to each initial individual, where the reference point characterizes the preset evolution direction of the population, and the reference region characterizes the association degree between the initial individual and the reference point;

[0041] For each of the reference regions, perform selection genetic operations on the initial individuals corresponding to the reference region based on the crowding distance and non-dominated rank corresponding to the initial individuals corresponding to the reference region to obtain target individuals corresponding to the reference region;

[0042] Generate a temporary population according to the target individuals, and based on the temporary population, obtain a parameter optimization result.

[0043] Optionally, when the second processing module executes generating a temporary population according to the target individual and obtaining a parameter optimization result based on the temporary population, it is used for:

[0044] Performing a crossover operation on the target individual to obtain offspring individuals corresponding to the target individual; performing a mutation operation on the offspring individuals to obtain a group of offspring individuals corresponding to the target individual;

[0045] Combining the group of offspring individuals and the initial population to obtain a temporary population; and re-performing non-dominated sorting and crowding degree calculation on the temporary population to perform the selection genetic operation to obtain a new target individual, obtaining a new temporary population according to the new target individual, and determining whether the new temporary population meets the convergence condition;

[0046] If so, decoding and outputting the target individuals in the new temporary population to obtain a parameter optimization result output; if not, using the new temporary population as a new initial population, and re-executing the step of performing non-dominated sorting on the initial individuals in the initial population to obtain groups of initial individuals corresponding to respective non-dominated ranks until the new temporary population meets the convergence condition.

[0047] Optionally, when the first processing module executes performing multi-factor coupling for each of the initial individuals to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters, it is used for:

[0048] Obtaining the first input conditions for interior ballistic solution, missile shape parameters, and launch initial state parameters;

[0049] Based on the atmospheric model, the launch initial state parameters, and the missile shape parameters, obtaining the second input conditions for exterior ballistic solution;

[0050] Obtaining the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving with time;

[0051] According to the combustion curve, the first input conditions, and the second input conditions, performing interior and exterior ballistic solutions to obtain the aircraft state parameters of the aircraft;

[0052] According to the aircraft state parameters, determining whether the aircraft reaches the target position;

[0053] If not, re-executing obtaining the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving with time, until the aircraft reaches the target position.

[0054] Optionally, the first input conditions include charge structure parameters, propellant parameters, nozzle characteristic parameters, mass characteristic parameters, and time step.

[0055] Optionally, when the first processing module performs internal and external ballistics calculation according to the combustion curve, the first input condition, and the second input condition to obtain the aircraft state parameters of the aircraft, it is used for:

[0056] Perform internal ballistics calculation according to the combustion curve and the first input condition to obtain the real-time thrust value and mass center of mass characteristics of the engine;

[0057] Perform external ballistics calculation according to the thrust value, the mass center of mass characteristics, and the second input condition to obtain the aircraft state parameters.

[0058] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0059] The memory stores computer execution instructions;

[0060] The processor executes the computer execution instructions stored in the memory to implement the internal and external ballistics parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling according to any one of the first aspects.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the internal and external ballistics parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling according to any one of the first aspects.

[0062] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the internal and external ballistics parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling according to any one of the first aspects.

[0063] The internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by this application realizes the multi-objective coordinated optimization of the internal and external ballistic parameters of the missile engine by combining the NSGA-Ⅲ algorithm and multi-factor coupling. During the optimization process, the coupling relationship of various factors such as missile shape parameters and launch initial state parameters is fully considered, making the optimization results closer to the actual flight situation. By using the elite retention strategy and crowding degree calculation of the NSGA-Ⅲ algorithm, the diversity of the population and the uniform distribution of solutions are ensured, avoiding falling into local optimal solutions and improving the reliability and stability of the optimization results. At the same time, by automatically searching and optimizing design variables through the algorithm, the links of manual trial and error and empirical judgment are reduced, greatly improving the efficiency of missile engine design and shortening the design cycle. Finally, the optimized missile engine design scheme can achieve a better balance in multiple performance indicators, thus significantly improving the comprehensive performance of the missile and enhancing its adaptability and missile triggering effect in different combat scenarios. Brief Description of the Drawings

[0064] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0065] Figure 1 It is a schematic flow chart of an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of this application;

[0066] Figure 2 It is another schematic flow chart of an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of this application;

[0067] Figure 3 It is a schematic structural diagram of an internal and external ballistic parameter optimization device based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of this application;

[0068] Figure 4 It is a schematic structural diagram of an electronic device provided by this application.

[0069] Through the above accompanying drawings, the clear embodiments of this application have been shown, and there will be more detailed descriptions later. These drawings and text descriptions are not intended to limit the scope of the concept of this application in any way, but to explain the concept of this application to those skilled in the art by referring to specific embodiments. Detailed Embodiments

[0070] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0071] First, the terms related to the present application will be explained:

[0072] NSGA-Ⅲ algorithm: (Non-dominated Sorting Genetic Algorithm III) is an advanced multi-objective optimization algorithm, which is an extension of NSGA-II and is mainly aimed at high-dimensional multi-objective optimization problems. It guides the population to be evenly distributed on the Pareto front by introducing reference points and reference directions, enhancing the diversity of solutions and the uniformity of distribution.

[0073] Interior ballistics: It is an important branch of ballistics, mainly studying the gunpowder combustion, material flow, energy conversion, projectile motion and other related phenomena and their laws in the gun barrel and rocket engine during the launch process.

[0074] Exterior ballistics: It is a discipline that studies the motion laws and related phenomena of projectiles in flight after leaving the barrel, and is a branch of ballistics. It mainly studies the force conditions, centroid motion, laws of motion around the center of the projectile or projectile in flight and their influencing factors, etc., involving basic discipline fields such as theoretical mechanics, aerodynamics, atmospheric physics and geophysics. The research objects of exterior ballistics include flying objects such as bullets, shells, bombs, rockets and missiles.

[0075] The interior and exterior ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by the present application can be executed by a terminal device or a server.

[0076] The above-mentioned terminal device can be a wireless terminal or a wired terminal. A wireless terminal can be a device that provides voice and / or other service data connectivity to users, a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core network devices via a Radio Access Network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For another example, the wireless terminal can also be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), and other devices. The wireless terminal can also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, a remote terminal, an access terminal, a user terminal, a user agent, a user device or user equipment, which is not limited herein. Optionally, the above-mentioned terminal device can also be a smart watch, a tablet computer, and other devices.

[0077] As the power system of a missile, the solid rocket motor provides power for it, which affects the overall layout and airframe structure of the missile. Therefore, the optimization design of the motor should focus on the overall performance of the missile, rather than being limited to the local optimum of the motor calculation results. When designing a traditional solid power system, the internal ballistics of the solid power and the external ballistics of the missile flight are often separated from each other, and parameter matching is achieved only through one-way data transmission. However, during actual flight, there is a coupling influence relationship between the internal ballistics parameters of the motor and the external ballistics parameters of the missile. Specifically, changes in the external ballistics parameters of the aircraft will act on the internal ballistics parameters of the motor, causing changes in the thrust, specific impulse performance, and mass change rate of the motor, which in turn will have a counter-effect on the external ballistics performance. Therefore, when designing a solid power system, the design idea oriented to the external ballistics index should be considered.

[0078] Meanwhile, there are some significant deficiencies in two-way coupling when dealing with complex multi-objective problems.

[0079] Firstly, its computational complexity is high because it is necessary to solve multiple mutually influencing physical processes simultaneously, which results in high consumption of computational resources and long time consumption. Secondly, the numerical stability is poor. The frequent transfer and mutual influence of data are likely to cause numerical instability problems, such as negative grids and floating-point exceptions. In addition, in practical applications, two-way coupling often requires simplification of certain physical processes, which may lead to inaccurate results and inability to fully capture all complex physical phenomena.

[0080] To overcome these deficiencies, this application provides an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling. Combining the NSGA-Ⅲ algorithm is an effective solution. The NSGA-Ⅲ algorithm has powerful multi-objective optimization capabilities. It can handle multiple optimization objectives simultaneously and, through its elitist retention strategy and crowding degree calculation, ensure the diversity of the population and the uniformity of the solution distribution, thus avoiding falling into local optimal solutions. This algorithm performs excellently in dealing with high-dimensional multi-objective optimization problems, can effectively meet the complex multi-objective optimization requirements in two-way coupling, improve the reliability and stability of the optimization results, and ensure that the optimization solutions obtained in two-way coupling are more reasonable and effective.

[0081] An internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of this application aims to solve the above technical problems in the prior art and is executed by the above electronic device (terminal device or server).

[0082] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. These several specific embodiments below can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments. The following will describe the embodiments of this application in conjunction with the drawings.

[0083] As Figure 1 shown, Figure 1 is a schematic flowchart of an internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of this application. An internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling may specifically include steps S201 to S203, where:

[0084] S201. Obtain an initial population containing multiple initial individuals, and each initial individual represents a set of design variables of the engine; the initial individual is either missile shape parameters or launch initial state parameters.

[0085] The design variables are design parameters such as the charge geometric parameters and nozzle parameters. It should be noted that the above design parameters need to meet engineering constraints such as the upper limit of the combustion chamber pressure.

[0086] S202. For each initial individual, perform multi-factor coupling to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters.

[0087] The multi-factor coupling can be completed based on the bidirectional coupling model of the interior and exterior ballistic parameters.

[0088] The aircraft state parameters are the speed, coordinates, and range of the aircraft. The above interior ballistic parameters are the atmospheric pressure corresponding to the height based on the height in the coordinates.

[0089] S203. According to multiple aircraft state parameters and their respective corresponding interior ballistic parameters, use the NSGA-Ⅲ algorithm to optimize the parameters of the initial individuals in the initial population to obtain the parameter optimization results. Among them, the parameter optimization results represent the optimized engine design scheme.

[0090] The interior and exterior ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by the embodiments of the present application. This technical solution realizes the multi-objective coordinated optimization of the interior and exterior ballistic parameters of the missile engine by combining the NSGA-Ⅲ algorithm and multi-factor coupling. During the optimization process, the coupling relationship of various factors such as the missile shape parameters and the initial launch state parameters is fully considered, making the optimization results closer to the actual flight situation. Using the elitist retention strategy and crowding degree calculation of the NSGA-Ⅲ algorithm ensures the diversity of the population and the uniform distribution of solutions, avoids falling into local optimal solutions, and improves the reliability and stability of the optimization results. At the same time, by automatically searching and optimizing the design variables through the algorithm, the links of manual trial and error and empirical judgment are reduced, greatly improving the efficiency of missile engine design and shortening the design cycle. Finally, the optimized missile engine design scheme can achieve a better balance in multiple performance indicators, thereby significantly improving the comprehensive performance of the missile and enhancing its adaptability and missile triggering effect in different combat scenarios.

[0091] In an implementable manner, according to multiple aircraft state parameters and their respective corresponding interior ballistic parameters, using the NSGA-Ⅲ algorithm to optimize the parameters of the initial individuals in the initial population to obtain the parameter optimization results, including:

[0092] According to multiple aircraft state parameters and their respective corresponding interior ballistic parameters, perform non-dominated sorting on the initial individuals in the initial population to obtain groups of initial individuals corresponding to multiple non-dominated levels. The non-dominated level represents the quality degree of the corresponding initial individual.

[0093] Specifically, initialize the domination counters of all individuals to 0 and set the domination set to be empty. For each pair of individuals p and q in the population, determine the domination relationship between them: if p dominates q, add q to the domination set of p and increment the domination counter of q by 1. If q dominates p, increment the domination counter of p by 1. Identify all individuals with a domination counter of 0 to form the first-level non-dominated set F1. For each individual p in F1, decrement the domination counter of each individual q in its domination set by 1. If the domination counter of q becomes 0, add it to the next-level non-dominated set F2. Repeat the above process until all individuals are assigned to a non-dominated rank.

[0094] For each non-dominated rank, calculate the crowding degree for the initial group of individuals corresponding to that non-dominated rank to obtain the crowding distance for each initial individual. The crowding distance characterizes the distribution density of the initial individual in that non-dominated rank.

[0095] Specifically, for each objective function fk, sort the individuals in that non-dominated rank in ascending order of the value of fk. Initialize the crowding distance of the boundary individuals (the individuals at both ends after sorting) to infinity, indicating their positions at the edge of the objective space and having a higher retention priority. For the intermediate individuals, calculate their crowding distance as the difference in the objective function values between two adjacent individuals divided by the difference between the maximum and minimum values of that objective function.

[0096] It should be noted that for the intermediate individuals, calculate their crowding distance as the difference in the objective function values between two adjacent individuals divided by the difference between the maximum and minimum values of that objective function. This value reflects the distribution density of the individual in the objective space. The larger the crowding distance, the sparser the solutions around the individual.

[0097] Obtain a reference point and a target density, and based on the reference point and the target density, perform region allocation for each initial individual to obtain the reference region corresponding to each initial individual. Here, the reference point characterizes the preset evolution direction of the population, and the reference region characterizes the degree of association between the initial individual and the reference point.

[0098] For each reference region, based on the crowding distance and the non-dominated rank corresponding to the initial individuals in that reference region, perform selection and genetic operations on the initial individuals corresponding to that reference region to obtain the target individuals corresponding to that reference region.

[0099] Among them, based on the non-dominated rank and the crowding distance, give priority to selecting individuals with a lower non-dominated rank. If the non-dominated ranks are the same, select individuals with a larger crowding distance.

[0100] Generate a temporary population based on the target individuals and obtain the parameter optimization result based on the temporary population.

[0101] In one implementable manner, a temporary population is generated according to a target individual, and based on the temporary population, a parameter optimization result is obtained, including:

[0102] Perform a crossover operation on the target individual to obtain offspring individuals corresponding to the target individual. Perform a mutation operation on the offspring individuals to obtain a group of offspring individuals corresponding to the target individual.

[0103] Specifically, perform a crossover operation on the selected individuals to generate new offspring individuals. Common crossover methods include single-point crossover, multi-point crossover, simulated binary crossover (SBX), etc. Through the crossover operation, the excellent characteristics of the parent individuals are combined into the offspring. Perform a mutation operation on the crossed offspring individuals to increase the diversity of the population and avoid falling into local optima. The mutation operation can be Gaussian mutation, polynomial mutation, etc. By making slight changes at certain gene positions of the individuals, new solutions are generated.

[0104] Combine the group of offspring individuals and the initial population to obtain a temporary population. And re-perform non-dominated sorting and crowding degree calculation on the temporary population to perform selection genetic operations to obtain new target individuals, obtain a new temporary population according to the new target individuals, and determine whether the new temporary population meets the convergence condition.

[0105] Specifically, merge the current population and the offspring population to form a temporary enlarged population. Re-perform non-dominated sorting and crowding degree calculation on the merged population to evaluate the fitness and distribution of all individuals. According to the non-dominated rank and crowding distance, select the next-generation population from the merged population to ensure that excellent individuals are retained while maintaining the diversity and evolution direction of the population.

[0106] If so, decode and output the target individuals in the new temporary population to obtain the parameter optimization result output. If not, use the new temporary population as the new initial population, and re-perform the step of performing non-dominated sorting on the initial individuals in the initial population to obtain groups of initial individuals corresponding to respective non-dominated ranks until the new temporary population meets the convergence condition.

[0107] In one implementable manner, for each initial individual, perform multi-factor coupling to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters, including:

[0108] Obtain the first input conditions for interior ballistic calculation, missile shape parameters, launch initial state parameters. The first input conditions include charge structure parameters, propellant parameters, nozzle characteristic parameters, mass characteristic parameters, and time step.

[0109] First, use 3D software to draw the key geometric features of typical engine charges such as star-shaped, tubular, and finned-columnar charges, and set variable parameters such as the number of star angles, star angle radius, outer diameter of the grain for star-shaped charges, inner and outer diameters, and charge length for tubular charges. Then, obtain parameters such as charge structure parameters, propellant parameters, nozzle characteristic parameters, mass characteristic parameters, and time step required for internal ballistics calculation.

[0110] Among them, the charge structure parameters include the number of star angles, star angle radius, and outer diameter of the grain for star-shaped charges; the inner and outer diameters and charge length for tubular charges; the propellant parameters include propellant density, burning rate, burning rate coefficient, characteristic velocity, and pressure exponent; the nozzle characteristic parameters include throat diameter and expansion ratio.

[0111] In addition, missile shape parameters and launch initial state parameters are input parameters required for external ballistics calculation. Among them, the missile shape parameters include body diameter, length, wingspan, control surface size, and aerodynamic shape; the launch initial state parameters include initial position, initial velocity, attitude angles (pitch angle, yaw angle, roll angle), and launch mass.

[0112] Based on the atmospheric model, launch initial state parameters, and missile shape parameters, obtain the second input condition for external ballistics calculation.

[0113] Call the atmospheric model to obtain atmospheric change parameters, which include parameters related to altitude such as wind speed and direction, air density, and speed of sound; perform aerodynamic calculations based on the atmospheric change parameters and missile shape parameters to obtain aerodynamic parameters, which include dimensionless aerodynamic force coefficients and dimensionless aerodynamic moment factors. The dimensionless aerodynamic force coefficients include drag coefficient, lift coefficient, and side force coefficient, and the dimensionless aerodynamic moment factors include roll moment factor, yaw moment factor, and pitch moment factor. Take the launch initial state parameters and aerodynamic parameters as the second input condition for external ballistics calculation.

[0114] Obtain the combustion curve corresponding to the propellant burning surface, and the combustion curve characterizes the law of the evolution of the burning area over time.

[0115] Based on the combustion curve, the first input condition, and the second input condition, perform internal and external ballistics calculations to obtain the aircraft state parameters of the aircraft.

[0116] Specifically, according to the combustion curve and the first input condition, perform internal ballistics calculation to obtain the real-time thrust value and mass center characteristics of the engine. According to the thrust value, mass center characteristics, and the second input condition, perform external ballistics calculation to obtain the aircraft state parameters.

[0117] According to the combustion curve and the first input condition, the interior ballistic solution is carried out to obtain the real-time thrust value and mass center of mass characteristics of the engine, including: based on the three-dimensional model of the charge, the parallel layer migration method is used to simulate the process of the propellant burning surface uniformly retreating along the normal direction of the original surface during the combustion process, and then the evolution law of the combustion area over time is calculated. Furthermore, the combustion chamber pressure is calculated through the real-time change of the charge burning surface, and then the thrust value is obtained using the combustion chamber pressure, and the real-time mass characteristics are determined according to the charge geometry, that is, the changes in mass, center of mass, and moment of inertia caused by the propellant combustion.

[0118] According to the thrust value, mass center of mass characteristics and the second input condition, the exterior ballistic solution is carried out to obtain the flight vehicle state parameters, including: inputting the real-time thrust value and mass center of mass characteristics of the engine into the six-degree-of-freedom missile motion equations, that is, the exterior ballistic equations, and then calling the Runge-Kutta subroutine to solve the differential equations, and performing exterior ballistic calculations on the exterior ballistic equations to obtain the flight vehicle state parameters of the flight vehicle.

[0119] Among them, the exterior ballistic equations include the dynamic equations of the missile center of mass motion in the ground coordinate system:

[0120]

[0121] In the formula: x is the displacement in the x direction; y is the displacement in the y direction; z is the displacement in the z direction; V is the missile motion speed; θ is the ballistic inclination angle; ψ V is the ballistic deflection angle.

[0122] Study the dynamic equations of the motion around the center of mass in the body coordinate system:

[0123]

[0124] In the formula: is the pitch angle; ψ is the yaw angle; γ is the roll angle; ω x is the rotational angular velocity in the x direction; ω y is the rotational angular velocity in the y direction; ω z is the rotational angular velocity in the z direction.

[0125] During the missile flight process, the differential equation describing the missile mass change:

[0126]

[0127] In the formula, m s (t) is the fuel consumption per unit time; m0 is the initial mass of the missile; m T is the mass after the engine stops working; T is the engine working time.

[0128] Angle geometric relationship equation

[0129] During the missile flight, the equations describing the angular relationships are as follows:

[0130]

[0131] Where: α is the angle of attack; β is the sideslip angle; γ v is the velocity inclination angle; θ is the trajectory inclination angle; ψ V is the trajectory deviation angle; is the pitch angle; ψ is the yaw angle; γ is the roll angle.

[0132] Based on the aircraft state parameters, determine whether the aircraft reaches the target position.

[0133] If not, re-execute to obtain the combustion curve corresponding to the propellant burning surface. The combustion curve characterizes the law of the combustion area evolving with time until the aircraft reaches the target position.

[0134] Next, a specific example is used to illustrate the internal and external ballistic parameter optimization method based on the NSGA-Ⅲ algorithm and multi-factor coupling provided in the above embodiment, as Figure 2 shown, including the following steps:

[0135] Obtain an initial population containing multiple initial individuals. Each initial individual represents a set of design variables of the engine. The initial individual is either the missile shape parameter or the launch initial state parameter.

[0136] For each initial individual, perform multi-factor coupling to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding internal ballistic parameters.

[0137] Based on the multiple aircraft state parameters and their respective corresponding internal ballistic parameters, perform non-dominated sorting on the initial individuals in the initial population to obtain initial individual groups corresponding to multiple non-dominated ranks. The non-dominated rank represents the quality level of the corresponding initial individual.

[0138] For each non-dominated rank, calculate the crowding degree of the initial individual group corresponding to the non-dominated rank to obtain the crowding distance corresponding to each initial individual. The crowding distance characterizes the distribution density of the initial individual in the non-dominated rank.

[0139] Obtain a reference point and a target density, and based on the reference point and the target density, perform region allocation on each initial individual to obtain a reference region corresponding to each initial individual. Among them, the reference point represents the preset evolution direction of the population, and the reference region represents the correlation degree between the initial individual and the reference point.

[0140] For each reference region, based on the crowding distance and non-dominated rank of the initial individual corresponding to the reference region, perform selection genetic operations on the initial individual corresponding to the reference region to obtain the target individual corresponding to the reference region.

[0141] Perform crossover operations on the target individuals to obtain the offspring individuals corresponding to the target individuals. Perform mutation operations on the offspring individuals to obtain a group of offspring individuals corresponding to the target individuals.

[0142] Combine the group of offspring individuals and the initial population to obtain a temporary population. And re-perform non-dominated sorting and crowding degree calculation on the temporary population to perform selection genetic operations to obtain new target individuals, obtain a new temporary population according to the new target individuals, and determine whether the new temporary population meets the convergence condition.

[0143] If so, decode and output the target individuals in the new temporary population to obtain the parameter optimization result output. If not, use the new temporary population as the new initial population, and re-perform the step of performing non-dominated sorting on the initial individuals in the initial population to obtain groups of initial individuals corresponding to respective non-dominated ranks until the new temporary population meets the convergence condition.

[0144] It should be noted that the NSGA-Ⅲ algorithm handles multi-objective conflicts through non-dominated sorting, and multi-factor coupling provides the association of interdisciplinary parameters. The combination of the two realizes the multi-objective collaborative optimization of complex engineering problems, and has direction guidance and global search capabilities. In the internal and external ballistics optimization framework based on the NSGA-Ⅲ algorithm and multi-factor coupling, the realization of non-dominated sorting and multi-objective trade-off needs to be completed through a systematic hierarchical and diversity preservation mechanism. The aircraft state parameters and their corresponding internal ballistics parameters should be used as initial individuals to input into the multi-objective model, calculate their corresponding objective function values, and determine whether the constraint conditions are violated. Then perform non-dominated sorting based on the dominance relationship: traverse all individuals, if an individual is not inferior to another individual in all objectives and is better in at least one objective, then it is determined as a dominance relationship; form multiple layers of Pareto fronts by iteratively screening the non-dominated individuals, where the first layer is the global optimal solution set, and the subsequent layers sequentially reflect the sub-optimality of the solutions.

[0145] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0146] It should be further noted that although the steps in the flowchart are sequentially displayed according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0147] Figure 3 FIG. is a schematic structural diagram of an internal and external ballistic parameter optimization device based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by an embodiment of the present application. As Figure 3 shown, the internal and external ballistic parameter optimization device 40 based on the NSGA-Ⅲ algorithm and multi-factor coupling provided by the embodiment of the present application includes:

[0148] An acquisition module 401, configured to acquire an initial population including a plurality of initial individuals, and each initial individual represents a set of design variables of an engine; the initial individual is any one of missile shape parameters or launch initial state parameters;

[0149] A first processing module 402, configured to perform multi-factor coupling on each initial individual to obtain a plurality of aircraft state parameters corresponding to the initial individual and their respective corresponding internal ballistic parameters;

[0150] A second processing module 403, configured to perform parameter optimization on the initial individuals in the initial population by using the NSGA-Ⅲ algorithm according to a plurality of aircraft state parameters and their respective corresponding internal ballistic parameters, and obtain a parameter optimization result, where the parameter optimization result characterizes an optimized engine design scheme.

[0151] In a possible implementation manner, when the second processing module 403 performs parameter optimization on the initial individuals in the initial population by using the NSGA-Ⅲ algorithm according to a plurality of aircraft state parameters and their respective corresponding internal ballistic parameters to obtain a parameter optimization result., it is used for:

[0152] Performing non-dominated sorting on the initial individuals in the initial population according to a plurality of aircraft state parameters and their respective corresponding internal ballistic parameters to obtain a group of initial individuals corresponding to each non-dominated rank, and the non-dominated rank represents the quality degree of the corresponding initial individual;

[0153] For each non-dominated rank, calculate the crowding degree of the initial individual group corresponding to the non-dominated rank to obtain the crowding distance corresponding to each initial individual, where the crowding distance characterizes the distribution density of the initial individual in the non-dominated rank;

[0154] Obtain a reference point and a target density, and based on the reference point and the target density, perform region allocation for each initial individual to obtain a reference region corresponding to each initial individual, where the reference point characterizes the preset evolution direction of the population, and the reference region characterizes the association degree between the initial individual and the reference point;

[0155] For each reference region, based on the crowding distance and the non-dominated rank corresponding to the initial individuals in the reference region, perform selection and genetic operations on the initial individuals in the reference region to obtain the target individuals corresponding to the reference region;

[0156] Generate a temporary population according to the target individuals, and based on the temporary population, obtain the parameter optimization result.

[0157] In a possible implementation manner, when the above-mentioned second processing module 403 executes generating a temporary population according to the target individuals and obtaining the parameter optimization result based on the temporary population, it is used for:

[0158] Perform a crossover operation on the target individuals to obtain the offspring individuals corresponding to the target individuals; perform a mutation operation on the offspring individuals to obtain a group of offspring individuals corresponding to the target individuals;

[0159] Combine the group of offspring individuals and the initial population to obtain a temporary population; and re-perform non-dominated sorting and crowding degree calculation on the temporary population to perform selection and genetic operations to obtain new target individuals, obtain a new temporary population according to the new target individuals, and determine whether the new temporary population meets the convergence condition;

[0160] If so, decode and output the target individuals in the new temporary population to obtain the parameter optimization result output; if not, use the new temporary population as the new initial population, and re-execute the step of performing non-dominated sorting on the initial individuals in the initial population to obtain the initial individual groups corresponding to each non-dominated rank until the new temporary population meets the convergence condition.

[0161] In a possible implementation manner, when the above-mentioned first processing module 402 executes performing multi-factor coupling for each initial individual to obtain multiple aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters, it is used for:

[0162] Obtain the first input condition for interior ballistic solution, missile shape parameters, and launch initial state parameters;

[0163] Based on the atmospheric model, the initial launch state parameters, and the missile's shape parameters, obtain the second input condition for the external ballistic calculation;

[0164] Obtain the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving over time;

[0165] According to the combustion curve, the first input condition, and the second input condition, perform the internal and external ballistic calculations to obtain the flight vehicle state parameters of the flight vehicle;

[0166] Based on the flight vehicle state parameters, determine whether the flight vehicle reaches the target position;

[0167] If not, then re-execute obtaining the combustion curve corresponding to the propellant burning surface, where the combustion curve characterizes the law of the combustion area evolving over time, until the flight vehicle reaches the target position.

[0168] In a possible implementation, the first input condition includes the charge structure parameters, the propellant parameters, the nozzle characteristic parameters, the mass characteristic parameters, and the time step.

[0169] In a possible implementation, when the above-mentioned first processing module 402 performs the internal and external ballistic calculations according to the combustion curve, the first input condition, and the second input condition to obtain the flight vehicle state parameters of the flight vehicle, it is used for:

[0170] According to the combustion curve and the first input condition, perform the internal ballistic calculation to obtain the real-time thrust value and the mass center of mass characteristics of the engine;

[0171] According to the thrust value, the mass center of mass characteristics, and the second input condition, perform the external ballistic calculation to obtain the flight vehicle state parameters.

[0172] The internal and external ballistic parameter optimization device based on the NSGA-Ⅲ algorithm and multi-factor coupling provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0173] It should be understood that the above device embodiment is only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiment is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules, or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0174] In addition, unless otherwise specified, in each embodiment of the present application, each functional unit / module may be integrated into one unit / module, or each unit / module may exist physically alone, or two or more units / modules may be integrated together. The above integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0175] When the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc.

[0176] Figure 4 It is a schematic structural diagram of the electronic device provided by the present application. As Figure 4 shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. Among them, the processor 501, the memory 502, and the communication component 503 are connected through a bus 504.

[0177] In the specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0178] For the specific implementation process of the processor 501, reference may be made to the above method embodiment, and its implementation principle and technical effect are similar, so details are not described herein again.

[0179] Unless otherwise specified, the processor 501 may be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the memory 502 may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc.

[0180] When an integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0181] An embodiment of this application also provides a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the processor executes the computer-executable instructions, the redundant field update method for a distributed system as described above is implemented.

[0182] An embodiment of this application also provides a computer program product, including a computer program. When the computer program is executed by the processor, the redundant field update method for a distributed system as described above is implemented.

[0183] In the above embodiments, the descriptions of the various embodiments have their respective focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as within the scope described in this specification.

[0184] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other implementation schemes of this application. This application aims to cover any variations, uses, or adaptive changes of this application. These variations, uses, or adaptive changes follow the general principles of this application and include the common general knowledge or conventional technical means in this technical field that are not disclosed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this application are pointed out by the following claims.

[0185] It should be understood that this application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended claims.

Claims

1. A method for optimizing internal and external ballistic parameters based on NSGA-Ⅲ algorithm and multi-factor coupling, characterized in that: Used to optimize aircraft engine design, The method comprises: Acquire an initial population including a plurality of initial individuals, each of which represents a set of design variables of the engine; the initial individual is any one of a missile shape parameter or a launch initial state parameter; For each of the initial individuals, multiple factors are coupled to obtain a plurality of aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters; According to the plurality of aircraft state parameters and their corresponding interior ballistic parameters, the NSGA-III algorithm is used to perform parameter optimization on the initial individuals in the initial population to obtain parameter optimization results, wherein the parameter optimization results represent the optimized engine design scheme.

2. The internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling according to claim 1 is characterized in that: According to the plurality of aircraft state parameters and their corresponding interior ballistic parameters, the NSGA-III algorithm is used to optimize the parameters of the initial individuals in the initial population to obtain parameter optimization results, including: According to the plurality of aircraft state parameters and their corresponding interior ballistic parameters, the initial individuals in the initial population are non-dominatedly sorted to obtain initial individual groups corresponding to a plurality of non-dominated levels, wherein the non-dominated levels represent the quality of the corresponding initial individuals; For each of the non-dominated levels, the crowding degree of the initial individual group corresponding to the non-dominated level is calculated to obtain the crowding degree distance corresponding to each of the initial individuals, wherein the crowding degree distance represents the distribution density of the initial individuals in the non-dominated level; Acquire a reference point and a target density, and based on the reference point and the target density, perform a region allocation on each of the initial individuals to obtain a reference region corresponding to each of the initial individuals, wherein the reference point represents a preset evolutionary direction of the population, and the reference region represents a degree of association between the initial individual and the reference point; For each of the reference regions, based on the crowding distance and non-dominated level corresponding to the initial individuals in the reference region, a selection genetic operation is performed on the initial individuals in the reference region to obtain a target individual in the reference region; A temporary population is generated according to the target individual, and a parameter optimization result is obtained based on the temporary population.

3. The internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling according to claim 2 is characterized in that: Generating a temporary population according to the target individual, and obtaining a parameter optimization result based on the temporary population, including: Performing a crossover operation on the target individual to obtain an offspring individual corresponding to the target individual; performing a mutation operation on the offspring individual to obtain an offspring individual group corresponding to the target individual; The offspring individual group and the initial population are combined to obtain a temporary population; and the non-dominated sorting and crowding degree calculation are re-executed on the temporary population to perform the selection genetic operation to obtain a new target individual, a new temporary population is obtained according to the new target individual, and it is determined whether the new temporary population meets the convergence condition; If so, the target individual in the new temporary population is decoded and output to obtain the parameter optimization result output; if not, the new temporary population is used as the new initial population, and the steps of non-dominated sorting of the initial individuals in the initial population are re-executed to obtain the initial individual groups corresponding to multiple non-dominated levels, until the new temporary population meets the convergence conditions.

4. The internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling according to claim 1 is characterized in that: For each of the initial individuals, multiple factors are coupled to obtain a plurality of aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters, including: Obtain the first input condition of interior ballistic solution, missile shape parameters, and launch initial state parameters; Based on the atmospheric model, the launch initial state parameters and the missile shape parameters, a second input condition for solving the exterior ballistics is obtained; Obtaining a combustion curve corresponding to the burning surface of the propellant, wherein the combustion curve represents a law of evolution of the burning area over time; performing internal and external ballistics calculations according to the combustion curve, the first input condition, and the second input condition to obtain aircraft state parameters of the aircraft; Determining whether the aircraft has reached the target position according to the aircraft state parameters; If not, the combustion curve corresponding to the propellant burning surface is obtained again, wherein the combustion curve represents the law of the evolution of the combustion area over time until the aircraft reaches the target position.

5. The internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling according to claim 4 is characterized in that: The first input conditions include charge structure parameters, propellant parameters, nozzle characteristic parameters, mass characteristic parameters and time step.

6. The internal and external ballistic parameter optimization method based on NSGA-III algorithm and multi-factor coupling according to claim 4 is characterized in that: According to the combustion curve, the first input condition and the second input condition, internal and external ballistics are solved to obtain the aircraft state parameters of the aircraft, including: Performing an internal ballistic solution according to the combustion curve and the first input condition to obtain a real-time thrust value and mass center of mass characteristics of the engine; An exterior ballistic solution is performed according to the thrust value, the mass center of mass characteristic and the second input condition to obtain the aircraft state parameters.

7. An internal and external ballistic parameter optimization device based on NSGA-Ⅲ algorithm and multi-factor coupling, characterized in that: For executing the method according to any one of claims 1 to 6, The device comprises: An acquisition module is used to acquire an initial population including a plurality of initial individuals, each of which represents a set of design variables of the engine; the initial individual is any one of a missile shape parameter or a launch initial state parameter; A first processing module is used for performing multi-factor coupling for each of the initial individuals to obtain a plurality of aircraft state parameters corresponding to the initial individual and their respective corresponding interior ballistic parameters; The second processing module is used to optimize the parameters of the initial individuals in the initial population using the NSGA-III algorithm according to the multiple aircraft state parameters and their corresponding internal ballistic parameters, so as to obtain parameter optimization results, wherein the parameter optimization results represent the optimized engine design scheme.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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