Grid narrow well structure phase layout optimization method based on genetic algorithm optimization
Through the optimization method based on genetic algorithm, the defects of existing acoustic wave regulation equipment in regulation accuracy, real-timeness and equipment volume are solved, and the precise control of sound waves in complex environments is achieved, which significantly improves the effect of sound wave regulation.
Patent Information
- Application Number
- CN202411814478.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-11
- Publication Date
- 2025-04-25
AI Technical Summary
Existing sound wave control equipment has defects in regulation accuracy, real-timeness, and large size and bulkiness of the equipment, making it difficult to achieve precise control of sound waves in complex environments.
The optimization method based on genetic algorithm is adopted to generate the optimal phase layout at fixed-point frequency. By optimizing the design parameters of the grid narrow well structure, the spatial directional angle of the scattered sound field is adaptively matched to meet the expected design direction.
It improves the accuracy and real-time nature of sound wave regulation, reduces the impact of side lobes on the main lobe, significantly improves the sound wave deflection effect, and provides a new idea of lightweight sound wave regulation technology.
Smart Images
Figure CN119885333A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of acoustic wave control technology, and in particular to a method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization. Background Art
[0002] By precisely controlling the phase, amplitude and propagation direction of the sound wave, the propagation and scattering behavior of the sound wave in a complex environment can be manipulated. The deflection sound wave control technology of the scattered sound field can be used to improve image resolution and target positioning in acoustic imaging; it can be used for target detection and tracking in sonar systems; it can be used to achieve signal transmission in complex channels and improve anti-interference capabilities in acoustic communications. The development and application of these technologies will further promote the development of sound wave control technology in practical applications. Summary of the invention
[0003] The purpose of the present invention is to generate the optimal phase layout at a fixed frequency based on a genetic algorithm in order to solve the defects of the existing acoustic wave control equipment, such as the control accuracy, real-time performance, and the large size and bulkiness of the existing acoustic wave control equipment. For the grid narrow well acoustic structure, based on the idea of global optimization using a genetic algorithm, the grid structure whose spatial directivity angle of the scattered sound field meets the expected design direction is adaptively matched by optimizing the design parameters. This method can be extended to the control optimization of the scattered sound field structure of complex acoustic targets, providing a new idea for the future lightweight acoustic wave control technology.
[0004] An embodiment of the present invention provides a method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization, the method comprising:
[0005] Step 1, determining the phase layout optimization variable parameters of the grid narrow well structure;
[0006] Step 2, determine the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure;
[0007] Step 3, encoding the phase layout optimization variable parameters for the genetic algorithm;
[0008] Step 4, first determine the fitness of the genetic algorithm according to the fitness function, determine the relevant operating parameters of the genetic algorithm, and then iterate the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individuals gradually approach the optimal solution, and finally output the global optimal solution when the iteration exit condition is met;
[0009] Step 5: Decode the global optimal solution to obtain the optimal phase layout optimization variable parameters.
[0010] In some embodiments, the step 1 of determining the phase layout optimization variable parameters of the grid narrow well structure includes:
[0011] The optimization parameters of the grid narrow well structure are determined as the phase layout of the grid structure.
[0012] In some embodiments, the step 2, determining the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure, includes:
[0013] Step S1, establishing a mapping relationship between the phase layout and the scattering direction of the grid narrow well structure according to a mathematical model, as shown in the following formula:
[0014]
[0015] In the above, φ(m,n) is the phase layout of the grid narrow well structure, m and n are the number of narrow well rows and columns of the grid narrow well structure, k is the wave number, D is the dimension, and θ is the pitch angle, which ranges from 0 to 90°. is the azimuth, ranging from 0 to 360°;
[0016] Step S2, scattering the sound field through the grid narrow well structure Establish the spatial pointing characteristic relationship of the grid narrow well structure:
[0017]
[0018] Step S3, in the case of a grid structure m×n structure layout, The optimization problem of solving the maximum value is transformed into solving the scattered sound field in the expected direction of the acoustic structure of the expected grid narrow well. For the maximum problem, set the fitness function as:
[0019]
[0020] In some embodiments, the step 2, determining the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure, includes:
[0021] The constraint expression for optimizing the grid narrow well structure is as follows:
[0022]
[0023] In the above formula, α is the pitch angle of the expected deflection of the grid narrow well structure, ranging from 0 to 90°, and β is the horizontal angle of the expected deflection of the grid narrow well structure, ranging from 0 to 360°.
[0024] In some embodiments, the method further includes: setting the genetic algorithm parameters population size NP to 100, the number of genetic iterations M to 200, the crossover rate Pc to 0.8, and the mutation rate Pm to 0.05.
[0025] In some embodiments, the method for manufacturing the grid narrow well structure includes:
[0026] m*n air slots are made on a rectangular steel structure;
[0027] The bottom of m*n air slots are equipped with m*n slender screws and RS555 model micro motors;
[0028] The depth of the air slot is controlled and adjusted by a micromotor;
[0029] m*n micromotors are integrated and merged into a USB interface through three circuit boards;
[0030] By adding a housing around the device, a complete micromechanical mesh device was successfully built.
[0031] In some embodiments, each narrow well controls the depth of the narrow well by driving a nut through a micromotor at the bottom, and the micromotor is uniformly coded and managed by a host computer through a control center, thereby achieving two-dimensional control of the scattered sound field of the grid device in the air.
[0032] In some embodiments, the host computer controls the depth of the narrow well through the 81-way travel frame of the serial port interface. The travel distance of a narrow well can be input and the data can be sent to the device. In addition, the host computer can also reset and clear uniformly;
[0033] The instructions are transmitted to the micromotor through the RS-485 communication interface on the circuit board, and the micromotor controls the nut to push the narrow well bottom plate up and down to control the depth of the narrow well groove.
[0034] An embodiment of the present invention provides a method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization, the method comprising:
[0035] Based on genetic algorithm, establish fitness value function The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
[0036] An embodiment of the present invention provides an electronic device, including: a processor and a storage device;
[0037] The storage device stores a computer program, and when the computer program is executed by the processor, the method described in any of the above embodiments is implemented.
[0038] The beneficial effects of the above embodiments include:
[0039] The present invention considers the problems of non-unique phase layout, multi-dimensional variables and complex analysis that can actually meet the needs of the main lobe control of the scattered sound field of the grid structure, and proposes a genetic algorithm for random search and global optimization to control the scattered sound field, automatically accumulate evolutionary advantages through the fitness function, and output the global optimal solution. The genetic algorithm is used to optimize the basic phase layout of the grid structure. Under the same expected control direction, the optimized grid structure phase layout reduces the influence of the side lobe on the main lobe, and the sound wave deflection effect is outstanding. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The drawings illustrate generally, by way of example and not limitation, various embodiments discussed herein.
[0041] Figure 1 This is the basic idea of genetic algorithm;
[0042] Figure 2 This is a physical picture of the grid narrow well structure;
[0043] Figure 3 This is the optimization result of the grid narrow well structure with 99 layouts at 2kHz;
[0044] Figure 4 The three-dimensional plane projection of the optimization results of the grid narrow well structure with 99 layouts at 2kHz;
[0045] Figure 5 The optimization result of the grid narrow well structure with 99 layouts at 8kHz;
[0046] Figure 6 The three-dimensional plane projection of the optimization results of the grid narrow well structure with 99 layouts at 8kHz;
[0047] Figure 7 The optimization results of the grid narrow well structure with a 12 12 layout at 30kHz;
[0048] Figure 8 Three-dimensional plane projection of the optimization results of the grid narrow well structure with a 1212 layout at 30kHz. DETAILED DESCRIPTION
[0049] In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present application, the implementation of the embodiments of the present application is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present application.
[0050] In the embodiments of the present application, it should be noted that, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection or a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.
[0051] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.
[0052] The present invention provides a method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization, such as Figure 1 As shown, the method includes the following steps 1 to 5.
[0053] Step 1: Determine the phase layout optimization variable parameters of the grid narrow well structure.
[0054] Step 2: Determine the adaptation function and constraints that need to be optimized for the grid narrow well structure.
[0055] Step 3: Encode the phase layout optimization variable parameters for the genetic algorithm.
[0056] Step 4, first determine the fitness of the genetic algorithm according to the fitness function, determine the relevant operating parameters of the genetic algorithm, and then repeatedly iterate the genetic algorithm to perform selection, crossover, and mutation to improve the fitness of the genetic algorithm, and the individuals gradually approach the optimal solution, and finally output the global optimal solution when the iteration exit conditions are met.
[0057] Step 5: Decode the global optimal solution to obtain the optimal phase layout optimization variable parameters.
[0058] The genetic algorithm parameters are set as population size NP to 100, number of genetic iterations M to 200, crossover rate Pc to 0.8, and mutation rate Pm to 0.05.
[0059] The optimization parameters of the grid narrow well structure are determined as the phase layout of the grid structure.
[0060] In this embodiment, the steps in step 2 to derive the objective function to be optimized for the grid narrow well structure are:
[0061] Step S1, establishing a mapping relationship between the phase layout and the scattering direction of the grid narrow well structure according to a mathematical model, as shown in the following formula:
[0062]
[0063] In the above, Φ(m,n) is the phase layout of the grid narrow well structure, m and n are the number of narrow well rows and columns of the grid narrow well structure, k is the wave number, D is the dimension, and θ is the pitch angle, which ranges from 0 to 90°. is the azimuth angle, ranging from 0 to 360°.
[0064] Step S2, scattering the sound field through the grid narrow well structure Establish the spatial pointing characteristic relationship of the grid narrow well structure:
[0065]
[0066] Step S3, considering the actual amount of calculation, The maximum value solution will lead to inefficient optimization of the acoustic structure of the grid narrow well. Therefore, in the case of the grid structure m×n structure layout, The optimization problem of solving the maximum value is transformed into solving the scattered sound field in the expected direction of the acoustic structure of the expected grid narrow well. The maximum value problem is set, so the adaptation function is:
[0067]
[0068] The constraint expression for optimizing the grid narrow well structure in step 2 is:
[0069]
[0070] In the above formula, α is the pitch angle of the expected deflection of the grid narrow well structure, and its value range is 0-90°; β is the horizontal angle of the expected deflection of the grid narrow well structure, and its value range is 0-360°.
[0071] The grid narrow well structure is a programmable mechanical drive device that can autonomously control the depth. Figure 2 As shown in the figure, the manufacturing process of the device is as follows: First, m*n air slots are made on a rectangular steel structure. The bottom of the m*n air slots are equipped with m*n slender screws and RS555 model micromotors. The depth of the air slots is controlled and adjusted by the micromotors. The m*n micromotors are integrated and merged into a USB interface through three circuit boards. Finally, a shell is added around the device, and a complete micromechanical grid device is successfully established.
[0072] Each narrow well drives the nut through the micromotor at the bottom to control the depth of the narrow well. The micromotor is uniformly coded and managed by the host computer through the control center, thereby realizing the two-dimensional control of the scattered sound field of the grid device in the air. Furthermore, the host computer controls the depth of the narrow well through the 81-way travel frame of the serial port interface. Enter the travel distance of a narrow well (unit: mm) and click Send Data to send instructions to the device. In addition, the host computer can also reset and clear. The instruction is transmitted to the micromotor through the RS-485 communication interface on the circuit board. The micromotor controls the nut to push the bottom plate of the narrow well up and down to control the depth of the narrow well groove.
[0073] The invention establishes a fitness value function based on a grid narrow well structure phase layout optimization method optimized by a genetic algorithm. The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
[0074] An embodiment of the present invention provides an electronic device, including: a processor and a storage device; a computer program is stored in the storage device, and when the computer program is executed by the processor, a method as in any of the above embodiments is implemented.
[0075] Embodiment 1:
[0076] This embodiment introduces a 9×9 grid narrow well acoustic structure, a grid narrow well structure phase layout optimization method based on genetic algorithm optimization, and establishes a fitness value function The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
[0077] The problem of controlling the scattered acoustic field of the grid narrow well acoustic structure is that the ultimate optimization goal is the expected scattered acoustic field of the grid structure. By adjusting and optimizing the independent variable Φ(m,n), the optimal dependent variable is obtained.
[0078] The objective function of the grid narrow well structure optimization based on genetic algorithm optimization is:
[0079]
[0080] Among them, θ is the pitch angle, ranging from 0° to 90°; is the azimuth angle, ranging from 0° to 360°. k is the incident wave number, and D is the structural dimension. M and N are the number of rows and columns of the grid structure, respectively, and 9 is taken here. Φ(m,n) is the phase of the independent variable of the grid structure, with a total of 9×9 independent variables, see Table 1.
[0081]
[0082] Table 1 Optimization variable setting range
[0083] The expected deflection pitch angle of the grid narrow well structure is set to 20° and the horizontal angle is 0°. The sound wave is incident vertically from the top of the grid structure, the incident frequency is 2kHz, and the background is air. The maximum genetic generation in the genetic algorithm is defined as 2500 times and the population size is 30. After 2500 genetic iterations, the scattered radiation direction of the grid structure is obtained using the genetic algorithm, such as Figure 3 and Figure 4 The basic phase layout is shown in Table 2.
[0084]
[0085] Table 2 Basic phase layout
[0086] Embodiment 2:
[0087] This embodiment introduces a 9×9 grid narrow well acoustic structure, a grid narrow well structure phase layout optimization method based on genetic algorithm optimization, and establishes a fitness value function The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
[0088] The problem of controlling the scattered acoustic field of the grid narrow well acoustic structure is that the ultimate optimization goal is the expected scattered acoustic field of the grid structure. By adjusting and optimizing the independent variable Φ(m,n), the optimal dependent variable is obtained.
[0089] The objective function of the grid narrow well structure optimization based on genetic algorithm optimization is:
[0090]
[0091] Among them, θ is the pitch angle, ranging from 0° to 90°; is the azimuth angle, ranging from 0° to 360°. k is the incident wave number, and D is the structural dimension. M and N are the number of rows and columns of the grid structure, respectively, and 9 is taken here. Φ(m,n) is the phase of the independent variable of the grid structure, with a total of 9×9 independent variables, see Table 3.
[0092]
[0093] Table 3 Optimization variable setting range
[0094] The expected deflection pitch angle of the grid narrow well structure is set to 25° and the horizontal angle is 45°. The sound wave is incident vertically from the top of the grid structure, the incident frequency is 8kHz, and the background is air. The maximum genetic generation in the genetic algorithm is defined as 2500 times and the population size is 30. After 2500 genetic iterations, the scattered radiation direction of the grid structure is obtained using the genetic algorithm, such as Figure 5 and Figure 6 The basic phase layout is shown in Table 4.
[0095]
[0096]
[0097] Table 4 Basic phase layout
[0098] Embodiment 3:
[0099] This embodiment introduces a 12×12 grid narrow well acoustic structure, a grid narrow well structure phase layout optimization method based on genetic algorithm optimization, and establishes a fitness value function The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
[0100] The problem of controlling the scattered acoustic field of the grid narrow well acoustic structure is that the ultimate optimization goal is the expected scattered acoustic field of the grid structure. By adjusting and optimizing the independent variable Φ(m,n), the optimal dependent variable is obtained.
[0101] The objective function of the grid narrow well structure optimization based on genetic algorithm optimization is:
[0102]
[0103] Among them, θ is the pitch angle, ranging from 0° to 90°; is the azimuth angle, ranging from 0° to 360°. k is the incident wave number, and D is the structural dimension. M and N are the number of rows and columns of the grid structure, respectively, and here they are taken as 12. Φ(m,n) is the phase of the independent variable of the grid structure, with a total of 12×12 independent variables, see Table 5.
[0104]
[0105] Table 5 Optimization variable setting range
[0106] The expected deflection pitch angle of the grid narrow well structure is set to 20° and the horizontal angle is 0°. The sound wave is incident vertically from the top of the grid structure, the incident frequency is 30kHz, and the background is air. The maximum genetic generation in the genetic algorithm is defined as 2500 times and the population size is 30. After 2500 genetic iterations, the scattered radiation direction of the grid structure is obtained using the genetic algorithm, such as Figure 7 and Figure 8 The basic phase layout is shown in Table 6.
[0107]
[0108]
[0109] Table 6 Basic phase layout
[0110] The technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.
[0111] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. A method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization, characterized in that: The method comprises: Step 1, determining the phase layout optimization variable parameters of the grid narrow well structure; Step 2, determine the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure; Step 3, encoding the phase layout optimization variable parameters for the genetic algorithm; Step 4, first determine the fitness of the genetic algorithm according to the fitness function, determine the relevant operating parameters of the genetic algorithm, and then iterate the selection, crossover, and mutation through the genetic algorithm to improve the fitness of the genetic algorithm, and the individuals gradually approach the optimal solution, and finally output the global optimal solution when the iteration exit condition is met; Step 5: Decode the global optimal solution to obtain the optimal phase layout optimization variable parameters.
2. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 1, characterized in that: The step 1, determining the phase layout optimization variable parameters of the grid narrow well structure, includes: The optimization parameters of the grid narrow well structure are determined as the phase layout of the grid structure.
3. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 1, characterized in that: The step 2, determining the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure, includes: Step S1, establishing a mapping relationship between the phase layout and the scattering direction of the grid narrow well structure according to a mathematical model, as shown in the following formula: In the above, Φ(m,n) is the phase layout of the grid narrow well structure, m and n are the number of narrow well rows and columns of the grid narrow well structure, k is the wave number, D is the dimension, and θ is the pitch angle, which ranges from 0 to 90°. is the azimuth, ranging from 0 to 360°; Step S2, scattering the sound field through the grid narrow well structure Establish the spatial pointing characteristic relationship of the grid narrow well structure: Step S3, in the case of a grid structure m×n structure layout, The optimization problem of solving the maximum value is transformed into solving the scattered sound field in the expected direction of the acoustic structure of the expected grid narrow well. For the maximum problem, set the fitness function as:
4. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 1, characterized in that: The step 2, determining the adaptation function and constraint conditions that need to be optimized for the grid narrow well structure, includes: The constraint expression for optimizing the grid narrow well structure is as follows: In the above formula, α is the pitch angle of the expected deflection of the grid narrow well structure, ranging from 0 to 90°, and β is the horizontal angle of the expected deflection of the grid narrow well structure, ranging from 0 to 360°.
5. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 1, characterized in that: The method also includes: setting the genetic algorithm parameters population size NP to 100, the number of genetic iterations M to 200, the crossover rate Pc to 0.8, and the mutation rate Pm to 0.
05.
6. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 1, characterized in that: The method for manufacturing the grid narrow well structure comprises: m*n air slots are made on a rectangular steel structure; The bottom of m*n air slots are equipped with m*n slender screws and RS555 model micro motors; The depth of the air slot is controlled and adjusted by a micromotor; m*n micromotors are integrated and merged into a USB interface through three circuit boards; By adding a housing around the device, a complete micromechanical mesh device was successfully built.
7. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 6, characterized in that: The depth of each narrow well is controlled by a micromotor driving a nut at the bottom. The micromotor is uniformly coded and managed by the host computer through the control center, thereby realizing two-dimensional control of the scattered sound field of the grid device in the air.
8. The method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization according to claim 7, characterized in that: Furthermore, the host computer controls the depth of the narrow well through the 81-way travel frame of the serial port interface. By inputting the travel distance of a narrow well and clicking send data, the device can be instructed. In addition, the host computer can also reset and clear the equipment uniformly. The instructions are transmitted to the micromotor through the RS-485 communication interface on the circuit board, and the micromotor controls the nut to push the narrow well bottom plate up and down to control the depth of the narrow well groove.
9. A method for optimizing the phase layout of a grid narrow well structure based on genetic algorithm optimization, characterized in that: The method comprises: Based on genetic algorithm, establish fitness value function The genetic algorithm is used to optimize the initial value of the phase layout of the grid narrow well structure. The phase layout is continuously optimized to produce a larger Ensure that the grid structure is constantly moving toward the main lobe direction during phase optimization The grid structure with the increasing trend is adaptively matched to the spatial directivity angle of the scattered sound field to meet the expected design direction.
10. An electronic device, characterized in that: include: processor and storage device; The storage device stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 9 is implemented.