A multi-constrained smart array antenna arrangement method and system

Abstract

The application discloses a multi-constraint intelligent array antenna arrangement method and system, and belongs to the technical field of array antenna design, and comprises the following steps: S1: effective area division; S2: area array element position screening; S3: array element re-distribution area; S4: adaptive value output; and S5: evolutionary algorithm iteration. The application adopts the re-partition principle, greatly improves the poor uniformity and other performances caused by random arrangement under the premise of meeting the array element spacing requirement, and simultaneously improves the success rate of array element tracking; through statistical analysis of the array element positions in the new area, the arrangement law of the array elements in the range is acquired, and the successively determined movement law can effectively avoid tracking in the dense area, greatly shortens the optimization time, and simultaneously guiding the to-be-distributed array elements to the area with low array element density can effectively utilize the array aperture, and improves the antenna array directivity pattern performance obtained through the final tracking.

Description

Technical Field

[0001] This invention relates to the field of array antenna design technology, specifically to a method and system for arranging multi-constraint intelligent array antennas. Background Technology

[0002] With the development of spaceborne radar systems, the functional requirements and performance requirements of these systems are increasing, often necessitating the deployment of multiple frequency bands on a single platform. While conventional ground-based radar designs use a single antenna per radar, for mobile platforms, especially spaceborne platforms, the limited space and ultra-wide operating bandwidth present a challenge in meeting frequency requirements. To address this conflict, multi-band co-aperture antenna design is gaining increasing importance. Within the limited platform space, deploying multiple phased array antennas of different frequency bands allows for the distribution of low-frequency and high-frequency antennas within the same aperture, improving aperture utilization.

[0003] To deploy phased array antennas across different frequency bands, it is necessary to reserve placement positions for high-frequency antennas within the low-frequency aperture range. Therefore, multiple isolation regions of varying shapes exist within the low-frequency aperture, which can only accommodate high-frequency antennas and are unsuitable for low-frequency antennas. Furthermore, to reduce costs, sparse array placement is often adopted in phased array design. Therefore, how to reasonably bypass these isolation regions and obtain the optimal array element placement scheme to achieve the best array pattern performance has become a key design focus. Since sparse optimization of the antenna array requires that the spacing between antenna elements meet a constraint (i.e., the spacing between any two elements must be greater than the antenna element size), sparse array optimization considering isolation regions must be performed under the constraint of multiple unsuitable placement regions. On the one hand, array elements cannot be placed in multiple forbidden regions; on the other hand, the spacing constraints between array elements must be met. This significantly increases the optimization difficulty compared to the case without isolation regions, making the optimization process extremely complex. These problems urgently need to be solved. Therefore, a multi-constraint intelligent array antenna placement method and system are proposed. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to complete the optimized design of sparse array antenna under the premise of satisfying multiple constraints. It provides a multi-constraint intelligent array antenna arrangement method. The optimization process starts with the random generation of array elements in the arrangeable area, and the entire area is divided and filtered. Then, the array elements are redistributed and their positions are optimized according to the characteristics of different areas.

[0005] The present invention solves the above-mentioned technical problems through the following technical solution, and the present invention includes the following steps:

[0006] S1: Effective Area Division

[0007] Extract information on non-arrangeable areas, and combine the information to effectively divide the entire region. The division principle is to minimize the number of areas occupied by all non-arrangeable areas and to minimize the area of ​​the divided regions.

[0008] S2: Filtering of Array Element Positions

[0009] Based on the defined effective regions, select array elements that are randomly arranged across the entire region; if an array element is located in a region that cannot be arranged, it is marked as a prohibited array element; if an array element is located in a region that can be arranged and the distance between adjacent array elements is less than the size of two array elements, it is marked as an overlapping array element; count the number of array elements in all regions, and arrange the arrangeable regions according to this number, sorting them from largest to smallest number of array elements.

[0010] S3: Array element redistribution area

[0011] The prohibited array elements and overlapping array elements selected in step S2 are reassigned to regions so that all these array elements are located within the arrangeable regions; after determining the regions, the arrangement positions of the above array elements are optimized so that they do not overlap with all other array elements.

[0012] S4: Fitness Output

[0013] Based on the array element arrangement positions obtained in step S3, calculate or set the optimization fitness value;

[0014] S5: Evolutionary Algorithm Iteration

[0015] Regenerate the random array element positions and repeat steps S1 to S4, iterating and optimizing according to the evolutionary algorithm.

[0016] Furthermore, in step S1, if the shape of the global envelope is not a rectangle when effectively dividing the global region, multiple rectangle fitting methods are used for division.

[0017] Furthermore, in step S1, the dimensions of a single rectangular region are L and W, respectively. These two parameters are used as optimization variables for partitioning optimization: First, set initial L and W, and use these dimensions to divide the entire region to obtain numerous rectangular partitions. After partitioning, calculate the number of partitions Ns occupied by all non-arrangeable regions, change L and W to continue iterative optimization until the fitness function obtains the optimal solution.

[0018] Furthermore, the fitness function is set according to the following formula:

[0019] fitness = 0.5 × LW + 0.5 × N s

[0020] The optimization objective is to minimize the fitness function value.

[0021] Furthermore, in step S2, based on the partition size obtained in step S1, the two-dimensional coordinate range of each partition is obtained, and all partitions are numbered; the position of each array element is partitioned and judged in turn to obtain the number of the partition where the array element is located; when the array element is located in the arrangeable area, it is judged whether the distance between the array element and the other array elements meets the distance requirement; if the distance requirement is not met, it is marked as an overlapping array element.

[0022] Furthermore, in step S2, when the global envelope shape is not rectangular, the selection of array elements located between the partition rectangle and the global boundary when randomly generating array element positions is completed by the elimination method. That is, if the array element is not located in any of the arrangeable area rectangles at the same time, the array element is set as a prohibited array element.

[0023] Furthermore, in step S3, the specific process of reallocating the regions of the pair elements is as follows:

[0024] S31: Determine the new region to be assigned to the array elements according to the sorting table of arrangeable regions.

[0025] First, determine the position of the array element in the sorting table of possible arrangement areas. Then, find the possible arrangement areas at the symmetrical positions in the table and use these areas as the new areas for the array element.

[0026] S32: Repeat step S31 to complete the determination of new regions for all array elements;

[0027] S33: Determine the movement pattern of the array elements to be assigned in the new area based on the array element arrangement in all new areas: First, count the number of array elements in different areas of the new area, and then determine the movement pattern of the array elements to be assigned based on the counted number of array elements. The principle is to make the array elements as evenly distributed as possible.

[0028] S34: Determine the movement method for all array elements to be assigned according to step S33, and move all array elements to be assigned in sequence.

[0029] S35: Check all array elements to be assigned to ensure that all array elements have been assigned; if an array element to be assigned does not find a valid position in the new area, select an area adjacent to the current area as the area to be assigned and complete the assignment according to the movement method of the current area.

[0030] Furthermore, in step S33, the movement pattern of the array elements to be assigned in the new region is determined by the array element density in different areas of the new region.

[0031] This invention also discloses a multi-constraint intelligent array antenna arrangement system, which uses the above-mentioned optimization method to optimize the element positions of a sparse array antenna, including:

[0032] The effective region division module is used to extract information on non-arrangeable regions and combine this information to divide the entire region into effective regions.

[0033] The regional array element location filtering module is used to filter array elements randomly arranged across the entire domain and sort the arrangeable regions.

[0034] The array element reallocation area module is used to reallocate areas for prohibited array elements and overlapping array elements, and to optimize the arrangement of array elements.

[0035] The fitness value output module is used to calculate or set the optimal fitness value;

[0036] The evolutionary algorithm iteration module is used to regenerate the set of random array elements and iterate and optimize them according to the evolutionary algorithm;

[0037] The control and processing module is used to issue instructions to each module to complete related actions;

[0038] The effective region division module, the region element position filtering module, the element reallocation region module, the fitness value output module, and the evolutionary algorithm iteration module are all electrically connected to the control processing module.

[0039] The present invention has the following advantages over the prior art:

[0040] 1. By using a region division method to distinguish between the isolated region (non-arrangeable region) and the arrangeable region, it is easier to bypass the isolated region in subsequent array element position optimization, thus improving the efficiency of sparse optimization after considering the isolated region.

[0041] 2. The intelligent region division method can efficiently acquire effective regions while minimizing the size of the regions, thereby improving the positioning accuracy of region allocation, increasing the success rate of allocation, and improving optimization efficiency;

[0042] 3. By employing a re-partitioning principle, the uniformity issues caused by random array arrangement are significantly improved while meeting the element spacing requirements, and the success rate of element tracking is increased. Statistical analysis of element positions within the new region reveals the element arrangement patterns within that region. The sequentially determined movement patterns effectively avoid tracking in dense areas, significantly shortening the optimization time. Furthermore, guiding elements to be assigned to areas with low element density effectively utilizes the array aperture, improving the performance of the antenna array pattern obtained through final tracking. The array antenna obtained using this method exhibits low sidelobes and high gain characteristics. When applied to spaceborne radar systems, this method effectively improves the platform's aperture utilization efficiency and increases the radar's operating range. Attached Figure Description

[0043] Figure 1 This is a flowchart of the multi-constraint intelligent array antenna arrangement method in Embodiment 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of the global region division obtained after effective region intelligent division in Embodiment 1 of the present invention;

[0045] Figure 3 This is the array element position arrangement after the array elements have been redistributed in Embodiment 2 of the present invention;

[0046] Figure 4 This is the normal φ = 0° cross-sectional pattern at 3GHz of the array antenna in Embodiment 2 of the present invention after optimization;

[0047] Figure 5 This is the array element position arrangement after the array elements have been redistributed in Embodiment 3 of the present invention;

[0048] Figure 6 This is the cross-sectional radiation pattern of the array antenna at 10GHz with a φ=0° cross-section scanning 45° after optimization in Embodiment 3 of the present invention. Detailed Implementation

[0049] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0050] Example 1

[0051] This embodiment provides a multi-constraint intelligent array antenna arrangement method, ultimately obtaining the optimal sparse array element distribution and the array radiation pattern with the lowest sidelobes. For example... Figure 1 As shown, Figure 1 This is a flowchart of the multi-constraint intelligent array antenna arrangement method in this embodiment. This embodiment is based on a genetic algorithm, which uses mathematical methods and computer simulation to transform the problem-solving process into a process similar to the crossover and mutation of chromosome genes in biological evolution. The optimization process begins with the random generation of array elements in the arrangeable area, followed by the division and filtering of the entire area. Then, based on the characteristics of different areas, the array elements are redistributed and their positions optimized.

[0052] This embodiment solves the above-mentioned technical problems through the following technical solution, which includes the following steps:

[0053] S1: Intelligent Division of Effective Region

[0054] like Figure 2 As shown, Figure 2This is a schematic diagram of the whole-domain region division obtained after effective region intelligent division in this embodiment; information on non-arrangeable regions is extracted, and the whole-domain region is effectively divided in combination with the information. The division principle is to minimize the number of regions occupied by all non-arrangeable regions and to minimize the area of ​​the divided regions.

[0055] S2: Filtering of Array Element Positions

[0056] Based on the defined effective regions, filter the array elements that are randomly arranged across the entire region; if an array element is located in a region that cannot be arranged, it is marked as a prohibited array element; if an array element is located in a region that can be arranged and the distance between adjacent array elements is less than the size of two array elements, it is marked as an overlapping array element; count the number of array elements in all regions, and arrange the regions that can be arranged according to this number, sorting them from largest to smallest array element count to obtain a sorting table of regions that can be arranged.

[0057] S3: Array element redistribution area

[0058] The prohibited array elements and overlapping array elements selected in step S2 are reassigned to regions so that all these array elements are located within the arrangeable regions; after determining the regions, the arrangement positions of the above array elements are optimized so that they do not overlap with all other array elements.

[0059] S4: Fitness Output

[0060] Based on the array element arrangement positions obtained in step S3, calculate or set the optimization fitness value;

[0061] S5: Evolutionary Algorithm Iteration

[0062] Regenerate the random array element positions and repeat steps S1 to S4, iterating and optimizing according to the evolutionary algorithm.

[0063] Furthermore, in step S1, when intelligently dividing the entire domain, if the shape of the entire domain envelope is not a rectangle, a number of rectangle fitting methods can be used for division; for example, when the shape of the entire domain is a circle, a number of rectangles are used to fit the circle, and the vertices of the edge rectangle (the two vertices of the edge rectangle located on the outer perimeter) are inscribed in the boundary of the circle. The area enclosed by the edge rectangle and the boundary of the circle is similar to a fan shape, and this area is not included in the area that can be arranged.

[0064] Furthermore, in step S1, when intelligently partitioning the entire domain, it is proposed to divide the entire domain into multiple identical rectangular regions, with dimensions L and W respectively. These two parameters are used as optimization variables for partitioning optimization: First, set initial L and W, and use these dimensions to partition the entire domain to obtain numerous rectangular partitions. After partitioning, calculate the number of partitions Ns occupied by all non-arrangeable regions, and change L and W to continue iterative optimization until the fitness function obtains the optimal solution; the fitness function can be set according to the following formula:

[0065] fitness = 0.5 × LW + 0.5 × N s

[0066] The optimization objective is to minimize the fitness function value. The divided region is a rectangle, determined by L and W. Both parameters are set as optimization variables, and their minimum values ​​are taken. The smaller the values ​​of L and W, the smaller the area of ​​the rectangle, and thus the more accurate the fitted shape.

[0067] In this embodiment, in step S2, the two-dimensional coordinate range of each partition is obtained according to the partition size obtained in step S1, and all partitions are numbered; the position of each array element is partitioned and the number of the partition where the array element is located is obtained in turn; when the array element is located in the arrangeable area, it is determined whether the distance between the array element and the other array elements meets the distance requirement; if the distance requirement is not met, it is marked as an overlapping array element.

[0068] Furthermore, in step S2, when the overall shape is not rectangular, the selection of array elements located between the partition rectangle and the overall boundary when randomly generating array element positions is completed by the elimination method. That is, if the array element is not located in any of the arrangeable area rectangles at the same time, the array element is set as a prohibited array element.

[0069] Furthermore, in step S3, the specific process of reallocating the regions of the pair elements is as follows:

[0070] S31: Determine the new region to be assigned to the array elements according to the sorting table of arrangeable regions.

[0071] First, determine the position of the array element in the sorting table of possible arrangement areas. Then, to ensure the uniformity of array element distribution, find the possible arrangement areas at symmetrical positions in the table and use these areas as the new areas for the array element.

[0072] S32: Repeat step S31 to complete the determination of new regions for all array elements;

[0073] S33: Determine the movement pattern of the array elements to be assigned in the new area based on the array element arrangement in all new areas: First, count the number of array elements in different areas of the new area, and then determine the movement pattern of the array elements to be assigned based on the counted number of array elements. The principle is to make the array elements as evenly distributed as possible.

[0074] S34: According to the method in step S33, determine the movement mode of all array elements to be assigned, and move all array elements to be assigned in sequence.

[0075] S35: Check all array elements to be assigned to ensure that all array elements have been assigned; if an array element to be assigned does not find a valid position in the new area, select an area adjacent to the current area as the area to be assigned and complete the assignment according to the movement method of the current area.

[0076] Furthermore, in step S33, the movement pattern of the array elements to be assigned in the new region is determined by the array element density in different areas of the new region. For example, when the number of array elements in the upper half of the new region is twice the number of array elements in the lower half, the array elements to be assigned move from bottom to top in the new region to find a suitable position, and vice versa. When the number of array elements in the left half of the new region is twice the number of array elements in the right half, the array elements to be assigned move from right to left in the new region to find a suitable position, and vice versa. When the difference between the number of array elements on both sides is less than 30% of the number of array elements on one side, the array elements to be assigned move from the lower left to the upper right to find a suitable position.

[0077] Example 2

[0078] This embodiment optimizes the sidelobe level of the radiation pattern of a rectangular aperture antenna with a length and width of 400mm and 370mm, respectively, and sets the x-axis and y-axis as follows: Figure 3 As shown. The antenna array to be optimized operates in the 2–4 GHz frequency band, with 40 elements, each 30 mm in size. There are three non-arrangeable regions in the entire area. All elements are deployed as independent units in the remaining regions, with an element movement time of 2 mm along both the x and y axes in the new regions. The radiation pattern to be optimized is the normal plane of the antenna array with a φ = 0° cross-section, and the target sidelobes are below -10 dB. During the optimization process, the parameters to be optimized consist of the position coordinates of the 40 elements. A set of randomly generated elements is used as the initial value in the optimization process. Region division, intelligent screening, and element re-partitioning operations are performed sequentially until the generated element coordinates are valid. During optimization, a fitness function is constructed based on the antenna's target sidelobes. The initial element coordinates and the fitness function are substituted into the genetic algorithm for calculation. During the process, all newly generated element coordinates need to undergo region division, intelligent screening, and element re-partitioning operations. The population size is set to 400, and the number of optimization iterations is set to 100.

[0079] like Figures 3-4 As shown, Figure 3 This is the array element position arrangement after the array elements have been redistributed in this embodiment. Figure 4 This is the normal φ = 0° cross-sectional pattern of the array antenna at 3GHz in this embodiment after optimization.

[0080] As can be seen from the above, the optimization process in this embodiment takes less than 4 hours, the normal sidelobe level of the optimized array is below -14dB, and no grid lobes appear within the ±90° range.

[0081] Example 3

[0082] This embodiment optimizes the sidelobe level of a circular aperture antenna with a radius of 0.6m, setting the x-axis and y-axis as follows: Figure 5As shown. The antenna array to be optimized operates in the 8–12 GHz frequency band, with 100 elements and an element size of 24.5 mm. There are 7 circular non-arrangeable regions in the entire area. All elements are arranged as independent units in the remaining regions, and the element movement time interval along the x and y axes is 2 mm. The radiation pattern to be optimized is a 45° cross-section of the antenna array with a φ = 0° scan, and the target sidelobes are below -10 dB. During the optimization process, the parameters to be optimized consist of the position coordinates of the 100 elements. A set of elements is randomly generated as the initial value and substituted into the optimization process. Region division, intelligent screening, and element re-partitioning operations are performed sequentially until the generated element coordinates are valid. During optimization, a fitness function is constructed based on the antenna's target sidelobes. The initial values ​​of the element coordinates and the fitness function are substituted into the genetic algorithm for calculation. During the process, the newly generated element coordinates all need to undergo region division, intelligent screening, and element re-partitioning operations. The population size is set to 200, and the number of optimization iterations is set to 100.

[0083] like Figures 5-6 As shown, Figure 5 This is the array element position arrangement after the array elements have been redistributed in this embodiment. Figure 6 This is the cross-sectional radiation pattern of the array antenna at 10GHz with a φ=0° cross-section scanning 45° after optimization in this embodiment.

[0084] As can be seen from the above, the optimization process in this embodiment takes less than 6 hours, the normal sidelobe level of the optimized array is below -14dB, and no grid lobes appear within the ±90° range.

[0085] In summary, the multi-constraint intelligent array antenna arrangement method in the above embodiments adopts the principle of re-partitioning, which significantly improves the performance such as poor uniformity caused by random arrangement while meeting the element spacing requirements, and at the same time improves the success rate of element tracking. By statistically analyzing the element positions in the new region, the arrangement rules of the elements within the range are obtained. The sequentially determined movement rules can effectively avoid tracking in dense areas, significantly shortening the optimization time. At the same time, guiding the elements to be assigned to areas with low element density can effectively utilize the array aperture and improve the performance of the antenna array radiation pattern obtained by the final tracking. The array antenna obtained by this method can have low sidelobes and high gain characteristics. When this method is applied to a spaceborne radar system, it can effectively improve the platform's aperture utilization efficiency and increase the radar's working range.

[0086] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for arranging multi-constraint intelligent array antennas, characterized in that, Includes the following steps: S1: Effective Area Division Extract information on non-arrangeable areas, and combine the information to effectively divide the entire region. The division principle is to minimize the number of areas occupied by all non-arrangeable areas and to minimize the area of ​​the divided regions. S2: Filtering of Array Element Positions Based on the defined effective regions, filter the array elements that are randomly arranged across the entire region; if an array element is located in a region that cannot be arranged, it is marked as a prohibited array element; if an array element is located in a region that can be arranged and the distance between adjacent array elements is less than the size of two array elements, it is marked as an overlapping array element; count the number of array elements in all regions, and arrange the regions that can be arranged according to this number, sorting them from largest to smallest array element count to obtain a sorting table of regions that can be arranged. S3: Array element redistribution area The prohibited array elements and overlapping array elements selected in step S2 are reassigned to regions so that all these array elements are located within the arrangeable regions; after determining the regions, the arrangement positions of the above array elements are optimized so that they do not overlap with all other array elements. S4: Fitness Output Based on the array element arrangement positions obtained in step S3, and based on the area of ​​the divided region and the number of regions occupied by all non-arrangeable regions, calculate or set the optimization adaptation value. S5: Evolutionary Algorithm Iteration Regenerate the random array element positions and repeat steps S1 to S4. Iterate and optimize according to the evolutionary algorithm, with the optimization objective being to minimize the fitness function.

2. The method for arranging a multi-constraint intelligent array antenna according to claim 1, characterized in that: In step S1, if the shape of the global envelope is not a rectangle when dividing the global domain into effective regions, multiple rectangle fitting methods are used for division.

3. The method for arranging a multi-constraint intelligent array antenna according to claim 2, characterized in that: In step S1, the dimensions of a single rectangular region are L and W, respectively. These two parameters are used as optimization variables for partitioning optimization: First, set initial L and W, and use these dimensions to divide the entire region to obtain numerous rectangular partitions. After partitioning, calculate the number of partitions Ns occupied by all non-arrangeable regions, change L and W to continue iterative optimization until the fitness function obtains the optimal solution.

4. The method for arranging a multi-constraint intelligent array antenna according to claim 3, characterized in that: The fitness function is set according to the following formula: fitness=0.5×LW+0.5×N s The optimization objective is to minimize the fitness function value.

5. The method for arranging a multi-constraint intelligent array antenna according to claim 1, characterized in that: In step S2, based on the partition size obtained in step S1, the two-dimensional coordinate range of each partition is obtained, and all partitions are numbered; the position of each array element is partitioned sequentially to obtain the partition number of the array element; when the array element is located in the arrangeable area, it is determined whether the distance between the array element and the other array elements meets the distance requirement; if the distance requirement is not met, it is marked as an overlapping array element.

6. The method for arranging a multi-constraint smart array antenna according to claim 1, characterized in that: In step S2, when the global envelope shape is not rectangular, the selection of array elements located between the partition rectangle and the global boundary when randomly generating array element positions is completed by the elimination method. That is, if the array element is not located in any of the arrangeable area rectangles at the same time, the array element is set as a prohibited array element.

7. The method for arranging a multi-constraint intelligent array antenna according to claim 4, characterized in that: In step S3, the specific process of reallocating the region for the pair elements is as follows: S31: Determine the new region to be assigned to the array elements according to the sorting table of arrangeable regions. First, determine the position of the array element in the sorting table of possible arrangement areas. Then, find the possible arrangement areas at the symmetrical positions in the table and use these areas as the new areas for the array element. S32: Repeat step S31 to complete the determination of new regions for all array elements; S33: Determine the movement pattern of the array elements to be assigned in the new area based on the array element arrangement in all new areas: First, count the number of array elements in different areas of the new area, and then determine the movement pattern of the array elements to be assigned based on the counted number of array elements. The principle is to make the array elements as evenly distributed as possible. S34: Determine the movement method for all array elements to be assigned according to step S33, and move all array elements to be assigned in sequence. S35: Check all array elements to be assigned to ensure that all array elements have been assigned; if an array element to be assigned does not find a valid position in the new area, select an area adjacent to the current area as the area to be assigned and complete the assignment according to the movement method of the current area.

8. The method for arranging a multi-constraint intelligent array antenna according to claim 7, characterized in that: In step S33, the movement pattern of the array elements to be assigned in the new region is determined by the array element density in different regions of the new region.

9. A multi-constraint intelligent array antenna arrangement system, characterized in that, Optimizing the element positions of a sparse array antenna using the method described in any one of claims 1 to 8 includes: The effective region division module is used to extract information on non-arrangeable regions and combine this information to divide the entire region into effective regions. The regional array element location filtering module is used to filter array elements randomly arranged across the entire domain and sort the arrangeable regions. The array element reallocation area module is used to reallocate areas for prohibited array elements and overlapping array elements, and to optimize the arrangement of array elements. The fitness value output module is used to calculate or set the optimal fitness value; The evolutionary algorithm iteration module is used to regenerate the set of random array elements and iterate and optimize them according to the evolutionary algorithm; The control and processing module is used to issue instructions to each module to complete related actions; The effective region division module, the region element position filtering module, the element reallocation region module, the fitness value output module, and the evolutionary algorithm iteration module are all electrically connected to the control processing module.

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