A method and system for designing a matrix optical cell based on a genetic algorithm

Abstract

This invention relates to the field of optical sensing and measurement technology, specifically to a design method and system for a matrix-type optical absorption cell based on a genetic algorithm. The method is as follows: First, a parameterized model of the matrix-type optical absorption cell is constructed, and the two side mirrors are divided into partitions. A genetic algorithm gene encoding sequence is constructed using the local curvature center of each partition as the optimization variable. A recursive relationship is established based on the law of light reflection, and the light propagation path is calculated successively, while optical performance indicators are statistically analyzed. A multi-objective comprehensive evaluation fitness function integrating indicators such as reflection count and spot coverage is constructed. An improved genetic algorithm incorporating partition-aware mutation and adaptive population update mechanisms is used for global iterative optimization. Finally, the optimal parameter combination and the corresponding light path and spot distribution characteristics are output. This invention can achieve automated optimization design of the absorption cell structure, improve design efficiency, and simultaneously optimize the effective optical path and mirror utilization, enhancing global optimization stability.

Description

Technical Field

[0001] This invention relates to the field of optical sensing and measurement technology, and in particular to a matrix-type optical absorption cell design method and system based on genetic algorithms. Background Technology

[0002] Laser absorption spectroscopy is widely used in gas sensor systems due to its advantages such as high sensitivity, fast response speed, and calibration-free operation. According to Beer-Lambert's law, the sensor's detection sensitivity is related to the optical path length, and the multipass gas cell is a key optical component for achieving long optical path detection.

[0003] Currently, matrix-type absorbers are a representative type of long-path multi-pass absorber structure. Traditional matrix-type absorbers (MMS) achieve a matrix-shaped light spot distribution only on one side of the mirror, and the maximum number of revisits at a single location is limited, thus restricting further improvements in mirror utilization and effective optical path. Furthermore, MMS are typically based on a confocal cavity model, and there are complex nonlinear coupling relationships between the mirror curvature center parameter, reflection point distribution, number of reflections, and effective optical path, making it difficult to directly solve for the light path using analytical methods. In engineering applications, MMS design often relies on empirical parameters or simple parameter traversal methods, making it difficult to quickly and efficiently select structural parameter combinations that meet the target requirements. Therefore, it is necessary to propose a method for optimizing the parameters of matrix-type optical absorbers to improve mirror utilization, extend the effective optical path, and enhance parameter selection efficiency.

[0004] Therefore, this invention proposes a matrix-type optical absorption cell design method and system based on genetic algorithms to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a matrix-type optical absorption cell design method and system based on genetic algorithms. This invention can improve the design efficiency and core performance of optical absorption cells and is more stable in global optimization.

[0006] On the one hand, the technical solution of this invention to solve the technical problem is a matrix-type optical absorption cell design method based on genetic algorithm, including the following steps: S1. Construct a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and construct the genetic algorithm gene coding sequence corresponding to the design parameters. S2. Select any parameter to be optimized in the genetic algorithm gene coding sequence as a candidate parameter combination. After setting the initial reflection point, establish a recursive relationship according to the law of light reflection. Calculate the propagation path of light between the two mirrors one after another until the preset path termination condition is met and the path calculation ends. Statistically obtain the optical performance index corresponding to the parameter combination. S3. Construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design, and perform global optimal parameter iterative optimization based on genetic algorithm until the maximum number of iterations is reached; S4. Output the final global optimal parameter combination, as well as its corresponding ray path and spot distribution characteristics.

[0007] The parameterized model of the matrix optical absorption cell is constructed in S1 as follows: S1.1 Let the distance between the left and right mirrors of the absorption cell be d. Divide the left and right mirrors of the absorption cell into four regions, denoted as M1, M2, M3, and M4, and divide the right mirror into four regions, denoted as M5, M6, M7, and M8. Take the set of integer coordinates of each region as the usable range of the light spot. S1.2 Construct the genetic algorithm gene coding sequence corresponding to the design parameters, using the local curvature center parameter corresponding to each region as the parameter to be optimized, and construct the gene coding sequence based on the parameter to be optimized. ; , in, Represent each region The x and y coordinates of the center of curvature, .

[0008] S2 is as follows: (1) Set the initial reflection point. Based on the preset incident light position and incident direction, determine the incident point of the light on the reflector. and initial reflection point Candidate parameter combinations The coordinates of the local curvature center of each mirror section are used as input. A recursive relationship is established according to the law of reflection of light. Subsequent reflection points are calculated and recorded in sequence, thus forming the propagation path of light between the two mirrors. (2) The preset path termination condition is: when the coordinates of the reflection point exceed the usable range of the light spot, and the reflection point and the incident point... When the coordinates coincide or the number of reflections equals the preset maximum number of reflections, the path calculation ends, and the sequence of reflection points is obtained. , ... }, number of reflections The number of reflection points in the sequence; (3) After the path calculation is completed, the optical performance indicators are statistically analyzed, including the number of reflections. And the distribution of reflection points.

[0009] S3 is as follows: S3.1 Based on the optical performance index obtained in S2, the fitness value corresponding to each candidate parameter combination is calculated through the multi-objective comprehensive evaluation fitness function; S3.2, Based on fitness value Select and retain high-quality individuals, update the individual historical best parameters and global historical best parameters of the current genetic algorithm gene coding sequence, and let the i-th The generation The candidate parameter combinations for each individual are: The current fitness value of this individual is , No. Individuals up to the [number]th The optimal combination of individual historical parameters for a generation is As of the The global historical optimal parameter combination of the generation is ,when ,renew ,when ,renew ; S3.3, based on the partition-aware mutation mechanism and the stagnation detection-driven adaptive mutation and population update mechanism, performs population selection, crossover, and mutation operations to generate the next generation population, and then executes S3.1 until the maximum number of iterations is reached to obtain the final globally optimal parameter combination.

[0010] In S3.1, a multi-objective comprehensive evaluation fitness function is constructed as follows: , in, This represents the overall fitness value of the parameter combination P; , , , These are the weight coefficients for each item; Indicates the effective optical path evaluation item. This indicates the light spot coverage evaluation item. This indicates the evaluation item with the highest number of repeated visits. This indicates a path out-of-bounds evaluation item.

[0011] The calculation formulas for each evaluation item are as follows: , in, This indicates the number of light reflections corresponding to the parameter combination P. Indicates the distance between the reflectors in the absorption cell. Indicates the preset optical path; = , Among them, the number of coordinates occupied is the reflection point sequence { , ... The number of unique coordinates in the} is equal to the number of coordinates contained within the usable range of the light spot; , in, Represented as a sequence of reflection points { , ... The maximum number of repetitions of the repeating coordinates in the array; , This indicates that the light path has crossed the boundary. This indicates that the ray path does not cross the boundary.

[0012] The partition-aware mutation mechanism in S3.3 is as follows: Set up each mirror zone Utilization level evaluation ,according to The mutation probability of the corresponding parameter for this partition is dynamically adjusted. The mutation probability is calculated using the following formula: , in, For the first The probability of variation of parameters corresponding to each mirror partition; The basic mutation probability is preset during the initialization phase of the genetic algorithm and determined based on population size, code length, expected search intensity, and experimental experience. This represents the adjustment coefficient. Indicates the first Evaluation of the utilization rate of each mirrored zone.

[0013] The stagnation detection-driven adaptive mutation and population update mechanism in S3.3 is as follows: The system monitors the update of the global optimal fitness value in real time during the iteration process. When the optimal optical path does not improve after a preset number of iterations, it is determined that the evolution has stalled, triggering an increase in the intensity of adaptive mutation. The system also enhances population diversity by restarting the population or injecting random individuals.

[0014] On the other hand, the present invention also provides a matrix optical absorption cell design system based on a genetic algorithm, for implementing a matrix optical absorption cell design method based on a genetic algorithm, including: The model building and initialization module is used to build a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and build the genetic algorithm gene coding sequence corresponding to the design parameters. The ray path calculation module is used to calculate the ray path. For any candidate parameter combination in the population, after setting the initial reflection point, it establishes a recursive relationship based on the law of reflection of light, and calculates the propagation path of the ray between the two mirrors one after another. After completing the path termination judgment, it obtains the optical performance index corresponding to the parameter combination. The iterative optimization module is used to construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design. It performs iterative optimization of the global optimal parameters based on an improved genetic algorithm until the maximum number of iterations is reached. It calculates the fitness value corresponding to each candidate parameter combination and updates the individual historical optimal parameters and the global historical optimal parameters of the current population. Based on the partition-aware mutation mechanism, stagnation detection-driven adaptive mutation and population update mechanism, it performs selection, crossover and mutation operations of the population to generate the next generation population until the maximum number of iterations is reached, and obtains the final global optimal parameter combination. The results output module is used to output the final global optimal parameter combination, as well as its corresponding ray path, effective optical path and spot distribution characteristics.

[0015] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. The above technical solutions have the following advantages or beneficial effects: This invention constructs a parameterized model, using the local curvature centers of the two reflector partitions as optimization variables and encoding them genetically. This replaces the traditional design mode that relies on manual experience and repeated trial and error, enabling calculable and iterative automated structural design, thereby improving the efficiency of parameter search and scheme generation. Furthermore, this invention establishes a recursive relationship based on the law of light reflection, accurately calculates light paths, and constructs a multi-objective fitness function that integrates indicators such as reflection count, light spot coverage, and path boundary violations. This function can simultaneously consider both effective optical path improvement and light spot distribution uniformity, obtaining an optimal structure with more reflections, longer optical paths, and more efficient mirror utilization. This invention innovatively introduces a partition-aware mutation mechanism, combining stagnation detection-driven adaptive mutation and population update strategies to dynamically adjust the mutation probability of each partition parameter, avoiding the algorithm from getting trapped in local optima and thus improving the stability and reliability of the optimization results. The method of this invention does not rely on a fixed mirror structure, can adapt to different sizes, partitioning methods, and target optical path requirements, and can output optimal parameters, light paths, and light spot distributions. It is accompanied by a complete design process system, facilitating software implementation and engineering application. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0018] Figure 2 This is a diagram showing the distribution trajectory of the light spots on the left and right reflecting mirrors.

[0019] Figure 3 This is a heatmap showing the number of visits to the light spot on the left reflector.

[0020] Figure 4 This is a heatmap showing the number of visits to the light spot on the right-side reflector. Detailed Implementation

[0021] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0022] Example 1 like Figure 1 As shown, a matrix-type optical absorption cell design method based on genetic algorithm includes the following steps: S1. Construct a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and construct the genetic algorithm gene coding sequence corresponding to the design parameters. S2. Select any parameter to be optimized in the genetic algorithm gene coding sequence as a candidate parameter combination. After setting the initial reflection point, establish a recursive relationship according to the law of light reflection. Calculate the propagation path of light between the two mirrors one after another until the preset path termination condition is met and the path calculation ends. Statistically obtain the optical performance index corresponding to the parameter combination. S3. Construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design, and perform global optimal parameter iterative optimization based on genetic algorithm until the maximum number of iterations is reached; S4. Output the final global optimal parameter combination, as well as its corresponding ray path and spot distribution characteristics.

[0023] In a specific implementation, a parameterized model of a matrix-type optical absorption cell is constructed in S1, as follows: S1.1 Let the distance between the left and right mirrors of the absorption cell be d. Divide the left and right mirrors of the absorption cell into four regions, denoted as M1, M2, M3, and M4, and divide the right mirror into four regions, denoted as M5, M6, M7, and M8. Take the set of integer coordinates of each region as the usable range of the light spot. S1.2 Construct the genetic algorithm gene coding sequence corresponding to the design parameters, using the local curvature center parameter corresponding to each region as the parameter to be optimized, and construct the gene coding sequence based on the parameter to be optimized. ; , in, Represent each region The x and y coordinates of the center of curvature, .

[0024] Step S1 can improve the design efficiency of the matrix optical absorption cell structure parameters. By constructing a parameterized model of the matrix optical absorption cell, the two side mirrors are divided into multiple mirror partitions. The local curvature center parameters corresponding to each partition are used as optimization variables to establish a genetic algorithm gene coding sequence corresponding to the structural parameters. This transforms the original optical absorption cell structure design process, which relied on manual experience and repeated trial and error, into a calculable and iterative automatic optimization process, thereby improving the efficiency of structural design and parameter search. In a specific implementation, S2 is as follows: (1) Set the initial reflection point. Based on the preset incident light position and incident direction, determine the incident point of the light on the reflector. and initial reflection point Candidate parameter combinations The coordinates of the local curvature center of each mirror section are used as input. A recursive relationship is established according to the law of reflection of light. Subsequent reflection points are calculated and recorded in sequence, thus forming the propagation path of light between the two mirrors. (2) The preset path termination condition is: when the coordinates of the reflection point exceed the usable range of the light spot, and the reflection point and the incident point... When the coordinates coincide or the number of reflections equals the preset maximum number of reflections, the path calculation ends, and the sequence of reflection points is obtained. , ... }, number of reflections The number of reflection points in the sequence; (3) After the path calculation is completed, the optical performance indicators are statistically analyzed, including the number of reflections. And the distribution of reflection points.

[0025] In a specific implementation, S3 is as follows: S3.1 Based on the optical performance index obtained in S2, the fitness value corresponding to each candidate parameter combination is calculated through the multi-objective comprehensive evaluation fitness function; S3.2, Based on fitness value Select and retain high-quality individuals, update the individual historical best parameters and global historical best parameters of the current genetic algorithm gene coding sequence, and let the i-th The generation The candidate parameter combinations for each individual are: The current fitness value of this individual is , No. Individuals up to the [number]th The optimal combination of individual historical parameters for a generation is As of the The global historical optimal parameter combination of the generation is ,when ,renew ,when ,renew ; S3.3, based on the partition-aware mutation mechanism and the stagnation detection-driven adaptive mutation and population update mechanism, performs population selection, crossover, and mutation operations to generate the next generation population, and then executes S3.1 until the maximum number of iterations is reached to obtain the final globally optimal parameter combination.

[0026] By introducing a partition-aware mutation mechanism and combining it with a stagnation detection-driven adaptive mutation and population update mechanism, the mutation probability of corresponding parameters can be dynamically adjusted according to the utilization level of each mirror partition. When stagnation occurs during the optimization process, the population diversity is increased, thereby reducing the possibility of the algorithm getting stuck in local optima and improving the global optimization ability and the stability of the optimization results. In a specific implementation, the multi-objective comprehensive evaluation fitness function is constructed in S3.1 as follows: , in, Indicates parameter combination The overall fitness value; , , , These are the weight coefficients for each item; Indicates the effective optical path evaluation item. This indicates the light spot coverage evaluation item. This indicates the evaluation item with the highest number of repeated visits. This indicates a path out-of-bounds evaluation item.

[0027] The calculation formulas for each evaluation item are as follows: , in, This indicates the number of light reflections corresponding to the parameter combination P. Indicates the distance between the reflectors in the absorption cell. Indicates the preset optical path; = , Among them, the number of coordinates occupied is the reflection point sequence { , ... The number of unique coordinates in the} is equal to the number of coordinates contained within the usable range of the light spot; , in, Represented as a sequence of reflection points { , ... The maximum number of repetitions of the repeating coordinates in the array; , This indicates that the light path has crossed the boundary. This indicates that the ray path does not cross the boundary.

[0028] In a specific implementation, the partition-aware mutation mechanism in S3.3 is as follows: Set up each mirror zone Utilization level evaluation ,according to The mutation probability of the corresponding parameter for this partition is dynamically adjusted. The mutation probability is calculated using the following formula: , in, For the first The probability of variation of parameters corresponding to each mirror partition; The basic mutation probability is preset during the initialization phase of the genetic algorithm and determined based on population size, code length, expected search intensity, and experimental experience. This represents the adjustment coefficient. Indicates the first Evaluation of the utilization rate of each mirrored zone.

[0029] In a specific implementation, the stagnation detection-driven adaptive mutation and population update mechanism in S3.3 is as follows: The system monitors the update of the global optimal fitness value in real time during the iteration process. When the optimal optical path does not improve after a preset number of iterations, it is determined that the evolution has stalled, triggering an increase in the intensity of adaptive mutation. The system also enhances population diversity by restarting the population or injecting random individuals.

[0030] The parameter optimization method proposed in this invention does not rely on a single fixed mirror structure and can be adjusted according to different absorption cell sizes, mirror partitioning methods and target optical path requirements. Therefore, it has good versatility and scalability and can provide methodological support for the design of different types of matrix multi-pass absorption cells.

[0031] This invention ultimately outputs the optimal parameter combination and its corresponding ray path that satisfies the target reflection frequency range, and intuitively shows the light spot distribution trajectory on the reflector surface, providing an implementable technical means for the structural design and performance analysis of matrix-type optical absorption cells. It also presents a corresponding design system that can realize the complete process from parameter modeling and path analysis to optimized output, facilitating subsequent software implementation, program deployment, and engineering applications.

[0032] Example 2 A matrix optical absorber cell design system based on a genetic algorithm is provided to implement a matrix optical absorber cell design method based on a genetic algorithm, comprising: The model building and initialization module is used to build a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and build the genetic algorithm gene coding sequence corresponding to the design parameters. The ray path calculation module is used to calculate the ray path. For any candidate parameter combination in the population, after setting the initial reflection point, it establishes a recursive relationship based on the law of reflection of light, and calculates the propagation path of the ray between the two mirrors one after another. After completing the path termination judgment, it obtains the optical performance index corresponding to the parameter combination. The iterative optimization module is used to construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design. It performs iterative optimization of the global optimal parameters based on an improved genetic algorithm until the maximum number of iterations is reached. It calculates the fitness value corresponding to each candidate parameter combination and updates the individual historical optimal parameters and the global historical optimal parameters of the current population. Based on the partition-aware mutation mechanism, stagnation detection-driven adaptive mutation and population update mechanism, it performs selection, crossover and mutation operations of the population to generate the next generation population until the maximum number of iterations is reached, and obtains the final global optimal parameter combination. The results output module is used to output the final global optimal parameter combination, as well as its corresponding ray path, effective optical path and spot distribution characteristics.

[0033] Example 3 Based on the proposed method of partitioned parameterized modeling, establishing recursive relationships according to the law of light reflection, and improving the multi-objective optimization of genetic algorithms for matrix optical absorption cell design, this invention was validated for medium-to-long optical path requirements commonly used in industrial gas detection. Ultimately, a high-precision, high-space-utilization absorption cell structure design was achieved. The following describes the design process in conjunction with... Figures 2 to 4 Please provide a detailed explanation.

[0034] Core design metrics and parameter constraints: Geometric parameters: distance between left and right reflectors d = 30cm, side length of a single reflector 1.5cm; Performance target: Preset effective optical path length L = 300m Algorithm constraints: The curvature center coordinates of the 8 mirror partitions (left M1-M4, right M5-M8) range from 4 to 7; the usable range of mirror surface spots is a 5×5 integer coordinate matrix (corresponding to discrete sampling points on the physical surface of the mirror); The fitness weights are as follows: 0.5 corresponds to effective optical path, 0.2 corresponds to spot coverage, 0.1 corresponds to maximum repeated visits, and 0.2 corresponds to path out of bounds.

[0035] Perform the steps of the method of the present invention as follows: S1. Construct a 16-bit gene coding sequence G, and convert the curvature center parameters of the 8 partitions into individuals that can be iterated by the genetic algorithm; S2. Set the initial incident point coordinates as (1,8), and calculate the ray path successively based on the recursive formula of the confocal cavity reflection point; S3. An improved genetic algorithm with partition-aware mutation and stagnation detection adaptive update is used for iterative optimization. S4. Output the global optimal parameter combination, which is the coordinate of the curvature center of each region's reflector. By adjusting the position of the curvature center of the reflector, the corresponding light path and light spot distribution characteristics can be obtained.

[0036] Each partition corresponds to a lens, and the coordinates of the lens's curvature center correspond to the output optimal parameters. By fixing or adjusting the lens, the curvature center is brought to the corresponding position. This allows for the design of an optical absorption cell that matches the simulated light path.

[0037] The optimal path was found using the above method. The incident point coordinates are (1,8), the number of reflections is 1003, the optical path length is 300.9m, and the absorption cell volume ratio is... The light spot reflection path and the number of times the light spot is accessed are as follows: Figures 2 to 4 As shown, a total of 3 core attached figures are output, corresponding to the distribution of the light spot trajectories of the left and right mirrors and the statistics of the number of visits to the left and right light spots, respectively, which comprehensively reflect the optical performance of the absorption cell.

[0038] like Figure 2 The diagram shows the light spot distribution trajectory of the left and right reflecting mirrors, with the horizontal axis X and the vertical axis Y. The four quadrants correspond to the four mirror zones. Figure 2 The discrete points in the middle represent all the reflection points of the light rays on the mirror, and connecting them forms the projection of the light ray's propagation trajectory between the left and right mirrors. Figure 2Each point represents the position of the curvature center of each reflector. In the light spot distribution trajectory diagram of the right reflector, A(5,5) corresponds to the curvature center of reflector M1, B(5.5,6) corresponds to the curvature center of reflector M2, C(5.5,5) corresponds to the curvature center of reflector M3, and D(5.5,5.5) corresponds to the curvature center of reflector M4. In the light spot distribution trajectory diagram of the left reflector, E(5,5.5) corresponds to the curvature center of reflector M5, F(5.5,5.5) corresponds to the curvature centers of reflectors M6 and M7, and G(5.5,5) corresponds to the curvature center of reflector M8.

[0039] like Figure 3 and Figure 4 The figures show heatmaps of the number of times the light spots were accessed by the left and right mirrors, respectively. The horizontal and vertical axes correspond to integer coordinates on the surface of the mirrors. Each cell represents a usable sampling point for a light spot. The height within the cell represents the total number of times that coordinate point was accessed by light reflection. The higher the height, the higher the utilization rate of the mirror in that area.

[0040] The above experimental results verify the feasibility and superiority of the method of the present invention, which can quickly output the optimal structure of the matrix absorption cell that meets different optical path requirements, and provide a standardized technical solution for the design of optical absorption cells in fields such as gas detection and spectral analysis.

[0041] Although the specific embodiments of the invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the invention. Based on the technical solutions of the invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the invention.

Claims

1. A method for designing a matrix type optical absorption cell based on a genetic algorithm, characterized by, Includes the following steps: S1. Construct a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and construct the genetic algorithm gene coding sequence corresponding to the design parameters. The parameterized model of the matrix optical absorption cell is constructed in S1 as follows: S1.1 Let the distance between the left and right mirrors of the absorption cell be d. Divide the left and right mirrors of the absorption cell into four regions, denoted as M1, M2, M3, and M4, and divide the right mirror into four regions, denoted as M5, M6, M7, and M8. Take the set of integer coordinates of each region as the usable range of the light spot. S1.2, construct a genetic algorithm gene coding sequence corresponding to the design parameters, take the local curvature center parameter corresponding to each region as the optimization parameter, and construct a gene coding sequence based on the optimization parameter ; ， in, Represent each region The x and y coordinates of the center of curvature, ; S2. Select any parameter to be optimized in the genetic algorithm gene coding sequence as a candidate parameter combination. After setting the initial reflection point, establish a recursive relationship according to the law of light reflection. Calculate the propagation path of light between the two mirrors one after another until the preset path termination condition is met and the path calculation ends. Statistically obtain the optical performance index corresponding to the parameter combination. S3. Construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design, and perform global optimal parameter iterative optimization based on genetic algorithm until the maximum number of iterations is reached; The multi-objective comprehensive evaluation fitness function is constructed as follows: ， in, This represents the overall fitness value of the parameter combination P; , , , These are the weight coefficients for each item; Indicates the effective optical path evaluation item. This indicates the light spot coverage evaluation item. This indicates the evaluation item with the highest number of repeated visits. This indicates a path out-of-bounds evaluation item; S4. Output the final global optimal parameter combination, as well as its corresponding ray path and spot distribution characteristics.

2. The method for designing a matrix optical cell according to claim 1, wherein, S2 is as follows: (1) Set the initial reflection point. Based on the preset incident light position and incident direction, determine the incident point of the light on the reflector. and initial reflection point Candidate parameter combinations The coordinates of the local curvature center of each mirror section are used as input. A recursive relationship is established according to the law of reflection of light. Subsequent reflection points are calculated and recorded in sequence, thus forming the propagation path of light between the two mirrors. (2) The preset path termination condition is: when the coordinates of the reflection point exceed the usable range of the light spot, and the reflection point and the incident point... When the coordinates coincide or the number of reflections equals the preset maximum number of reflections, the path calculation ends, and the sequence of reflection points is obtained. , ... }, number of reflections The number of reflection points in the sequence; (3) After the path calculation is finished, the optical performance index is counted, including the number of reflections and the distribution of reflection points. and the distribution of reflection points.

3. The method for designing a matrix optical cell according to claim 1, wherein, S3 is as follows: S3.1 Based on the optical performance index obtained in S2, the fitness value corresponding to each candidate parameter combination is calculated through the multi-objective comprehensive evaluation fitness function; S3.2, Based on fitness value Select and retain high-quality individuals, update the individual historical best parameters and global historical best parameters of the current genetic algorithm gene coding sequence, and let the i-th The generation The candidate parameter combinations for each individual are: The current fitness value of this individual is , No. Individuals up to the [number]th The optimal combination of individual historical parameters for a generation is As of the The global historical optimal parameter combination of the generation is ,when ,renew ,when ,renew ; S3.3, based on the partition-aware mutation mechanism and the stagnation detection-driven adaptive mutation and population update mechanism, performs population selection, crossover, and mutation operations to generate the next generation population, and then executes S3.1 until the maximum number of iterations is reached to obtain the final globally optimal parameter combination.

4. The method for designing a matrix optical cell according to claim 3, wherein, The calculation formulas for each evaluation item are as follows: ， wherein, represents the number of light reflections corresponding to the parameter combination P, represents the distance between the absorption cell mirrors, represents the preset optical path; = ， Among them, the number of coordinates occupied is the reflection point sequence { , ... The number of unique coordinates in the} is equal to the number of coordinates contained within the usable range of the light spot; ， in, Represented as a sequence of reflection points { , ... The maximum number of repetitions of the repeating coordinates in the array; , represents that the ray path appears to be out of bounds, represents that the ray path does not appear to be out of bounds.

5. The method for designing a matrix optical cell according to claim 4, wherein, The partition-aware mutation mechanism in S3.3 is as follows: Setting each mirror surface partition of the degree of utilization evaluation quantity , according to the dynamic adjustment of the partition corresponding parameter variation probability, variation probability calculation formula as follows: ， in, For the first The probability of variation of parameters corresponding to each mirror partition; The basic mutation probability is preset during the initialization phase of the genetic algorithm and determined based on population size, code length, expected search intensity, and experimental experience. This represents the adjustment coefficient. Indicates the first Evaluation of the utilization rate of each mirrored zone.

6. The method for designing a matrix optical cell according to claim 5, wherein, The stagnation detection-driven adaptive mutation and population update mechanism in S3.3 is as follows: The system monitors the update of the global optimal fitness value in real time during the iteration process. When the optimal optical path does not improve after a preset number of iterations, it is determined that the evolution has stalled, triggering an increase in the intensity of adaptive mutation. The system also enhances population diversity by restarting the population or injecting random individuals.

7. A genetic algorithm based design system for a matrix optical cell, for implementing a genetic algorithm based design method for a matrix optical cell according to any one of claims 1 to 6, characterized in that, include: The model building and initialization module is used to build a parameterized model of a matrix-type optical absorption cell, divide the mirrors on both sides of the absorption cell into partitions, determine the design parameters to be optimized, and build the genetic algorithm gene coding sequence corresponding to the design parameters. The ray path calculation module is used to calculate the ray path. For any candidate parameter combination in the population, after setting the initial reflection point, it establishes a recursive relationship based on the law of reflection of light, and calculates the propagation path of the ray between the two mirrors one after another. After completing the path termination judgment, it obtains the optical performance index corresponding to the parameter combination. The iterative optimization module is used to construct a multi-objective comprehensive evaluation fitness function suitable for matrix-type optical absorption cell design. It performs global optimal parameter iterative optimization based on an improved genetic algorithm until the maximum number of iterations is reached. Calculate the fitness value corresponding to each candidate parameter combination, update the individual historical best parameters and the global historical best parameters of the current population; based on the partition-aware mutation mechanism, the stagnation detection-driven adaptive mutation and population update mechanism, perform population selection, crossover and mutation operations to generate the next generation population until the maximum number of iterations is reached, and obtain the final global best parameter combination; The results output module is used to output the final global optimal parameter combination, as well as its corresponding ray path, effective optical path and spot distribution characteristics.

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