A method and system for optimizing the power spectrum of synthesized beams in spectral synthesis

Abstract

This application pertains to the field of laser atmospheric transmission and discloses a method and system for optimizing the power spectrum of a spectral synthesized laser beam. The optimization method includes: acquiring laser system parameters, atmospheric environment parameters, and path geometry parameters for a preset beam transmission scenario; establishing a normalized synthesized power spectrum numerical model for sub-beam peak power control; generating a normalized synthesized power spectrum dataset; performing wave optics simulation based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain a far-field average light intensity dataset; selecting the optimal sub-beam peak power combination based on the far-field average light intensity dataset, and using the synthesized power spectrum generated by this combination as the optimal synthesized power spectrum. This technical solution considers various effects in laser atmospheric transmission. By optimizing the synthesized power spectrum, it can significantly improve the far-field average light intensity of spectral synthesized laser atmospheric transmission, thereby enhancing the application performance of the spectral synthesized laser system.

Description

Technical Field

[0001] This application belongs to the field of laser atmospheric transmission, and specifically relates to a method, system, device and medium for optimizing the power spectrum of a spectral synthesized beam. Background Technology

[0002] The atmosphere contains atmospheric molecules and aerosol particles. When the laser beam emitted by the laser system propagates through the atmosphere, it interacts with the atmosphere, producing diffraction, attenuation, and thermal halo effects. Due to these effects, the characteristics of the laser beam in the far field are affected by three types of transmission scene parameters.

[0003] Spectral synthesis technology is an effective way to improve the output power of laser systems and is a hot research topic in the field of high-energy lasers. In the design and development of spectral synthesis beams, given the emission power and number of synthesis paths, although the distribution of the synthesized power spectrum is crucial to the system's performance, there is currently a lack of an effective method to optimize the synthesized power spectrum. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method, system, device, and medium for optimizing the power spectrum of a synthesized beam.

[0005] The first aspect of this disclosure proposes a method for optimizing the power spectrum of a spectral synthesized beam, the optimization method comprising: Acquire the laser system parameters, atmospheric environment parameters, and path geometry parameters for the preset beam transmission scenario; Based on laser system parameters, atmospheric environment parameters, and path geometry parameters, a normalized combined power spectrum numerical model for sub-beam peak power modulation is established. A normalized synthesized power spectrum dataset is generated based on a numerical model of normalized synthesized power spectrum with sub-bundle peak power modulation. Wave optics simulations were performed based on normalized composite power spectrum datasets, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain far-field average light intensity datasets. The optimal sub-beam peak power combination was selected based on the far-field average light intensity dataset, and the synthesized power spectrum generated by this combination was taken as the optimal synthesized power spectrum.

[0006] According to a preferred embodiment of this disclosure, the normalized combined power spectrum dataset generated by the normalized combined power spectrum numerical model based on sub-beam peak power modulation includes: Based on the number of sub-bundles, multiple sets of random arrays with specific statistical distributions are generated within a preset range, and the random numbers of the random arrays are used as the peak power of each sub-bundle. Normalized composite power spectrum data are calculated based on random arrays and a normalized composite power spectrum numerical model. The normalized composite power spectrum is calculated repeatedly based on the random arrays described in each group, and the results are combined to form a normalized composite power spectrum dataset.

[0007] According to a preferred embodiment of this disclosure, the step of establishing a normalized combined power spectrum numerical model for sub-beam peak power modulation based on laser system parameters, atmospheric environment parameters, and path geometry parameters includes: The wavelength range is divided into multiple intervals based on the maximum and minimum wavelength values, and the center wavelength of each interval is calculated. A numerical model of the sub-beam power spectrum is established based on the sub-beam peak power, the center wavelength of the sub-beam, the full width at half maximum (FWHM) of the sub-beam, and the center wavelength of each of the aforementioned intervals in the laser system parameters. Based on the number of sub-beam paths and the numerical model of sub-beam power spectrum, normalization processing is performed to establish a normalized composite power spectrum numerical model.

[0008] According to a preferred embodiment of this disclosure, the step of performing wave optics simulation based on a normalized composite power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain a far-field average light intensity dataset includes: The equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance are calculated based on the normalized composite power spectrum. The equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance are input into the wave optics simulation system, and the far-field average light intensity is calculated in combination with atmospheric environmental parameters. The far-field average light intensity is calculated repeatedly based on the normalized synthesized power spectrum in the normalized synthesized power spectrum dataset to form a far-field average light intensity dataset.

[0009] According to a preferred embodiment of this disclosure, the step of selecting the optimal sub-beam peak power combination based on the far-field average light intensity dataset and using the synthesized power spectrum generated by this combination as the optimal synthesized power spectrum includes: The optimal sub-beam peak power combination was selected based on the far-field average light intensity dataset. The optimal combined power spectrum is calculated based on the optimal sub-beam peak power combination.

[0010] According to a preferred embodiment of this disclosure, the laser system parameters include: emission power, beam radius, minimum wavelength value of power spectrum distribution, maximum wavelength value of power spectrum distribution, number of sub-beams, center wavelength of power spectrum of each sub-beam, and full width at half maximum (FWHM) of power spectrum of each sub-beam; the atmospheric environment parameters include: temperature, transverse wind speed of the optical path, absorption coefficient, and extinction coefficient; the path geometry parameters include: focal length.

[0011] To address the aforementioned technical problems, a second aspect of this disclosure proposes a system for optimizing the power spectrum of a spectral synthesized beam, the optimization system comprising: The data acquisition module is used to acquire laser system parameters, atmospheric environment parameters, and path geometry parameters for a preset beam transmission scenario; The model building module is used to establish a normalized combined power spectrum numerical model for sub-beam peak power modulation based on laser system parameters, atmospheric environment parameters, and path geometry parameters. The data simulation module is used to generate a normalized synthesized power spectrum dataset based on a normalized synthesized power spectrum numerical model with sub-bundle peak power modulation. The optical simulation module is used to perform wave optical simulation based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain the far-field average light intensity dataset. The power spectrum optimization module is used to select the optimal combination of sub-beam peak power based on the far-field average light intensity dataset, and the resulting composite power spectrum is taken as the optimal composite power spectrum.

[0012] According to a preferred embodiment of this disclosure, the data simulation module is specifically used to generate multiple sets of random arrays with a specific statistical distribution within a preset range based on the number of sub-bundles, and to use the random numbers of the random arrays as the peak power of each sub-bundle; to calculate the normalized composite power spectrum data based on the random arrays and the normalized composite power spectrum numerical model; and to repeatedly calculate the normalized composite power spectrum based on each set of random arrays, and combine them to form a normalized composite power spectrum dataset.

[0013] To address the aforementioned technical problems, a third aspect of this disclosure provides an electronic device, comprising: Processor; and A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the above embodiments.

[0014] To address the aforementioned technical problems, a fourth aspect of this disclosure provides a computer storage medium that stores one or more programs, which, when executed by a processor, implement the method described in any of the above embodiments.

[0015] Compared with existing technologies, this application has the following advantages: A normalized combined power spectrum numerical model for sub-beam peak power modulation is established by acquiring laser system parameters, atmospheric environment parameters, and path geometry parameters; a normalized combined power spectrum dataset is generated based on this model; optical simulation is performed using this dataset in conjunction with atmospheric environment parameters and path geometry parameters to obtain the far-field average light intensity data of each combined beam; finally, the optimal sub-beam peak power combination is selected based on the far-field average light intensity data, and the combined power spectrum generated by this combination is taken as the optimal combined power spectrum. This technical solution effectively considers various effects in laser atmospheric transmission. By optimizing the distribution of the combined power spectrum, it can significantly improve the average light intensity of the spectral synthesized laser reaching the far field during atmospheric transmission, thereby enhancing its application effectiveness.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of a method for optimizing the power spectrum of a spectral synthesized beam according to an embodiment of the present disclosure is shown. Figure 2 A second schematic flowchart of a beam power spectrum optimization method for spectral synthesis according to an embodiment of the present disclosure is shown. Figure 3 A schematic flowchart of a method for optimizing the power spectrum of a beam in spectral synthesis according to an embodiment of the present disclosure is shown in part three. Figure 4 A schematic flowchart of a method for optimizing the power spectrum of a beam in spectral synthesis according to an embodiment of the present disclosure is shown in Figure 4. Figure 5 A schematic flowchart of a beam power spectrum optimization method for spectral synthesis according to an embodiment of the present disclosure is shown in Figure 5. Figure 6 A schematic diagram showing the absorption coefficient and extinction coefficient of light beams of different wavelengths according to embodiments of the present disclosure is provided. Figure 7 A schematic diagram of the 10th set of normalized synthesized power spectrum data according to an embodiment of the present disclosure is shown; Figure 8 A schematic diagram showing the distribution of the square of the complex amplitude of the 10th group of spectral synthesized beam sources according to an embodiment of the present disclosure is shown; Figure 9 A schematic diagram of the far-field average light intensity distribution corresponding to the 10th group of complex amplitudes according to an embodiment of the present disclosure is shown. Figure 10 A schematic diagram of a far-field average light intensity dataset obtained from 1,000 sets of normalized composite power spectrum data according to an embodiment of the present disclosure is shown. Figure 11 A schematic diagram of the success rate spectrum of the 564th combination according to an embodiment of the present disclosure is shown; Figure 12 A schematic diagram of a spectral synthesis beam power spectrum optimization system according to an embodiment of the present disclosure is shown. Figure 13 A schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The same reference numerals in the accompanying drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of the same or similar elements, components, or parts may be omitted below. It should also be understood that although qualifiers such as first, second, third, etc., indicating numbers may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these qualifiers. That is, these qualifiers are only used to distinguish one from another. For example, a first device may also be referred to as a second device, without departing from the essence of the technical solution of this disclosure. Furthermore, the terms "and / or" and "and / or" refer to all combinations including the first or more of the listed items.

[0021] Please see Figure 1 , Figure 1 This is one of the flowcharts of a method for optimizing the power spectrum of a synthesized beam, as disclosed in this publication. Figure 1 As shown, the optimization methods include: S11. Obtain the laser system parameters, atmospheric environment parameters, and path geometry parameters of the preset beam transmission scenario.

[0022] In this embodiment, before conducting in-depth analysis and research on beam transmission characteristics, it is necessary to comprehensively and systematically collect, organize, and rationally set various key parameters involved in the actual beam transmission scenario. These parameters mainly cover three core aspects: laser system parameters (such as emission power, beam radius, etc.), atmospheric environment parameters (such as temperature, wind speed, etc.), and path geometry parameters (such as transmission distance, focal length, etc.). The accurate acquisition and scientific setting of the above-mentioned parameters can provide sufficient and necessary basic data support for the subsequent construction of high-precision power spectrum numerical models, the conduct of realistic optical transmission simulations, and the targeted optimization of power spectrum distribution schemes. This ensures that the entire power spectrum optimization process closely follows the actual beam transmission conditions, effectively avoids analytical deviations caused by missing or distorted parameters, and ultimately significantly improves the practicality, reliability, and accuracy of the optimization results in practical engineering applications.

[0023] In this embodiment, the laser system parameters include: emission power. , beam radius Minimum wavelength value of power spectrum distribution Maximum wavelength value of power spectrum distribution Sub-bundle path number Center wavelength of power spectrum of each sub-beam Full width at half maximum (FWHM) of the power spectrum of each sub-beam ; Atmospheric environmental parameters include: temperature Lateral wind speed along the optical path absorption coefficient Extinction coefficient ; Geometric path parameters include: focal length .

[0024] S12. Based on laser system parameters, atmospheric environment parameters, and path geometry parameters, a normalized combined power spectrum numerical model for sub-beam peak power modulation is established.

[0025] In this embodiment, the entire wavelength range covered by the laser system is systematically divided into several independent and non-overlapping wavelength sub-intervals according to pre-defined rules. After the interval division is completed, a specific mathematical algorithm is used to accurately determine the corresponding center wavelength value for each wavelength sub-interval. For example, a simple and effective method can be adopted: taking the arithmetic mean of the wavelength values ​​at both ends of the interval. This achieves discretization sampling of the original continuous power spectrum in the wavelength dimension. The center wavelengths and their corresponding power spectrum values ​​obtained from this discretization sampling process constitute the essential basic data support for subsequent optimization of the synthesized power spectrum.

[0026] Building upon this foundation, we further utilize three key characteristic parameters—center wavelength, peak power, and full width at half maximum (FWHM)—for each sub-beam as core input variables. Simultaneously, we reasonably assume that the power spectrum of each sub-beam satisfies a specific mathematical distribution function. For example, we can assume it follows a Gaussian or Lorentz function distribution, or determine its distribution by curve fitting of discrete data points obtained from actual experimental measurements, or obtain its precise distribution expression through rigorous formula derivation based on the fundamental physical principles of laser operation. Supported by these assumptions and input conditions, a systematic numerical theoretical model is constructed using rigorous mathematical modeling methods. This model accurately describes the characteristics of the combined power spectrum formed by the synthesis of multiple sub-beams. Furthermore, the power spectrum distribution of the synthesized beam in this model can be effectively adjusted and optimized by flexibly controlling the peak power parameters of each sub-beam. The successful establishment of this numerical model not only provides a core model foundation for generating a high-precision and widely covered normalized synthesized power spectrum dataset, but also provides a powerful computational tool and theoretical basis for conducting high-fidelity wave optical transmission simulations and for targeted optimization of synthesized power spectrum distribution schemes.

[0027] S13. A normalized synthesized power spectrum dataset is generated based on a numerical model of normalized synthesized power spectrum with sub-bundle peak power modulation.

[0028] In this embodiment, within a pre-specified range of wavelength values, a set of values ​​with random characteristics is systematically generated based on specific statistical distribution rules, such as uniform distribution, normal distribution, or other custom probability distribution functions. By introducing this controllable randomness mechanism into the iterative search process of power spectrum optimization, the inherent limitation of traditional deterministic optimization algorithms being prone to getting trapped in local optima can be effectively overcome. This ensures that the optimization algorithm can more comprehensively and thoroughly explore and search for various possible sub-beam peak power combination schemes throughout the entire parameter space, thereby significantly improving the probability and reliability of successfully locating and converging to the global optimum in the complex multidimensional parameter space. This provides a more robust optimization guarantee for finally obtaining the best-performing synthetic power spectrum distribution scheme.

[0029] S14. Based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters, wave optics simulation is performed to obtain the far-field average light intensity dataset.

[0030] In this embodiment, relying on a rigorously established and validated atmospheric transmission wave optics simulation system for spectral synthesized beams, high-precision numerical simulation calculations are performed on the complete transmission process of the spectral synthesized beam in a real atmospheric environment. For each set of power spectrum distribution data in the normalized synthesized power spectrum dataset, based on its specific spectral distribution shape, equivalent wavelength parameters, complex amplitude distribution of the light source, and equivalent atmospheric transmittance function are first constructed using rigorous mathematical processing methods. After constructing the above equivalent parameters, they are imported into the wave optics simulation system as complete input conditions. Through the system's built-in wave optics propagation algorithm, the physical processes of diffraction, scattering, and attenuation of the beam in atmospheric turbulence are calculated and simulated layer by layer. Finally, the average light intensity value of the synthesized beam corresponding to the set of power spectrum distributions on the far-field receiving plane is solved and extracted. Following the same simulation process and calculation method described above, each set of normalized synthetic power spectrum data in the dataset is simulated and calculated independently. This systematically acquires and organizes a complete set of synthetic beam far-field average light intensity data that strictly corresponds to each power spectrum distribution, providing comprehensive and accurate simulation results to support subsequent power spectrum optimization and screening work.

[0031] S15. Based on the far-field average light intensity dataset, the optimal sub-beam peak power combination is selected, and the synthesized power spectrum generated by this combination is taken as the optimal synthesized power spectrum.

[0032] In this embodiment, each far-field average light intensity value in the dataset of far-field average light intensity of the synthesized beam calculated by the wave optics simulation system is used as the core screening basis and evaluation criterion. Through systematic comparison, sorting, and comprehensive analysis of all far-field average light intensity values ​​in the dataset, the optimal data set that achieves the global maximum far-field average light intensity value is accurately identified and located. After successfully determining the maximum far-field average light intensity value, the specific set of sub-beam peak power combination parameters corresponding to this maximum value is further traced and locked, formally recognized as the optimal sub-beam peak power combination scheme under the current atmospheric environmental conditions and transmission geometry constraints. Simultaneously, the normalized synthesized power spectrum distribution generated by this optimal sub-beam peak power combination is determined as the optimal synthesized power spectrum scheme under the current transmission scenario. This systematic screening and recognition process, while fully considering atmospheric transmission effects, can efficiently and accurately identify the power spectrum distribution with the best far-field transmission performance from numerous candidate schemes, providing a clear and reliable optimal solution for the engineering optimization design of the spectral synthesized laser system.

[0033] In this embodiment, a normalized combined power spectrum numerical model for sub-beam peak power modulation is established by acquiring laser system parameters, atmospheric environment parameters, and path geometry parameters. A normalized combined power spectrum dataset is generated based on this model. Optical simulations are then performed using this dataset in conjunction with atmospheric environment parameters and path geometry parameters to obtain the far-field average light intensity data for each combined beam. Finally, the optimal sub-beam peak power combination is selected based on the far-field average light intensity data, and the combined power spectrum generated by this combination is taken as the optimal combined power spectrum. This technical solution effectively considers various effects in laser atmospheric transmission. By optimizing the distribution of the combined power spectrum, it can significantly improve the average light intensity of the spectral synthesized laser reaching the far field during atmospheric transmission, thereby enhancing its application effectiveness.

[0034] In this embodiment, as Figure 2 As shown, based on laser system parameters, atmospheric environment parameters, and path geometry parameters, a normalized combined power spectrum numerical model for sub-beam peak power modulation is established, including the following steps: S21. Divide the wavelength range into multiple intervals based on the maximum and minimum wavelength values, and calculate the center wavelength of each interval.

[0035] Minimum wavelength value of known power spectrum distribution Maximum wavelength value of power spectrum distribution Divide the wavelength range into The interval, the first The center wavelength of each interval is , in, For the first The center wavelength of each interval It is the minimum wavelength value of the power spectrum distribution. It is the maximum wavelength value of the power spectrum distribution.

[0036] S22. Based on the sub-beam peak power, the center wavelength of the sub-beam, the full width at half maximum (FWHM) and the center wavelength of each of the aforementioned intervals in the laser system parameters, establish a numerical model of the sub-beam power spectrum.

[0037] It is known that the first The center wavelength of the sub-beam power spectrum is Half-height and full width are Record the first The peak power of each sub-beam is Then the first The numerical model of the power spectrum of each sub-beam is as follows

[0038] In the formula, For the first Power spectrum of individual beams For the first The center wavelength of each wavelength range for Individual beam peak power.

[0039] S23. Based on the number of sub-beam paths and the numerical model of sub-beam power spectrum, normalization processing is performed to establish a normalized composite power spectrum numerical model.

[0040] Given the number of sub-beam paths , No. The numerical model of the power spectrum of each sub-beam is as follows , No. The peak power of each sub-beam is The normalized composite power spectrum numerical model is then:

[0041] In this embodiment, as Figure 3 As shown, the normalized combined power spectrum dataset is generated based on the numerical model of normalized combined power spectrum with sub-bundle peak power modulation, including the following steps: S31. Based on the number of sub-bundles, generate multiple sets of random arrays with specific statistical distributions within a preset range, and use the random numbers of the random arrays as the peak power of each sub-bundle.

[0042] Given the number of sub-beam paths ,exist Generate values ​​with a mean of 0.5 within the range. The nth uniformly distributed random array, the nth The nth random number corresponds to the nth The peak power of each sub-beam is denoted as... .

[0043] In this embodiment, the random array with a specific statistical distribution includes: a random array composed of uniformly distributed random numbers with a specific mean, a random array composed of Gaussian distributed random numbers, a random array composed of normally distributed random numbers, etc.

[0044] S32. Calculate the normalized composite power spectrum data based on the random array and the normalized composite power spectrum numerical model.

[0045] Given the first The center wavelength of the sub-beam power spectrum is Half-height and full width are , No. The center wavelength of each wavelength interval is .

[0046] Order No. The peak power of each sub-beam is Numerical model of sub-beam power spectrum , obtained the The power spectrum data of each sub-beam are .

[0047] Order No. The peak power of each sub-beam is Numerical model of synthesized power spectrum The normalized synthesis power spectrum data were obtained as follows: ; S33. Calculate the normalized composite power spectrum multiple times based on the random arrays described in each group, and combine them to form a normalized composite power spectrum dataset.

[0048] Repeat the above steps Next, generate A set of random arrays is calculated to obtain Normalized composite power spectrum data. Let the first group be... The first group of random arrays The number of random numbers is , No. Normalized composite power spectrum data are denoted as .

[0049] In this embodiment, as Figure 4 As shown, wave optics simulations were performed based on the normalized composite power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain the far-field average light intensity dataset, including the following steps: S41. Calculate the equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance based on the normalized composite power spectrum.

[0050] The known wavelength range is divided into The interval, the first The center wavelength of each interval is , No. The normalized synthesis power spectrum of the group is Then the first The equivalent wavelength of the group is , The equivalent atmospheric transmittance is

[0051] In the formula, wavelength The laser at the transmission distance The extinction coefficient at that point.

[0052] The complex amplitude of the light source is

[0053] In the formula, It is the complex amplitude of the light source. It is the horizontal coordinate vector at the light source. It's the transmission power. It is the beam radius. It's the focal length. It is the symbol for an imaginary number.

[0054] S42. Input the equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance into the wave optics simulation system, and calculate the far-field average light intensity by combining atmospheric environmental parameters.

[0055] The known temperature is Crosswind speed is The absorption coefficient is Extinction coefficient Let the far-field light intensity distribution calculated by the wave optics simulation system be denoted as . , This is the far-field lateral coordinate vector.

[0056] Far-field average light intensity distribution The corresponding 63.2% circumference radius is Record the first The normalized synthesis power spectrum of the group is The corresponding far-field average light intensity is Then its expression is ; In the formula, It's the transmission power. It is the equivalent atmospheric transmittance. It's the focal length.

[0057] S43. Calculate the far-field average light intensity multiple times based on the normalized composite power spectrum in the normalized composite power spectrum dataset to form a far-field average light intensity dataset.

[0058] The known normalized synthetic power spectrum dataset contains For each set of normalized composite power spectrum data, the far-field average light intensity was calculated. The far-field average light intensity is grouped to form a far-field average light intensity dataset.

[0059] In this embodiment, as Figure 5 As shown, the optimal sub-beam peak power combination is selected based on the far-field average light intensity dataset, and the resulting composite power spectrum is used as the optimal composite power spectrum. The process includes the following steps: S51. Select the optimal sub-beam peak power combination based on the far-field average light intensity dataset.

[0060] In this embodiment, the known far-field average light intensity dataset includes The average light intensity data of the group is recorded as follows: The average light intensity of the far field group is .Compare The magnitude of the far-field average light intensity data is used to denote the group number of the group with the maximum far-field average light intensity. Then the first The set of random arrays represents the optimal sub-beam peak power combination, where the first... The peak power of each sub-beam is .

[0061] S52. Calculate the optimal combined power spectrum based on the optimal sub-bundle peak power combination.

[0062] Given the first The center wavelength of the sub-beam power spectrum is Half-height and full width are The wavelength range is divided into The interval, the first The center wavelength of each wavelength interval is .

[0063] Order No. The set of random arrays represents the optimal sub-beam peak power combination, where the first... The peak power of each sub-beam is Numerical model of sub-beam power spectrum , obtained the The power spectrum data of each sub-beam are ; Numerical model of synthesized power spectrum The normalized synthesis power spectrum data were obtained as follows:

[0064] In the formula, This represents the optimal normalized composite power spectrum.

[0065] The optimal combined power spectrum is then: .

[0066] In one specific embodiment, the laser system parameters are as follows: emission power of 50 kW, beam radius of 0.1 m, minimum wavelength of power spectrum distribution of 1030 nm, maximum wavelength of power spectrum distribution of 1090 nm, and 19 sub-beams. The center wavelength of the first sub-beam is 1033 nm, the center wavelengths of the next sub-beams are spaced 3 nm apart, and the center wavelength of the 19th sub-beam is 1087 nm. The full width at half maximum (FWHM) of the power spectrum of each sub-beam is 0.4 nm.

[0067] The atmospheric environmental parameters are: temperature 303.15 Kelvin, and transverse wind speed along the optical path 3 m / s.

[0068] Figure 6The absorption coefficient and extinction coefficient for different wavelengths are shown.

[0069] The geometric path parameters are: focal length is 5 kilometers; The power spectrum wavelength range is divided into 3000 intervals, the first... The center wavelength of each interval for nanometer; exist Generate 19 uniformly distributed random numbers with a mean of 0.5 within the range. The nth random number corresponds to the nth The peak power of each sub-beam is denoted as Substituting these values ​​into the numerical model of the normalized composite power spectrum, we can obtain the normalized composite power spectrum data. Repeat the above operation 1000 times to generate 1000 sets of random numbers and obtain 1000 sets of normalized composite power spectrum data. Normalized composite power spectrum data are denoted as , Figure 7 The normalized synthesis power spectrum data for group 10 are shown.

[0070] 1000 sets of complex amplitudes of the spectral synthesized beam source were generated from 1000 sets of normalized synthesized power spectrum data. Figure 8 The distribution of light intensity (complex amplitude squared) of the source corresponding to the 10th group of normalized composite power spectrum data is shown.

[0071] By incorporating the complex amplitude of the light source into the wave optics simulation system, the long-term average light intensity distribution can be calculated. Figure 9 The far-field average light intensity distribution corresponding to the 10th group of normalized combined power spectrum data is shown. The calculated far-field average light intensity is... Tiles per square meter.

[0072] From 1000 sets of normalized composite power spectrum data, a far-field average light intensity dataset of 1000 sets of normalized composite power spectrum data can be calculated.

[0073] Figure 10 The dataset of far-field average light intensity obtained from 1000 sets of normalized synthetic power spectrum data is shown.

[0074] Comparing the far-field average light intensity in the dataset, the value of the far-field average light intensity in group 564 was determined to be the largest. Watts / square meter. Let the normalized synthesis power spectrum of this group be... The optimal synthesis power spectrum is then... watt. Figure 11 The power spectrum of group 564 is shown, which is the optimal synthesized power spectrum.

[0075] Please see Figure 12 , Figure 12 This disclosure provides a power spectrum optimization system for spectral synthesis beam synthesis. The optimization system includes: a data acquisition module 11, a model building module 12, a data simulation module 13, an optical simulation module 14, and a power spectrum optimization module 15.

[0076] In this embodiment, the data acquisition module 11 is used to acquire laser system parameters, atmospheric environment parameters, and path geometry parameters for a preset beam transmission scenario. The laser system parameters include: emission power, beam radius, minimum wavelength of the power spectrum distribution, maximum wavelength of the power spectrum distribution, number of sub-beams, center wavelength of the power spectrum of each sub-beam, and full width at half maximum (FWHM) of the power spectrum of each sub-beam. The atmospheric environment parameters include: temperature, transverse wind speed along the optical path, absorption coefficient, and extinction coefficient. The path geometry parameters include: focal length.

[0077] In this embodiment, the model building module 12 is used to establish a normalized combined power spectrum numerical model for sub-beam peak power modulation based on laser system parameters, atmospheric environment parameters, and path geometry parameters.

[0078] In this embodiment, the data simulation module 13 is used to generate a normalized composite power spectrum dataset based on the normalized composite power spectrum numerical model of sub-bundle peak power modulation.

[0079] In this embodiment, the optical simulation module 14 is used to perform wave optical simulation based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters and path geometry parameters to obtain the far-field average light intensity dataset.

[0080] In this embodiment, the power spectrum optimization module 15 is used to select the optimal sub-beam peak power combination based on the far-field average light intensity dataset, and use the synthesized power spectrum generated by the combination as the optimal synthesized power spectrum.

[0081] In this embodiment, the data simulation module 13 is specifically used to generate uniformly distributed random numbers with a specific mean within a preset range; assign the random numbers to the peak power of each sub-bundle, and generate composite power spectrum numerical data through the sub-bundle power spectrum data; normalize the composite power spectrum numerical data to generate normalized composite power spectrum numerical data; repeat this step to obtain multiple sets of normalized composite power spectrum numerical data, forming a normalized composite power spectrum numerical data dataset.

[0082] In this embodiment, the model building module 12 is specifically used to divide the wavelength range into multiple intervals according to the maximum and minimum wavelength values, and calculate the center wavelength of each interval; based on the sub-beam peak power, the center wavelength of the sub-beam in the laser system parameters, the full width at half maximum (FWHM), and the center wavelength of each interval, to establish a sub-beam power spectrum numerical model; and based on the number of sub-beam paths and the sub-beam power spectrum numerical model, to perform normalization processing and establish a normalized composite power spectrum numerical model.

[0083] In this embodiment, the optical simulation module 14 is specifically used to calculate the equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance based on the normalized composite power spectrum; input the equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance into the wave optical simulation system, and calculate the far-field average light intensity in combination with atmospheric environmental parameters; and repeatedly calculate the far-field average light intensity based on the normalized composite power spectrum in the normalized composite power spectrum dataset to form a far-field average light intensity dataset.

[0084] In this embodiment, the power spectrum optimization module 15 is specifically used to select the optimal sub-beam peak power combination based on the far-field average light intensity dataset; and to calculate the optimal synthesized power spectrum based on the optimal sub-beam peak power combination.

[0085] like Figure 13 As shown, this embodiment of the present disclosure provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140. Memory 1130 is used to store computer programs; When the processor 1110 executes the program stored in the memory 1130, it implements any of the above methods.

[0086] The electronic device provided in this embodiment includes a processor 1110 that executes a program stored in a memory 1130 to obtain laser system parameters, atmospheric environment parameters, and path geometry parameters for a preset beam transmission scenario; establishes a normalized composite power spectrum numerical model for sub-beam peak power modulation based on the laser system parameters, atmospheric environment parameters, and path geometry parameters; generates a normalized composite power spectrum dataset based on the normalized composite power spectrum numerical model for sub-beam peak power modulation; performs wave optics simulation based on the normalized composite power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain a far-field average light intensity dataset; and selects the optimal sub-beam peak power combination based on the far-field average light intensity dataset, and uses the composite power spectrum generated by this combination as the optimal composite power spectrum.

[0087] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, and a component bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0088] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0089] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0090] The processor 1110 mentioned above can be a general-purpose processor 1110, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0091] This disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors 1110 to implement the methods of any of the above embodiments.

[0092] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0093] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the power spectrum of a synthesized beam, characterized in that, The optimization method includes: Acquire the laser system parameters, atmospheric environment parameters, and path geometry parameters for the preset beam transmission scenario; Based on laser system parameters, atmospheric environment parameters, and path geometry parameters, a normalized combined power spectrum numerical model for sub-beam peak power modulation is established. A normalized synthesized power spectrum dataset is generated based on a numerical model of normalized synthesized power spectrum with sub-bundle peak power modulation. Wave optics simulations were performed based on normalized composite power spectrum datasets, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain far-field average light intensity datasets. The optimal sub-beam peak power combination was selected based on the far-field average light intensity dataset, and the synthesized power spectrum generated by this combination was taken as the optimal synthesized power spectrum.

2. The optimization method according to claim 1, characterized in that, The normalized combined power spectrum numerical model based on sub-beam peak power modulation generates a normalized combined power spectrum dataset, including: Based on the number of sub-bundles, multiple sets of random arrays with specific statistical distributions are generated within a preset range, and the random numbers of the random arrays are used as the peak power of each sub-bundle. Normalized composite power spectrum data are calculated based on random arrays and a normalized composite power spectrum numerical model. The normalized composite power spectrum is calculated repeatedly based on the random arrays described in each group, and the results are combined to form a normalized composite power spectrum dataset.

3. The optimization method according to claim 1, characterized in that, The normalized combined power spectrum numerical model for sub-beam peak power modulation, based on laser system parameters, atmospheric environment parameters, and path geometry parameters, includes: The wavelength range is divided into multiple intervals based on the maximum and minimum wavelength values, and the center wavelength of each interval is calculated. A numerical model of the sub-beam power spectrum is established based on the sub-beam peak power, the center wavelength of the sub-beam, the full width at half maximum (FWHM) of the sub-beam, and the center wavelength of each of the aforementioned intervals in the laser system parameters. Based on the number of sub-beam paths and the numerical model of sub-beam power spectrum, normalization processing is performed to establish a normalized composite power spectrum numerical model.

4. The optimization method according to claim 1, characterized in that, The wave optics simulation based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters yields a far-field average light intensity dataset, including: The equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance are calculated based on the normalized composite power spectrum. The equivalent wavelength, complex amplitude of the light source, and equivalent atmospheric transmittance are input into the wave optics simulation system, and the far-field average light intensity is calculated in combination with atmospheric environmental parameters. The far-field average light intensity is calculated repeatedly based on the normalized synthesized power spectrum in the normalized synthesized power spectrum dataset to form a far-field average light intensity dataset.

5. The optimization method according to claim 1, characterized in that, The optimal sub-beam peak power combination was selected based on the far-field average light intensity dataset, and the resulting composite power spectrum was taken as the optimal composite power spectrum, including: The optimal sub-beam peak power combination was selected based on the far-field average light intensity dataset. The optimal combined power spectrum is calculated based on the optimal sub-bundle peak power combination.

6. The optimization method according to any one of claims 1 to 5, characterized in that, The laser system parameters include: emission power, beam radius, minimum wavelength of power spectrum distribution, maximum wavelength of power spectrum distribution, number of sub-beams, center wavelength of power spectrum of each sub-beam, and full width at half maximum (FWHM) of power spectrum of each sub-beam; the atmospheric environment parameters include: temperature, transverse wind speed of the optical path, absorption coefficient, and extinction coefficient; the path geometry parameters include: focal length.

7. A beam power spectrum optimization system for spectral synthesis, characterized in that, The optimization system includes: The data acquisition module is used to acquire laser system parameters, atmospheric environment parameters, and path geometry parameters for a preset beam transmission scenario; The model building module is used to establish a normalized combined power spectrum numerical model for sub-beam peak power modulation based on the laser system parameters, atmospheric environment parameters, and path geometry parameters. The data simulation module is used to generate a normalized synthesized power spectrum dataset based on a normalized synthesized power spectrum numerical model with sub-bundle peak power modulation. The optical simulation module is used to perform wave optical simulation based on the normalized synthesized power spectrum dataset, laser system parameters, atmospheric environment parameters, and path geometry parameters to obtain the far-field average light intensity dataset. The power spectrum optimization module is used to select the optimal combination of sub-beam peak power based on the far-field average light intensity dataset, and the resulting composite power spectrum is used as the optimal composite power spectrum.

8. The beam power spectrum optimization system according to claim 7, characterized in that, The data simulation module is specifically used to generate multiple sets of random arrays with specific statistical distributions within a preset range based on the number of sub-bundles, and to use the random numbers of the random arrays as the peak power of each sub-bundle; and to calculate the normalized composite power spectrum data based on the random arrays and the normalized composite power spectrum numerical model. The normalized composite power spectrum is calculated repeatedly based on the random arrays described in each group, and the results are combined to form a normalized composite power spectrum dataset.

9. An electronic device, characterized in that, include: processor; as well as A memory storing computer-executable instructions, which, when executed, cause the processor to perform the optimized method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, in, The computer storage medium stores one or more programs, which, when executed by a processor, implement the optimization method according to any one of claims 1-7.

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