Large dynamic range simulation method for zero-order diffraction substrate compensation

Abstract

The invention discloses a large dynamic range simulation method for zero-order diffraction base energy compensation, and the invention relates to the technical field of simulation of stellar optical radiation characteristics. The large dynamic range simulation method for zero-order diffraction base energy compensation uses the calculation of the difference between the target spectrum curve and the zero-order diffraction area area to adjust the zero-order diffraction area to ensure the simulation accuracy of the base spectrum curve, and introduces the spectrum simulation minimum unit spectrum type combined with the genetic algorithm to optimize the fuzzy PID control algorithm to perform spectrum spectrum type modulation under the dual judgment of the magnitude simulation error and the spectrum simulation error, so as to realize the large dynamic range magnitude precision simulation of the spectrum range of 450nm-1000nm, the color temperature range of 3000K-9000K, and 0Mv+12Mv, wherein, when the magnitude simulation range is 0Mv+6Mv, the spectrum simulation error is better than ±3.48%, and the magnitude simulation error is better than ±0.029Mv, and when the magnitude simulation range is +7Mv+12Mv, the spectrum simulation error is better than ±4.5%, and the magnitude simulation error is better than ±0.048Mv.

Description

Technical Field

[0001] The invention relates to the technical field of stellar optical radiation characteristic simulation, and in particular to a large dynamic range simulation method for zero-order diffraction substrate energy compensation. Background Art

[0002] As celestial bodies in the universe, stars mainly generate energy through nuclear fusion reactions and transmit it outward in the form of electromagnetic radiation. This radiation covers the entire electromagnetic spectrum from radio waves to gamma rays, but the optical radiation, i.e. the visible light part, is intuitively felt. The optical radiation characteristics of stars are affected by their internal structure and external environment, such as surface temperature, chemical composition, atmospheric pressure, magnetic field, etc.

[0003] By simulating the optical radiation characteristics of stars, we can have a deeper understanding of the physical nature and evolution process of stars, and provide strong support for the research of astrophysics. Due to the complexity of the simulation methods of stellar optical radiation characteristics and the many influencing factors, the boundary conditions of the spectral simulation error and the magnitude simulation error in actual situations are difficult to determine theoretically and can only be obtained through experiments and tests. For this reason, a large dynamic range simulation method of zero-order diffraction basis energy compensation is proposed to realize the spectral simulation of stellar optical radiation characteristics. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a large dynamic range simulation method for zero-order diffraction substrate energy compensation, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: A large dynamic range simulation method for zero-order diffraction substrate energy compensation, specifically comprising the following steps:

[0006] S1. Set the color temperature and magnitude of the target curve and the corresponding error value to obtain the target spectrum curve and error area;

[0007] S2, using a digital micromirror device (DMD) to perform spectrum traversal mapping, obtain a zero-order diffraction region and a negative first-order diffraction region, and perform spectrum fitting on the negative first-order diffraction region to obtain a spectrum simulation minimum unit spectrum;

[0008] S3, adjusting the area of ​​the zero-order diffraction region, outputting a base spectrum curve, and calculating the area difference between the base spectrum curve and the target spectrum curve;

[0009] S4, judging whether the magnitude simulation error is satisfied, if yes, go to S5, if no, go to S3;

[0010] S5, judging whether the spectrum simulation error is satisfied, if yes, go to S7, if no, go to S6;

[0011] S6, combining the spectrum simulation minimum unit spectrum shape, using genetic algorithm to optimize the fuzzy PID control algorithm to simulate the optical radiation characteristics of the star, modulate the spectrum shape, obtain the spectrum simulation curve, and go to S4;

[0012] S7. Output the final spectrum curve and end the simulation.

[0013] The present invention is further configured as follows: the method for obtaining the minimum unit spectrum of the spectrum simulation in S2 includes:

[0014] The minimum unit of spectral simulation is extracted from the negative first-order diffraction region, and the spectrum shape is fitted using a fitting algorithm to obtain the spectrum type of the minimum unit of spectral simulation.

[0015] The present invention is further configured as follows: the method of adjusting the area of ​​the zero-order diffraction region in S3 includes:

[0016] The switching states of the micromirror array in the zero-order diffraction area on the digital micromirror device (DMD) are changed one by one to obtain output substrate spectrum curves of different area sizes to achieve area adjustment.

[0017] The present invention is further configured as follows: the modulation of the spectrum shape in S6 includes:

[0018] Genetic algorithm is used to optimize the parameters of fuzzy PID control algorithm and finely modulate the spectrum shape;

[0019] During the modulation process, the difference between the spectral curve and the target spectral curve is continuously evaluated, and the control parameters are adjusted to reduce the difference.

[0020] The present invention provides a large dynamic range simulation method for zero-order diffraction substrate energy compensation, which has the following beneficial effects:

[0021] The present invention utilizes the calculation of the difference between the target spectral curve and the zero-order diffraction area to adjust the zero-order diffraction area to ensure the simulation accuracy of the base spectral curve, introduces the spectral simulation minimum unit spectrum type and combines the genetic algorithm to optimize the fuzzy PID control algorithm to perform spectral spectrum type modulation under the dual judgment of the magnitude simulation error and the spectral simulation error, and realizes the accurate simulation of the magnitude in a large dynamic range of 450nm-1000nm, 3000K-9000K, and 0Mv-+12Mv, wherein when the magnitude simulation range is 0Mv-+6Mv, the spectral simulation error is better than ±3.48%, and the magnitude simulation error is better than ±0.029Mv, and when the magnitude simulation range is +7Mv-+12Mv, the spectral simulation error is better than ±4.5%, and the magnitude simulation error is better than ±0.048Mv. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the process of the present invention;

[0023] Figure 2 450nm-1000nm spectrum curve of the continuous laser light source in the embodiment of the present invention;

[0024] Figure 3 PTFE material and BaSO in the embodiment of the present invention 4 Material integrating sphere reflectivity curve;

[0025] Figure 4 Schematic diagram of test results of three typical magnitude spectrum simulation curves of 0Mv, +3Mv and +6Mv at a color temperature of 3000K in an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of test results of three typical magnitude spectrum simulation curves of 0Mv, +3Mv and +6Mv at a color temperature of 6000K in an embodiment of the present invention;

[0027] Figure 6 Schematic diagram of test results of three typical magnitude spectrum simulation curves of 0Mv, +3Mv and +6Mv at a color temperature of 9000K in an embodiment of the present invention;

[0028] Figure 7 Schematic diagram of simulation error test results of three typical star magnitudes of 0Mv, +3Mv and +6Mv at three typical color temperatures in an embodiment of the present invention;

[0029] Figure 8 Schematic diagram of test results of spectrum simulation error and magnitude simulation error in the color temperature range of 3000K-9000K and the range of 0-+6Mv in an embodiment of the present invention;

[0030] Fig. 9 Schematic diagram of test results of 6Mv, +7Mv and +12Mv spectrum simulation curves at 3000K color temperature in an embodiment of the present invention;

[0031] Fig.10 Schematic diagram of test results of 6Mv, +7Mv and +12Mv spectrum simulation curves at 6000K color temperature in an embodiment of the present invention;

[0032] Fig.11 Schematic diagram of test results of 6Mv, +7Mv and +12Mv spectrum simulation curves at 9000K color temperature in an embodiment of the present invention;

[0033] Fig.12 Schematic diagram of the test results of spectral simulation error and magnitude simulation error in the color temperature range of 3000K-9000K +7Mv-+12Mv in the embodiment of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] See also Figure 1-12 The embodiment of the present invention provides the following technical solutions: a large dynamic range simulation method for zero-order diffraction substrate energy compensation includes initialization setting, substrate curve modulation and spectrum shape modulation, wherein the initialization setting is used to give the modulation target of the entire algorithm and provide basic information for the subsequent substrate spectrum curve and spectrum shape modulation, and the target spectrum curve and error area are obtained by setting the color temperature and magnitude of the target curve and the corresponding error value, thereby changing the boundary conditions during the execution of the algorithm to verify the optimal performance of the large dynamic range simulation algorithm for zero-order diffraction substrate energy compensation, and by controlling the working state of the micromirror array on the DMD, the spectral information corresponding to the micromirror is obtained, thereby determining the zero-order and negative first-order diffraction distribution areas on the DMD, thereby obtaining the spectral shape information of the minimum unit of spectral simulation, and then completing the spectral shape fitting of the minimum unit of spectral simulation by fitting the minimum unit algorithm of spectral simulation, wherein the fitting algorithm includes but is not limited to the least squares method and the polynomial fitting algorithm.

[0036] As a preferred solution, substrate curve modulation is the key to achieving a large dynamic range of star magnitude. The illumination distribution in the zero-order diffraction area is relatively uniform and does not have spectral selectivity. The switching state of the micromirror array in the zero-order diffraction area on the DMD is changed one by one to control the area size of the output substrate spectrum curve so that the area difference between the substrate spectrum curve and the target spectrum curve meets the magnitude simulation error range. At this time, the output energy of the substrate spectrum curve is within the error range of the target magnitude.

[0037] As a preferred solution, spectral shape modulation is used to complete the modulation of the spectral shape based on the base spectral curve that meets the magnitude simulation error, using the genetic algorithm to optimize the stellar optical radiation characteristics simulation algorithm of the fuzzy PID. Since the magnitude energy will change after the spectral shape modulation, the spectral simulation curve that meets the spectral simulation accuracy after modulation is compared with the magnitude simulation error, thereby ensuring that the final output spectral curve can meet both the spectral simulation accuracy and the magnitude simulation accuracy requirements.

[0038] Experimental simulation analysis

[0039] An experimental device was introduced, which included a continuous laser light source, an integrating sphere coupling system, a fiber optic spectrometer, a collimator and a weak light illuminance meter. Since the star magnitude energy was very low, the continuous laser light source, the integrating sphere coupling system and the fiber optic spectrometer were packaged and shielded, and the collimator was covered with a shielding coating.

[0040] Among them, the continuous laser light source is used to provide a wide spectrum beam that can be modulated. The SC-PRO continuous laser light source with a relatively smooth spectrum shape in the spectral range of 450nm-1000nm is selected. The actual object and spectrum curve of the continuous laser light source are shown in the attached figure. Figure 2 shown.

[0041] The integrating sphere coupling system consists of a coupling lens and a high reflectivity integrating sphere, which is used to mix the uneven light beam emitted by the spectrum modulation system to make the light beam emit evenly. The integrating sphere material is PTFE, as shown in the attached Figure 3 As shown, in the entire 450nm-1000nm spectral range, the PTFE material has a higher reflectivity, excellent Lambertian properties, and a smoother curve.

[0042] The fiber optic spectrometer is used to monitor the color temperature and magnitude output in real time. The fiber optic spectrometer is connected to the integrating sphere coupling system. The selected fiber optic spectrometer model is AvantesSolar-1.

[0043] The collimator is connected to the integrating sphere coupling system through optical fiber. A collimator with a focal length of 1000mm is selected. The collimator uses the optical principle of collimating the light at the focal plane to transform the point light source formed by the optical fiber into collimated light, thereby satisfying the optical characteristics of stars emitting light at infinite distance.

[0044] The low light illuminance meter is selected to measure the limit illuminance of 1.0×10 —8 The lx weak light illuminance meter is used to monitor the energy of the outgoing star at the exit of the collimator.

[0045] The test method of the above experimental device during the experiment includes:

[0046] The spectral simulation accuracy test method is to set the color temperature range, select the color temperature interval, use the fiber optic spectrometer to measure the simulated spectral curve at the outlet of the integrating sphere, and compare it with the theoretical spectral curve corresponding to the set color temperature to obtain the spectral simulation error.

[0047] The test method of magnitude simulation range and magnitude simulation accuracy is as follows: Due to the limitation of the measurement capability of the weak light illuminance meter, the illuminance value of 0Mv-+6Mv is directly measured at the exit of the parallel light tube using a weak light illuminance meter; when it exceeds +6Mv, according to the principle that the spectral curve area measured by the fiber optic spectrometer is proportional to the spectral energy, the fiber optic spectrometer is used to measure the simulated spectral curve area and the spectral curve area of ​​+6Mv at the exit of the integrating sphere, and the simulated magnitude illumination value can be indirectly obtained by dividing the two areas. Through multiple iterative attempts, the weakest magnitude value that can be simulated by the large dynamic range simulation method of zero-order diffraction base energy compensation is obtained. By comparing the measured magnitude illumination value with the theoretical magnitude illumination value, the magnitude simulation error can be obtained.

[0048] As a preferred scheme, taking the three typical color temperatures of 3000K, 6000K and 9000K in the color temperature range of 3000K-9000K and the three typical star magnitudes of 0Mv, +3Mv and +6Mv in the range of 0Mv-+6Mv as examples, the boundary conditions of spectral simulation error and magnitude simulation error are determined. On this basis, the optimal performance of the large dynamic range simulation method of zero-order diffraction substrate energy compensation is verified through experimental tests under the conditions of 3000K-9000K color temperature range (interval of 1000K) and 0Mv-+6Mv range (interval of 1Mv).

[0049] In order to determine the boundary conditions, the spectrum simulation error and magnitude simulation error were iteratively adjusted. It was found that the spectrum range always covers 450nm-1000nm. When the spectrum simulation error is set at ±3.5% and the magnitude simulation error is set at ±0.03Mv, the simulation of three typical magnitudes of 0Mv, +3Mv and +6Mv under three typical color temperatures of 3000K, 6000K and 9000K can be achieved. The test results of the spectrum simulation experiment are shown in the attached figure. Figure 4-6 The magnitude simulation test results are shown in the attached Figure 7 shown.

[0050] As a detailed description, Figure 4 , Attachment Figure 5 and attached Figure 6 Figure a in the figure is the 0Mv spectrum simulation curve, Figure b is the +3Mv spectrum simulation curve, and Figure c is the +6Mv spectrum simulation curve.

[0051] In order to determine the optimal performance of spectrum and magnitude simulation errors, the spectrum simulation error ±3.5% and the magnitude simulation error ±0.03Mv were used as boundary conditions to verify whether the boundary conditions can be met within the color temperature range of 3000K-9000K (interval of 1000K) and the range of 0Mv-+6Mv (interval of 1Mv), thereby determining the optimal performance. The experimental test results are shown in the attached Figure 8 As shown, Figure a is the spectral simulation error and Figure b is the magnitude simulation error. It can be seen that in the color temperature range of 3000K-9000K (interval of 1000K) and the range of 0Mv-6Mv (interval of 1Mv), the spectral simulation errors of 49 data are better than ±3.5% and the magnitude simulation errors are better than ±0.03Mv. Among them, the largest spectral simulation error occurs at +3Mv of 4000K color temperature, which is -3.48%; the largest magnitude simulation error occurs at +1Mv of 5000K color temperature and +5Mv of 3000K color temperature, both of which are -0.029Mv.

[0052] As a preferred solution, in order to achieve the minimum magnitude simulation capability and determine the spectral simulation error at this time, based on the +6Mv simulation experiment, the boundary conditions are determined using three typical color temperatures of 3000K, 6000K and 9000K as examples to obtain the optimal performance.

[0053] In order to determine the boundary conditions, since the higher the magnitude, the weaker the energy, the more obvious the jump of the spectrum test data, and the greater the dispersion of the measurement results, by iteratively adjusting the spectrum simulation error and the magnitude simulation error, it is found that when the spectrum simulation error is set at ±4.5% and the magnitude simulation error is set at ±0.05Mv, the stable and reproducible simulation of +7Mv and +12Mv under three typical color temperatures of 3000K, 6000K and 9000K can be achieved. At this time, in order to facilitate the indirect acquisition of the magnitude simulation error through the area ratio of the simulated spectrum curve, the maximum spectrum radiation intensity of +12Mv is used for normalization, and the spectrum simulation results of +6Mv and +7Mv under three typical color temperatures of 3000K, 6000K and 9000K are compared. The experimental test results of the spectrum simulation curve of +12Mv are shown in the attached figure. Figure 9-11 As shown, Fig. 9 , Attachment Fig.10 and attached Fig.11 Figures a in the middle are all +6Mv and +7Mv spectral simulation curves, and Figures b in the middle are all +12Mv spectral simulation curves.

[0054] Table 1. Test results of magnitude simulation experiment

[0055]

[0056] In order to determine the minimum magnitude simulation capability, the spectral simulation error of ±4.5% and the magnitude simulation error of ±0.05Mv were used as boundary conditions to verify whether the 3000K-9000K color temperature range (interval of 1000K) and +7Mv-+12Mv range (interval of 1Mv) met the boundary conditions, thereby determining the optimal performance. The experimental test results are shown in the attached figure. Fig.12 As shown, Figure a is the spectral simulation error of +7Mv-+12Mv, and Figure b is the magnitude simulation error of +7Mv-+12Mv. It can be seen that in the color temperature range of 3000K-9000K (interval of 1000K) and +7Mv-+12Mv (interval of 1Mv), the spectral simulation errors of the 42 data are better than ±4.5% and the magnitude simulation errors are better than ±0.05Mv; among them, the maximum spectral simulation error occurs at +9Mv at a color temperature of 3000K, which is 4.5%, and the maximum magnitude simulation errors occur at +9Mv at a color temperature of 5000K and +12Mv at a color temperature of 8000K, both of which are -0.048Mv.

[0057] In summary, the large dynamic range simulation method of zero-order diffraction substrate energy compensation can achieve accurate simulation of large dynamic range magnitude in the spectral range of 450nm-1000nm, color temperature range of 3000K-9000K, and 0Mv-+12Mv; among them, when the magnitude simulation range is 0Mv-+6Mv, the spectral simulation error is better than ±3.48%, and the magnitude simulation error is better than ±0.029Mv; when the magnitude simulation range is +7Mv-+12Mv, the spectral simulation error is better than ±4.5%, and the magnitude simulation error is better than ±0.048M.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A large dynamic range simulation method for zero-order diffraction substrate energy compensation, characterized by: The specific steps include: S1. Set the color temperature and magnitude of the target curve and the corresponding error value to obtain the target spectrum curve and error area; S2, using a digital micromirror device (DMD) to perform spectrum traversal mapping, obtain a zero-order diffraction region and a negative first-order diffraction region, and perform spectrum fitting on the negative first-order diffraction region to obtain a spectrum simulation minimum unit spectrum; S3, adjusting the area of ​​the zero-order diffraction region, outputting a base spectrum curve, and calculating the area difference between the base spectrum curve and the target spectrum curve; S4, judging whether the magnitude simulation error is satisfied, if yes, go to S5, if no, go to S3; S5, judging whether the spectrum simulation error is satisfied, if yes, go to S7, if no, go to S6; S6, combining the spectrum simulation minimum unit spectrum shape, using genetic algorithm to optimize the fuzzy PID control algorithm to simulate the optical radiation characteristics of the star, modulate the spectrum shape, obtain the spectrum simulation curve, and go to S4; S7. Output the final spectrum curve and end the simulation.

2. The large dynamic range simulation method for zero-order diffraction substrate energy compensation according to claim 1, characterized in that: The method for obtaining the minimum unit spectrum of the spectrum simulation in S2 includes: The minimum unit of spectral simulation is extracted from the negative first-order diffraction region, and the spectrum shape is fitted using a fitting algorithm to obtain the spectrum type of the minimum unit of spectral simulation.

3. The large dynamic range simulation method for zero-order diffraction substrate energy compensation according to claim 1, characterized in that: The method of adjusting the area of ​​the zero-order diffraction region in S3 includes: The switching states of the micromirror array in the zero-order diffraction area on the digital micromirror device (DMD) are changed one by one to obtain output substrate spectrum curves of different area sizes to achieve area adjustment.

4. The large dynamic range simulation method for zero-order diffraction substrate energy compensation according to claim 1, characterized in that: The modulation of the spectrum shape in S6 includes: Genetic algorithm is used to optimize the parameters of fuzzy PID control algorithm and finely modulate the spectrum shape; During the modulation process, the difference between the spectral curve and the target spectral curve is continuously evaluated, and the control parameters are adjusted to reduce the difference.

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