Device and method for measuring transmission quality of vortex light in air-sea environment

Abstract

The invention discloses a device and method for measuring the transmission quality of vortex light in an air-sea environment, and relates to the technical field of laser communication, and the device comprises an optical emission guide unit which is used for emitting signal light; the vortex light generation device is used for converting the signal light into a vortex light beam; the self-adaptive optical compensation unit is used for dynamically constructing a comprehensive performance index based on the air-sea vortex light beam and the vortex light beam, generating a control vector by adopting an improved SPGD algorithm based on the comprehensive performance index, performing phase compensation on the air-sea vortex light beam, and generating a compensated vortex light beam; the interference detection unit is used for performing interference fusion on the compensated vortex light and the signal light to generate an interference image; and the data processing unit is used for obtaining transmission quality data of the vortex light by adopting an improved OfficientNet-B0 model based on the interference image. According to the invention, the accuracy and efficiency of vortex light transmission quality measurement can be improved.

Description

Technical Field

[0001] This application relates to the field of laser communication technology, and in particular to a device and method for measuring the transmission quality of vortex light in an ocean-atmosphere environment. Background Technology

[0002] In the field of modern optical communication and detection technology, vortex light, due to its unique orbital angular momentum (OAM), has shown great application potential. Theoretically, OAM has an infinite number of eigenstates, which provides a new dimension of multiplexing for optical communication and is expected to greatly improve the transmission capacity and spectral efficiency of communication systems. Vortex light has been widely studied and applied in many fields such as free-space optical communication, optical imaging, micromanipulation, and underwater quantum optical communication.

[0003] In the field of vortex beam orbital angular momentum detection technology, the topological charge and OAM mode degradation of vortex beams are key indicators for evaluating their transmission quality. However, some methods have significant limitations in measuring these parameters. For example, some schemes based on turbulence phase compensation and beam geometry transformation can improve the detection range, but their complex system structure makes integration difficult. Other schemes utilize polarization characteristics for detection, simplifying operation, but are only applicable to specific types of vortex beams and lack generalization ability. While deep learning-based detection methods demonstrate high efficiency in simulation environments, they fail to consider the complex wavefront distortion and OAM degradation caused by multi-factor coupling in real ocean-atmosphere composite transmission environments, resulting in insufficient reliability in practical applications. Therefore, most technologies struggle to comprehensively and accurately test the topological charge and OAM mode degradation of vortex beams in complex ocean-atmosphere interface environments, making it impossible to determine the quality of vortex beam transmission. Summary of the Invention

[0004] The purpose of this application is to provide a device and method for measuring the transmission quality of vortex light in an air-sea environment, which can improve the accuracy and efficiency of vortex light transmission quality measurement.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a device for measuring the transmission quality of vortex light in an ocean-atmosphere environment, comprising: The system includes an optical emission guidance unit, a vortex light generation device, a channel simulation unit, an adaptive optics compensation unit, an interferometric detection unit, and a data processing unit; the channel simulation unit is used to simulate the sea-atmosphere environment. The optical emission guidance unit is used to emit signal light; The vortex light generating device is used to convert the signal light into a vortex beam; the vortex beam passes through the sea-atmosphere environment to generate a sea-atmosphere vortex beam; The adaptive optics compensation unit is used to dynamically construct a comprehensive performance index based on the air-sea vortex beam and the vortex beam, and based on the comprehensive performance index, use a modified SPGD algorithm to generate a control vector to perform phase compensation on the air-sea vortex beam to generate a compensated vortex beam. The interferometric detection unit is used to perform interference fusion of the compensated vortex light and the signal light to generate an interference image; The data processing unit is used to obtain the transmission quality data of the vortex light based on the interferometric image using an improved EfficientNet-B0 model; the transmission quality data includes: topological charge number and OAM mode degradation degree; the improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

[0007] Secondly, this application provides a method for measuring the transmission quality of vortex light in an ocean-atmosphere environment, comprising: Acquire signal light and convert it into a vortex beam, which then passes through the ocean-atmosphere environment to generate an ocean-atmosphere vortex beam; Based on the air-sea vortex beam and the dynamic construction of the vortex beam, a comprehensive performance index is constructed. Based on the comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the air-sea vortex beam, thereby generating a compensated vortex beam. The compensated vortex light and the signal light are subjected to interference fusion to generate an interference image; The interferometric image is input into the improved EfficientNet-B0 model to obtain the transmission quality data of the vortex light; the transmission quality data includes: topological charge number and the degree of degradation of the OAM mode; the improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application first simulates an ocean-atmosphere environment using a channel simulation unit, generating an ocean-atmosphere vortex beam after the vortex beam passes through the environment, thus better reflecting practical applications. Then, an adaptive optics compensation unit dynamically constructs a comprehensive performance index based on the ocean-atmosphere vortex beam and the vortex beam itself. Based on this comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the ocean-atmosphere vortex beam, making the final compensated vortex beam more closely match the phase of the original vortex beam, thereby improving the transmission quality of the compensated vortex beam. Finally, an improved EfficientNet-B0 model is used to detect the topological charge number and the degree of degradation of the OAM mode. Due to the addition of a lightweight attention module for early classification, it not only enables adaptive feature optimization and enhancement of the feature map but also allows for rapid output of vortex beam transmission quality data, achieving rapid detection and improving the accuracy and efficiency of vortex beam transmission quality measurement. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of a device for measuring the transmission quality of vortex light in an air-sea environment, provided in this application.

[0011] Figure 2 This is a flowchart illustrating a method for measuring the transmission quality of vortex light in an air-sea environment, as provided in this application.

[0012] Figure 3 A schematic diagram of the phase of the vortex beam provided in this application.

[0013] Figure 4 This is a phase diagram of the air-sea vortex beam provided in this application.

[0014] Figure 5 A schematic diagram of the phase of the compensated vortex beam provided in this application.

[0015] Figure 6 A schematic diagram of the structure of the improved EfficientNet-B0 model provided in this application.

[0016] Figure 7 This is a schematic diagram of the interference pattern provided in this application when the topological charge number is -3.

[0017] Figure 8This is a schematic diagram of the interference pattern provided in this application when the topological charge number is -2.

[0018] Figure 9 This is a schematic diagram of the interference pattern provided in this application when the topological charge number is -1.

[0019] Figure 10 This is a schematic diagram of the interference pattern provided in this application when the topological charge number is 0.

[0020] Figure 11 This is a schematic diagram of the interference pattern provided in this application when the topological charge number is 1.

[0021] Figure 12 This is a schematic diagram of the interference pattern provided in this application when the topological charge number is 2.

[0022] Figure 13 This is a schematic diagram of the interference pattern when the topological charge number is 3, as provided in this application.

[0023] Reference numerals: Optical emission guiding unit-1, laser-11, collimating beam expander-12, polarizer-13, first beam splitter prism-14, reflector-15, vortex light generating device-2, spatial light modulator-21, spatial light modulation controller-22; channel simulation unit-3, adaptive optics compensation unit-4, second beam splitter prism-41, reflective deformable mirror-42, third beam splitter prism-43, first CCD detector-44, second CCD detector-45, optical compensation controller-46, interferometric detection module-5, data processing unit-6. Detailed Implementation

[0024] 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, and 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.

[0025] Vortex light faces numerous challenges when propagating in an air-sea environment. The air-sea environment is extremely complex; seawater flow, temperature gradients, salinity gradients, and atmospheric aerosols and dust all significantly affect vortex light transmission. In particular, ocean turbulence, with its internal quasi-ordered structures, disrupts the symmetry of the OAM (Optical Aspect-Oriented Media) spectrum distribution of vortex light, thus altering its OAM. Studies have shown that severe crosstalk occurs between different OAM modes in turbulent ocean environments, severely limiting the effective application of OAM modes in optical communication. Furthermore, salt particles and aerosols in the marine environment scatter and absorb vortex light, causing optical signal energy attenuation and further exacerbating vortex light OAM degradation. However, most techniques for detecting vortex light OAM degradation only consider a single factor, failing to comprehensively and accurately simulate and test the complex multi-factor coupling leading to OAM degradation in air-sea environments, thus failing to meet practical application requirements. Therefore, this application proposes a measurement device and method for vortex light transmission quality in an air-sea environment. First, the multi-factor coupled channel that leads to OAM mode degradation is reproduced in a controlled environment (i.e., simulating an ocean-atmosphere environment). Then, the vortex light with complex distortions after transmission is interfered with an ideal reference light, and its unobservable phase distortion and mode crosstalk information are encoded into an interference image. Finally, the improved EfficientNet-B0 model is used to decode the interference image, thereby realizing the robust identification of the topological charge of the vortex light and the quantitative assessment of its modal degradation degree, improving the accuracy and efficiency of vortex light transmission quality measurement.

[0026] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] In one exemplary embodiment, such as Figure 1 As shown, a device for measuring the transmission quality of vortex light in an ocean-atmosphere environment is provided, comprising: an optical emission guidance unit 1, a vortex light generation device 2, a channel simulation unit 3, an adaptive optics compensation unit 4, an interferometric detection unit, and a data processing unit 5; the channel simulation unit 3 is used to simulate the ocean-atmosphere environment.

[0028] Optical emission guidance unit 1 is used to emit signal light.

[0029] As one possible implementation, the optical emission guiding unit 1 includes: a laser 11, a collimating beam expander 12, a polarizer 13, a first beam splitter prism 14, and a reflector 15 arranged sequentially.

[0030] The laser emitted by the laser 11 is first expanded and collimated by the collimating beam expander 12, and then polarized by the polarizer 13. The polarized light is split into two signal beams by the first beam splitter prism 14. One signal beam enters the vortex light generating device 2, and the other signal beam enters the interference detection unit 5 through the reflector 15.

[0031] Specifically, laser 11 is a 532nm continuous-wave ND:YAG laser. A collimating beam expander 12 is connected to the right side of laser 11, with its output end aligned with the collimating beam expander 12 to ensure the beam center is incident on the center of the collimating beam expander 12. The collimating beam expander 12 expands the beam diameter output from laser 11 to 9mm to meet the working aperture of the vortex light generating device 2. The beam is then incident on polarizer 13, which adjusts the beam to a polarization state matching that of the spatial light modulator 21. The first beam splitter prism 14 is a non-polarizing beam splitter prism with a splitting ratio of 1:1, located on the output optical path at the right end of polarizer 13. It splits the beam into two signal beams: one signal beam (i.e., the transmitted beam) enters the vortex light generating device 2, and the other signal beam (i.e., the reflected beam) is guided by reflector 15 to the interference and detection module 5 as a reference beam.

[0032] Vortex light generating device 2 is used to convert signal light into vortex beam; the vortex beam passes through the sea-atmosphere environment to generate a sea-atmosphere vortex beam.

[0033] As one feasible approach, the vortex light generating device 2 includes: The spatial light modulation controller 22 is connected to the spatial light modulator 21 and the data processing unit 6 respectively, and is used to receive control commands from the data processing unit 6 to control the spiral phase diagram of the spatial light modulator 21.

[0034] Specifically, by inputting a specific phase map program into the spatial light modulation controller 22, the spatial light modulation controller 22 will load a spiral phase map onto the spatial light modulator 21. The spatial light modulator 21 is an SLM spatial light modulator.

[0035] Spatial light modulator 21 is used to convert signal light into a vortex beam via a spiral phase diagram.

[0036] Specifically, the spatial light modulator 21 adopts a TSLM07U-A transmissive modulator with a resolution of 1920×1080, an effective working area of ​​16.3mm×9.18mm, and a spectral range of 420nm-1200nm. It is located in the transmission light path of the optical emission and guidance unit 1, and its surface is perpendicular to the incident light axis. It generates a vortex beam with a specific topological charge by loading a spiral phase diagram. The spatial light modulation controller 22 is connected to the spatial light modulator 21 through a data line and can update the spiral phase diagram in real time.

[0037] As an implementable approach, the channel simulation unit 3 includes a fog chamber and a turbulence pool. The fog chamber is used to generate simulated sea fog in an air-sea environment, and the turbulence pool is used to generate turbulence in the air-sea environment.

[0038] Specifically, channel simulation unit 3 integrates a sea fog chamber, a turbulence pool, a salinity adjustment system, a temperature control system, and a turbulence simulation device. It can precisely control key parameters such as salinity, temperature, and turbulence intensity, and provides reliable simulation conditions for studying the transmission of vortex light in the real sea-atmosphere environment, which is designed for the complex transmission environment of the sea-atmosphere interface region.

[0039] The sea fog chamber is an acrylic glass water tank. The main task of channel simulation unit 3 is to simulate the air-sea channel using the sea fog chamber and turbulence tank. The sea fog chamber adopts a multi-layered sea fog simulation chamber structure. In each fog maintenance system, the principle of ultrasonic vibration is used to transform water into 0.5-1μm water mist particles to simulate a thin sea fog environment in the air-sea environment; and salt water of different concentrations can be generated into 1-2μm salt fog particles through ultrasonic vibration to simulate a dense sea fog environment in the air-sea environment. The turbulence tank adopts a box structure with light-transmitting holes on the left and right sides to support laser incident and emitted light. The top and bottom are a condenser plate and a heat spreader plate, respectively. The temperature difference between the top and bottom creates turbulence in the simulated sea fog environment inside the chamber. By controlling the temperature of the heat spreader plate, the magnitude of the turbulence is controlled, and specific turbulent environments are created by changing variables according to experimental requirements.

[0040] The adaptive optics compensation unit 4 is used to dynamically construct a comprehensive performance index based on the air-sea vortex beam and the vortex beam, and based on the comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the air-sea vortex beam and generate a compensated vortex beam.

[0041] Specifically, the adaptive optics compensation unit 4 is used to correct the wavefront distortion generated by the ocean-air vortex beam during channel transmission in real time, so as to reduce or correct the wavefront distortion, improve the beam quality of the optical signal entering the interference link, and improve the fidelity of the OAM mode corresponding to the target preset topological charge number.

[0042] As one feasible approach, the adaptive optics compensation unit 4 includes: The second beam splitter prism 41 is set in the outgoing optical path of the channel simulation unit to split the ocean-air vortex beam into two ocean-air vortex beams; one ocean-air vortex beam enters the first CCD detector; the other ocean-air vortex beam enters the reflective deformable mirror.

[0043] The first CCD detector 44 is used to convert the ocean-atmosphere vortex beam into a first image.

[0044] The reflective deformable mirror 42 is used to perform phase compensation on the air-sea vortex beam to generate a compensated vortex beam.

[0045] The third beam splitter prism 43 is set in the output light path of the reflective deformable mirror 42 and is used to split the compensated sea-air vortex light into two compensated vortex beams; one compensated vortex beam is injected into the second CCD detector 45; and the other compensated vortex beam is injected into the interferometric detection unit 5.

[0046] The second CCD detector 45 is used to convert the compensated vortex beam into a second image.

[0047] The optical compensation controller 46 is connected to the data processing unit 6, the first CCD detector 44, the reflective deformable mirror, and the second CCD detector 45, respectively. It is used to receive control commands from the data processing unit 6, dynamically construct comprehensive performance indicators based on the ocean-atmosphere vortex beam and the vortex beam, and generate control vectors based on the comprehensive performance indicators using the improved SPGD algorithm to perform phase control on the reflective deformable mirror.

[0048] Specifically, the ocean-air vortex beam emitted from the channel simulation unit 3 is split into two beams by the second beam splitter 41. Most of the energy is incident on the reflective deformable mirror 42, which compensates for the wavefront distortion. After the compensated ocean-air vortex beam is split by the third beam splitter 43, most of the energy goes to the interferometric detection unit 5, and a small portion of the energy goes to the second CCD detector 45 to detect the beam quality and provide real-time feedback to the controller to execute the improved SPGD algorithm.

[0049] Specifically, such as Figure 1 As shown, the second beam-splitting prism 41 is located at the right end of the channel simulation unit 3, receiving the sea-air vortex beam with a splitting ratio of 1:9. 10% of the energy enters the first CCD detector 44 to acquire image information, and 90% of the energy enters the reflective deformable mirror 42. The optical compensation controller 46 is an industrial control computer running a modified SPGD algorithm. After receiving the data, it generates a drive signal to control the reflective deformable mirror 42 to achieve closed-loop compensation, thereby realizing the braking of the reflective deformable mirror 42 and thus implementing phase modulation of the sea-air vortex beam. The reflective deformable mirror 42 is an ALPAO electromagnetic small-aperture reflective deformable mirror DM97-15 model with a working aperture of 13.5mm and a tilt deformation of 60μm. It is located on the right end of the transmission light path of the second beam splitter prism 41 and receives 90% of the energy beam from the beam splitter prism 41. The third beam splitter prism 43 is located on the reflection light path of the reflective deformable mirror 42 with a splitting ratio of 1:9. 10% of the energy is incident to the second CCD detector 45 for measuring compensation data and feeding it back to the optical compensation controller 46. Under the drive of the optical compensation controller 46, the compensation surface shape is regenerated and iterated continuously. The remaining energy is emitted to the interferometric detection unit 5.

[0050] Interference detection unit 5 is used to perform interference fusion of the compensated vortex light and the signal light to generate an interference image.

[0051] Specifically, the interferometric detection unit 5 acquires and transmits interferometric images with high precision, while simultaneously assisting in optical path calibration and optimization, providing a basis for analyzing the degradation degree of vortex light OAM. The interferometric detection unit 5 is a Mach-Zehnder structure. Its reference arm receives the reflected light from the optical emission guiding unit 1, while its signal arm receives the compensated vortex beam output from the adaptive optics compensation unit 4. The optical path difference between the two arms is compensated to ≤5μm via an optical fiber delay line, resulting in an interference fringe contrast ≥80%. After acquiring the interference fringe image, the interferometric detection unit 5 transmits it to the data processing unit 6 via a GigE interface.

[0052] Data processing unit 6 is used to obtain transmission quality data of vortex light based on interferometric images using an improved EfficientNet-B0 model. The transmission quality data includes: topological charge number and OAM mode degradation degree. The improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

[0053] Among them, data processing unit 6 is an industrial computer equipped with a GPU.

[0054] Specifically, an improved EfficientNet-B0 model is adopted, which innovates by introducing a lightweight attention module and an early classification exit. The early classification exit generates a dynamic inference path, significantly improving the model's real-time response speed while maintaining high accuracy. The lightweight attention module is embedded between network layers, quickly focusing on key regions related to OAM degradation in interference fringe images by performing hybrid attention calculations on feature maps using both channel and spatial dimensions, suppressing background noise and thus improving feature extraction efficiency. The dynamic inference path sets multiple early classification exits in the middle of the network and designs a confidence evaluation function to judge the difficulty of samples during the inference process. For high-confidence samples, the model outputs classification and degradation evaluation results in advance, avoiding complete network forward propagation, thereby achieving conditional calculation at the system level and significantly reducing average inference latency.

[0055] Specifically, the OAM mode degradation refers to the distortion of its orbital angular momentum (OAM) mode after transmission, and the topological charge is a core parameter characterizing the helical phase structure of a vortex beam. The device in this application does not directly measure the physical values ​​of OAM, but rather evaluates the degree of degradation in mode purity, wavefront phase, and intensity distribution of a vortex beam with a preset topological charge (i.e., the OAM mode degradation) after transmission by simulating a complex ocean-atmosphere channel. These factors collectively determine the availability and reliability of the OAM mode in communication.

[0056] The beneficial effects of the vortex light transmission quality measurement device proposed in this application under ocean-atmosphere environment are mainly reflected in: 1. This application integrates sea fog simulation, salinity regulation, temperature control and turbulence simulation devices through a channel simulation unit. It accurately reproduces the complex sea-atmosphere environment with multiple factors such as salinity, temperature and turbulence coupled in a unified channel simulation unit. It overcomes the limitation of existing technologies that only consider a single factor and provides reliable simulation conditions for studying the transmission of vortex light in real sea-atmosphere environments.

[0057] 2. By using an adaptive optics compensation unit, a comprehensive performance index is dynamically constructed based on the air-sea vortex beam and the vortex beam. Based on the comprehensive performance index, a control vector is generated using a modified SPGD algorithm to perform phase compensation on the air-sea vortex beam. This makes the phase of the finally compensated vortex beam more closely match the phase of the original vortex beam, thereby improving the transmission quality of the compensated vortex beam. Compared with no compensation or traditional compensation methods, this method is more efficient and more accurate.

[0058] 3. This application establishes a complete automated closed loop. The data processing unit employs an improved EfficientNet-B0 model with an added lightweight attention module for early classification exit. This not only enables adaptive feature optimization and enhancement of the feature map but also allows for rapid output of vortex light transmission quality data, achieving fast detection and thus improving the accuracy and efficiency of vortex light transmission quality measurement. This meets the practical need for real-time and efficient monitoring of vortex light OAM status, powerfully promoting the transition of vortex light technology from theoretical research to engineering applications.

[0059] 4. During the testing process, this application achieves automated and efficient testing. The data processing unit, combining deep learning and digital image processing technologies, receives data from the CCD camera and environmental sensors. After preprocessing, deep learning inference, and correlation analysis, it automatically outputs and stores the vortex light OAM degradation results, which can also be displayed in real time on a monitor. This reduces manual intervention, greatly improves testing efficiency and accuracy, provides strong support for the practical application of vortex light technology, and promotes the faster transition of related technologies from theoretical research to practical application.

[0060] Based on the same inventive concept, this application also provides a method for measuring the transmission quality of vortex light in an ocean-atmosphere environment. This method is applied to the aforementioned measuring device for the transmission quality of vortex light in an ocean-atmosphere environment. In an exemplary embodiment, such as... Figure 2 As shown, a method for measuring the transmission quality of vortex light in an ocean-atmosphere environment is provided, comprising steps S1 to S4: Step S1: Acquire signal light and convert it into a vortex beam. The vortex beam passes through the ocean-atmosphere environment to generate an ocean-atmosphere vortex beam.

[0061] Specifically, when converting signal light into a vortex beam, the spiral phase diagram is adjusted using a spatial light modulator to match the polarization state of the vortex beam to the operating state of the spatial light modulator. The electric field expression of the vortex beam is: .

[0062] in, The electric field of the vortex beam. The radial distance is the straight-line distance from the center of the vortex beam to a certain point. These are angular coordinates used to describe the azimuth angle of a point in space relative to the center of the beam. for axis, Let represent the electric field of the vortex beam at its initial position, describing the spatial distribution of the beam's intensity at that initial position. Indicates a spiral phase structure. This is the imaginary part of the phase. For topological load number, Wave number, relative to the wavelength of the beam. Related, specifically the relationship is as follows It is used to describe the propagation characteristics of light.

[0063] Meanwhile, the phase distribution of the vortex beam is: .

[0064] in, This represents the phase of the vortex beam.

[0065] Step S2: Based on the ocean-atmosphere vortex beam and the dynamic construction of the vortex beam, a comprehensive performance index is constructed. Based on the comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the ocean-atmosphere vortex beam and generate a compensated vortex beam.

[0066] As one feasible approach, step S2 specifically includes steps S21 to S24: Step S21: Construct comprehensive performance indicators based on the dynamics of the air-sea vortex beam and the vortex beam.

[0067] As one feasible approach, step S21 specifically includes steps S211 to S2111: Step S211: Based on the vortex beam, determine the vortex light field and the area of ​​the vortex annular region of the vortex beam.

[0068] Step S212: Based on the ocean-atmosphere vortex beam, determine the vortex light field and phase of the ocean-atmosphere vortex beam.

[0069] Step S213: Calculate the sea fog intensity and turbulence intensity based on the vortex light field of the vortex beam, the vortex light field of the sea-air vortex beam, and the phase of the sea-air vortex beam.

[0070] Specifically, sea fog intensity and turbulence intensity The calculation formula is: .

[0071] in, This refers to the vortex light field of a vortex beam. This represents the vortex light field of an ocean-atmosphere vortex beam. This represents the phase of the ocean-atmosphere vortex beam. For gradient.

[0072] Step S214: Calculate the baseline weights based on sea fog intensity and turbulence intensity.

[0073] As an feasible approach, the formula for calculating the benchmark weight is: .

[0074] in, As the benchmark weight for the model purity index, As the initial weights, The first turbulence sensitivity coefficient, For the first Turbulence intensity estimation at the next iteration The first sea fog sensitivity coefficient, For the first Sea fog intensity estimation at the next iteration The benchmark weight for the performance index of phase topology integrity, The initial weights for the performance index of phase topology integrity, This is the second turbulence sensitivity coefficient. The second sea fog sensitivity coefficient, The benchmark weights for performance indicators representing the spatial distribution characteristics of beam intensity. The initial weights for the performance index of the spatial distribution characteristics of the beam intensity are given. This is the third turbulence sensitivity coefficient.

[0075] Step S215: Normalize the baseline weights to obtain adaptive weights.

[0076] .

[0077] .

[0078] in, For the adaptive weight of the model purity index, An adaptive weight for the performance index of phase topology integrity. Adaptive weights for performance indicators representing the spatial distribution characteristics of beam intensity. This is the sum of the baseline weights.

[0079] Step S216: Calculate the mode purity index based on the vortex light field of the ocean-air vortex beam.

[0080] .

[0081] .

[0082] .

[0083] .

[0084] .

[0085] in, As an indicator of model purity, The mode coefficients of the target vortex beam are denoted as . The topological charge number is The mode coefficients of the vortex beam. The denominator is a constant; to prevent the denominator from being zero, The vortex beam after compensation is the vortex light field, i.e., the vortex beam after reflection by the deformable mirror. Phase compensation applied to the reflective deformable mirror The phase of the disturbance caused by turbulence. The amplitude attenuation and phase perturbation function when traversing the air-sea environment. For the first The control vector of each actuator For the first The response function of an actuator It is the radial distance from the center of the reflective deformable mirror to a point on its surface.

[0086] Step S217: Calculate the estimated topological charge number based on the vortex light field of the vortex beam.

[0087] Specifically, by calculating the loop integral of the phase gradient along a closed path to predict the topological charge, and using this to construct a performance index for the phase topological integrity of the beam, the predicted topological charge can be expressed by the following formula: .

[0088] in, Let be the phase of the vortex beam's vortex field. It is the gradient of the phase of the vortex light field of the vortex beam. To estimate the topological load.

[0089] Step S218: Calculate the performance index of phase topology integrity based on the estimated topology charge number.

[0090] Specifically, the performance indicators for phase topology integrity are: .

[0091] in, This is a performance indicator for the integrity of the phase topology. For the target topological load number, The positive number is used to prevent the denominator from being zero, and it also helps to smooth the index function.

[0092] Step S219: Calculate the performance index of the spatial distribution characteristics of the beam intensity based on the area of ​​the vortex annular region.

[0093] Specifically, the ultimate goal of wavefront compensation is to restore the quality of vortex light. The quality of vortex light is reflected not only in its phase but also in its intensity distribution, thereby constructing a performance index that targets the spatial distribution characteristics of the beam intensity.

[0094] Assume an ideal vortex beam in the annular region The intensity distribution within is The supplemented light intensity is Then we have: .

[0095] in, It is a performance index representing the spatial distribution characteristics of beam intensity. The area of ​​the vortex-like annular region.

[0096] Step S2110: Normalize the performance index of the mode purity index, the performance index of the phase topology integrity, and the performance index of the spatial distribution characteristics of the beam intensity to obtain normalized mode purity index, normalized phase topology integrity index, and normalized beam intensity spatial distribution characteristics.

[0097] Specifically, the normalized model purity index Performance indicators of normalized phase topology integrity Performance indicators of the spatial distribution characteristics of the normalized beam intensity The expression is: .

[0098] Step S2111: Construct a comprehensive performance index based on the normalized mode purity index, the normalized phase topology integrity performance index, the normalized beam intensity spatial distribution characteristics performance index, and the adaptive weight.

[0099] Specifically, comprehensive performance indicators The expression is: .

[0100] Step S22: Obtain the initial control vector.

[0101] Step S23: Based on the vortex light field of the sea-air vortex beam, the initial control vector is iterated multiple times using the improved SPGD algorithm until the comprehensive performance index reaches the threshold or the maximum number of iterations is reached, and then the control vector is generated.

[0102] Specifically, during the iteration of the improved SPGD algorithm, the initial control vector is first initialized, letting... Set the gain coefficient Number of iterations .

[0103] Generation of disturbances The perturbation of each element takes a random value of ±1, and then normalization is performed. .

[0104] Apply positive and negative perturbations , This causes the brakes of the reflective deformable mirror to activate, thereby applying initial compensation to the vortex beam. At this point, the image acquired by the second CCD detector can obtain the image based on this perturbation. Performance metrics at the next iteration , The correspondence is as follows: .

[0105] Therefore, calculate the first The change in the overall performance index during the next iteration is: .

[0106] Update the control vector and iterate: .

[0107] Among them, the The formula for calculating the gain coefficient in the next iteration is: .

[0108] in, No. The gain coefficient at each iteration determines the size of the update step. The decay coefficient controls the rate at which the gain decays exponentially with the number of iterations. The larger the value, the faster the decay. This represents the total number of iterations. This is the gradient sensitivity coefficient, which controls how much the gain responds to the magnitude of the gradient. Gain adjustment strategies can adaptively adjust the step size at different stages to balance convergence speed and stability.

[0109] Finally, the wavefront correction (i.e., phase compensation) is completed by iterating until the comprehensive performance index reaches the threshold or the maximum number of iterations is reached. Figures 3 to 5 This is a comparison chart of simulation results performed under ideal conditions.

[0110] Step S24: Perform phase compensation on the air-sea vortex beam based on the control vector to obtain the compensated vortex beam.

[0111] Step S3: Perform interference fusion on the compensated vortex light and the signal light to generate an interference image.

[0112] Specifically, by adjusting the fiber delay line of the interferometric detection unit, interference fringe images are acquired by a CCD camera, and the fringe contrast is calculated. When the contrast is ≥80%, the position of the fiber delay line is locked. Parameters such as the CCD camera exposure time of 20ms and the frame rate of 30 frames / second are set to acquire standard fringe images. During dynamic testing, under various environmental parameter gradients, the CCD camera continuously acquires interference images according to the set parameters, synchronously records environmental data, and transmits it to the data processing unit for storage via the GigE interface.

[0113] Step S4: Input the interferometric image into the improved EfficientNet-B0 model to obtain the transmission quality data of the vortex light; the transmission quality data includes: topological charge number and the degree of degradation of the OAM mode; the improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

[0114] Specifically, such as Figure 6 As shown, in the improved EfficientNet-B0 model, the size of the interference intensity image in the input layer is adjusted from the initial size to a square size, and the number of pixels in length and width should be an integer multiple of 32. This is because the improved EfficientNet-B0 model has 5 downsampling steps with a stride of 2, so the total downsampling factor of the entire network is 2^32. 5 =32, otherwise the network will not be able to process properly.

[0115] The improved EfficientNet-B0 model consists of 9 stages: Stage 1: Initial convolutional layer. The input 512×512 interference image is convolved with a 3×3 convolution kernel to obtain 32 256×256 feature images, which mainly extract basic edge and texture features.

[0116] Stage 2: The first MBConv module. The 16 256×256 images generated in this process are obtained by performing a 3×3 convolution operation on the 32 feature images from the previous step.

[0117] The following Stages 2 through 8 repeatedly stack the MBConv structure, while Stage 9 consists of a standard 1x1 convolutional layer (containing BN and the Swish activation function), an average pooling layer, and a fully connected layer. The pooling layer has a 16x16 pooling window, and the fully connected layer has 1280 nodes connected to it, with two output heads. The final output shows the topological charge of the vortex light, the degree of degradation of the OAM mode, and the corresponding classification probability.

[0118] The lightweight attention module is embedded before the MBConv module in Stages 3, 5, and 7, and its function is to perform adaptive feature optimization and enhancement on the input feature map. To reduce the computational cost of simple samples, three early classification exits are set after Stages 3, 5, and 7. Each exit consists of a global average pooling layer and a fully connected classifier, with two output heads. The final outputs are the topological charge of the vortex light, the degradation degree of the OAM mode, and the corresponding classification probability.

[0119] As an implementable approach, the improved EfficientNet-B0 model comprises, in sequence, an initial convolutional layer, a first feature extraction module, a first early classification exit, a second feature extraction module, a second early classification exit, a third feature extraction module, a third early classification exit, a first MBConv module, a convolutional layer, an average pooling layer, and a fully connected layer; the first, second, and third early classification exits each consist of a global average pooling layer and a fully connected classifier; the first, second, and third feature extraction modules each consist of a lightweight attention module and two MBConv modules. Step S4 specifically includes steps S41 to S46: Step S41: Input the interference image into the initial convolutional layer for feature extraction to obtain the initial feature map.

[0120] Step S42: Input the initial feature map into the first feature extraction module for weighted feature extraction to obtain the first feature map.

[0121] Specifically, taking the first feature extraction module as an example, the connection order in the first feature extraction module is: MBConv module - lightweight attention module - MBConv module. Assume the input feature map of the lightweight attention module is... The output is a weighted feature map. .

[0122] The calculation process of this module is as follows: .

[0123] in, This is the attention weight vector along the channel dimension. , It is the Sigmoid activation function. It is a 1×1 convolutional layer. It is a lightweight, two-layer fully connected layer. This is global average pooling.

[0124] Next, the spatial attention weight matrix is ​​calculated: .

[0125] in, The attention weight matrix is ​​a spatial dimension. , This represents a 1×1 convolutional layer used to compress the number of channels and fuse spatial information.

[0126] Finally, the attention-weighted feature map is output: .

[0127] in, This module performs element-wise multiplication. With minimal computational overhead, it enables the network to quickly focus on features most relevant to the phase singularity of vortex light and the interference fringe structure, improving the signal-to-noise ratio of useful information and laying the foundation for rapid and accurate judgment in subsequent levels.

[0128] Step S43: Input the first feature map to the first early classification exit to obtain the first confidence level and the transmission quality data of the first vortex light; if the first confidence level is greater than the confidence level threshold, then determine the transmission quality data of the first vortex light as the transmission quality data of the vortex light; if the first confidence level is less than or equal to the confidence level threshold, then input the first feature map to the second feature extraction module for weighted feature extraction to obtain the second feature map.

[0129] Specifically, taking the first early classification exit as an example, let the first early classification exit input be the first feature map. The output is the transmission quality data of the first confidence level and the first vortex light.

[0130] The classification probability vector output by the first feature map is: .

[0131] in, For classification probability vectors, This is the Softmax activation function, which ensures that the sum of all output probabilities is 1, and that the probability value for each class is between 0 and 1. This is the weight matrix of the classifier for the first early export. This is the bias vector for the first early exit.

[0132] The prediction confidence level for this exit is defined as the maximum value of the probability vector: .

[0133] in, This represents the first confidence level.

[0134] Set an adjustable confidence threshold. During inference, data flows through the network sequentially. When it reaches the first early exit, if the first confidence level is greater than the confidence threshold, the output of that exit is immediately taken as the final result, and the forward computation of subsequent network layers is terminated.

[0135] Because of the early classification exit, the model can respond very quickly when faced with "simple" samples with slight degradation and obvious features; the entire model is only called for calculation when faced with "complex" samples with severe degradation and difficult to judge.

[0136] Step S44: Input the second feature map into the second early classification exit to obtain the second confidence level and the transmission quality data of the second vortex light; if the second confidence level is greater than the confidence threshold, then determine the transmission quality data of the second vortex light as the transmission quality data of the vortex light; if the second confidence level is less than or equal to the confidence threshold, then input the second feature map into the third feature extraction module for weighted feature extraction to obtain the third feature map.

[0137] Specifically, such as Figure 6 As shown, the prediction confidence of the second early classification exit is the maximum value of the probability vector: .in, This represents the second confidence level.

[0138] Step S45: Input the third feature map into the third early classification exit to obtain the third confidence level and the transmission quality data of the third vortex light; if the third confidence level is greater than the confidence level threshold, then determine the transmission quality data of the third vortex light as the transmission quality data of the vortex light; if the third confidence level is less than or equal to the confidence level threshold, then input the third feature map into the first MBConv module for weighted feature extraction to obtain the fourth feature map.

[0139] Specifically, such as Figure 6As shown, the prediction confidence of the third early classification export is the maximum value of the probability vector: .in, This represents the third confidence level.

[0140] Step S46: The fourth feature map is sequentially processed through convolutional layers, average pooling layers, and fully connected layers for feature extraction, dimensionality reduction, and classification to obtain the transmission quality data of the vortex light.

[0141] Specifically, such as Figures 7 to 13 As shown, the interference fringes produced by the non-coaxial interference of a vortex beam and a plane wave are illustrated. Figures 7 to 9 This represents the interferometric images with topological charges of -3, -2, and -1, respectively. Figure 10 This represents the interferometric image when the topological charge number is 0. Figures 11 to 13 The images represent the interferometric images with topological charges of 1, 2, and 3, respectively.

[0142] Specifically, to obtain training data for the improved EfficientNet-B0 model, data acquisition is required. The experimental procedure for data acquisition is as follows: A set of parameters for a fixed channel simulation unit 3, such as salinity, temperature, and turbulence intensity, is used. A vortex beam with a known topological charge is emitted through the vortex beam generator 2, allowing it to pass through the channel and acquire an interferometric image. Simultaneously, theoretical simulation is used to simulate the degradation degree of the OAM mode in the received light field. The interferometric image is paired with the corresponding OAM mode degradation degree label to form a training sample. By traversing different preset topological charge numbers, channel parameters, and environmental conditions, a large-scale training dataset can be constructed for training the improved EfficientNet-B0 model.

[0143] To eliminate the light intensity inhomogeneity in the image, the normalization formula for the intensity of the interference image is: .

[0144] in, For pixel coordinates Interference image intensity, For pixel coordinates The intensity of the original interference image, The mean intensity of the interference image. Standard deviation of the intensity of the interference image.

[0145] Complex amplitude extraction using single-frame phase-shift interferometry demodulation: .

[0146] in, To extract the complex amplitude using the single-frame phase-shift interferometry demodulation method, For two-dimensional Fourier transform, For the frequency domain window function of the bandpass filter, spatial frequency The coordinates of the axis, spatial frequency The coordinates of the axis.

[0147] By making the implicit phase information in the interferometric image explicit and transforming it into physical features that can be used to train the improved EfficientNet-B0 model, the accuracy and robustness of identifying the degree of OAM mode degradation of vortex beams are improved.

[0148] The beneficial effects of the proposed method for measuring the transmission quality of vortex light in an air-sea environment are mainly reflected in the following aspects: When using the improved SPGD algorithm for phase compensation, the weights of the comprehensive performance index are dynamically and adaptively constructed based on the vortex beam and the air-sea vortex beam. This makes the air-sea vortex beam more closely match the vortex beam during phase compensation, thereby improving the transmission quality of the vortex beam. Finally, the improved EfficientNet-B0 model accurately tests the topological charge number (i.e., the identifier of the OAM mode) and the degree of OAM mode degradation (i.e., the comprehensive degradation of mode purity and beam quality) of the vortex beam, achieving precise measurement of the transmission quality of vortex light in an air-sea environment. Among these, accurate identification of the topological charge number is a fundamental prerequisite for ensuring error-free demodulation of information in the OAM multiplexing communication system; while the quantitative assessment of the degree of OAM mode degradation comprehensively reflects the systematic damage caused by complex environmental factors, such as the decrease in mode purity and wavefront distortion, providing quantitative data support for the practical system design of vortex light space laser communication.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0150] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0151] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A device for measuring the transmission quality of vortex light in an ocean-atmosphere environment, characterized in that, The measurement device for vortex light transmission quality under ocean-atmosphere conditions includes: an optical emission guidance unit, a vortex light generation device, a channel simulation unit, an adaptive optics compensation unit, an interferometric detection unit, and a data processing unit; the channel simulation unit is used to simulate the ocean-atmosphere environment. The optical emission guidance unit is used to emit signal light; The vortex light generating device is used to convert the signal light into a vortex beam; the vortex beam passes through the sea-atmosphere environment to generate a sea-atmosphere vortex beam; The adaptive optics compensation unit is used to dynamically construct a comprehensive performance index based on the air-sea vortex beam and the vortex beam, and based on the comprehensive performance index, use a modified SPGD algorithm to generate a control vector to perform phase compensation on the air-sea vortex beam to generate a compensated vortex beam. The interferometric detection unit is used to perform interference fusion of the compensated vortex light and the signal light to generate an interference image; The data processing unit is used to obtain the transmission quality data of the vortex light based on the interferometric image using an improved EfficientNet-B0 model; the transmission quality data includes: topological charge number and OAM mode degradation degree; the improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

2. The measuring device for vortex optical transmission quality in an ocean-atmosphere environment according to claim 1, characterized in that, The optical emission guiding unit includes, in sequence, a laser, a collimating beam expander, a polarizer, a first beam splitter, and a reflector; The laser emitted by the laser is first expanded and collimated by the collimating beam expander, and then transmitted through the polarizer to obtain polarized light; the polarized light is split into two signal beams by the first beam splitter prism; one signal beam enters the vortex light generating device; the other signal beam enters the interference detection unit through the reflector.

3. The measuring device for vortex optical transmission quality in an ocean-atmosphere environment according to claim 2, characterized in that, The vortex light generating device includes: A spatial light modulation controller is connected to both the spatial light modulator and the data processing unit, and is used to receive control commands from the data processing unit to control the spiral phase diagram of the spatial light modulator. The spatial light modulator is used to convert the signal light into a vortex beam through the spiral phase diagram.

4. The measuring device for vortex optical transmission quality in an ocean-atmosphere environment according to claim 1, characterized in that, The channel simulation unit includes a fog chamber and a turbulence pool. The fog chamber is used to generate simulated sea fog in the air-sea environment, and the turbulence pool is used to generate turbulence in the air-sea environment.

5. The measuring device for vortex optical transmission quality in an ocean-atmosphere environment according to claim 1, characterized in that, The adaptive optics compensation unit includes: The second beam splitter prism is disposed in the outgoing optical path of the channel simulation unit to split the ocean-air vortex beam into two ocean-air vortex beams; one ocean-air vortex beam enters the first CCD detector; the other ocean-air vortex beam enters the reflective deformable mirror. The first CCD detector is used to convert the ocean-atmosphere vortex beam into a first image; The reflective deformable mirror is used to perform phase compensation on the air-sea vortex beam to generate a compensated vortex beam. The third beam splitter prism is set in the output optical path of the reflective deformable mirror to split the compensated ocean-air vortex beam into two compensated vortex beams; one compensated vortex beam is injected into the second CCD detector; the other compensated vortex beam is injected into the interferometric detection unit. The second CCD detector is used to convert the compensated vortex beam into a second image; An optical compensation controller is connected to the data processing unit, the first CCD detector, the reflective deformable mirror, and the second CCD detector, respectively. It is used to receive control commands from the data processing unit, dynamically construct comprehensive performance indicators based on the ocean-atmosphere vortex beam and the vortex beam, and generate control vectors based on the comprehensive performance indicators using a modified SPGD algorithm to perform phase modulation on the reflective deformable mirror.

6. A method for measuring the transmission quality of vortex light in an ocean-atmosphere environment, characterized in that, The method for measuring the vortex light transmission quality in an ocean-atmosphere environment is applied to the measuring device for vortex light transmission quality in an ocean-atmosphere environment as described in claims 1-5. The method for measuring the vortex light transmission quality in an ocean-atmosphere environment includes: Acquire signal light and convert it into a vortex beam, which then passes through the ocean-atmosphere environment to generate an ocean-atmosphere vortex beam; Based on the air-sea vortex beam and the dynamic construction of the vortex beam, a comprehensive performance index is constructed. Based on the comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the air-sea vortex beam, thereby generating a compensated vortex beam. The compensated vortex light and the signal light are subjected to interference fusion to generate an interference image; The interferometric image is input into the improved EfficientNet-B0 model to obtain the transmission quality data of the vortex light; the transmission quality data includes: topological charge number and the degree of degradation of the OAM mode; the improved EfficientNet-B0 model adds a lightweight attention module and an early classification exit to the EfficientNet-B0 model.

7. The method for measuring the transmission quality of vortex light in an ocean-atmosphere environment according to claim 6, characterized in that, Based on the aforementioned air-sea vortex beam and the dynamic construction of a comprehensive performance index, and based on the comprehensive performance index, a modified SPGD algorithm is used to generate a control vector to perform phase compensation on the air-sea vortex beam, generating a compensated vortex beam, specifically including: Based on the aforementioned air-sea vortex beam and the dynamic construction of the vortex beam, a comprehensive performance index is established. Obtain the initial control vector; Based on the vortex light field of the air-sea vortex beam, the initial control vector is iterated multiple times using the improved SPGD algorithm until the comprehensive performance index reaches the threshold or the maximum number of iterations is reached, thereby generating the control vector. Phase compensation is performed on the air-sea vortex beam based on the control vector to obtain the compensated vortex beam.

8. The method for measuring the transmission quality of vortex light in an ocean-atmosphere environment according to claim 7, characterized in that, Based on the aforementioned air-sea vortex beam and the dynamic construction of the vortex beam, a comprehensive performance index is specifically constructed, including: Based on the vortex beam, determine the vortex light field and the area of ​​the vortex annular region of the vortex beam; Based on the aforementioned air-sea vortex beam, the vortex light field and phase of the air-sea vortex beam are determined; Based on the vortex light field of the vortex beam, the vortex light field of the air-sea vortex beam, and the phase of the air-sea vortex beam, calculate the sea fog intensity and turbulence intensity. Calculate the baseline weights based on the sea fog intensity and the turbulence intensity; The baseline weights are normalized to obtain adaptive weights; Based on the vortex light field of the aforementioned air-sea vortex beam, the mode purity index is calculated. Based on the vortex light field of the vortex beam, the estimated topological charge is calculated; Based on the estimated topological charge, calculate the performance index of phase topological integrity; Based on the area of ​​the vortex annular region, a performance index is calculated to determine the spatial distribution characteristics of the beam intensity. The performance indices of mode purity, phase topology integrity, and spatial distribution of beam intensity are normalized to obtain normalized mode purity, phase topology integrity, and spatial distribution of beam intensity. A comprehensive performance index is constructed based on the normalized mode purity index, the normalized phase topology integrity performance index, the normalized spatial distribution characteristics of beam intensity performance index, and the adaptive weights.

9. The method for measuring the transmission quality of vortex light in an ocean-atmosphere environment according to claim 8, characterized in that, The formula for calculating the benchmark weight is as follows: ； in, As the benchmark weight for the model purity index, As the initial weights, The first turbulence sensitivity coefficient, For the first Turbulence intensity estimation at the next iteration The first sea fog sensitivity coefficient, For the first Sea fog intensity estimation at the next iteration The benchmark weight for the performance index of phase topology integrity, The initial weights for the performance index of phase topology integrity, This is the second turbulence sensitivity coefficient. The second sea fog sensitivity coefficient, The benchmark weights for performance indicators representing the spatial distribution characteristics of beam intensity. The initial weights for the performance index of the spatial distribution characteristics of the beam intensity are given. This is the third turbulence sensitivity coefficient.

10. The method for measuring the transmission quality of vortex light in an ocean-atmosphere environment according to claim 6, characterized in that, The improved EfficientNet-B0 model comprises, in sequence, an initial convolutional layer, a first feature extraction module, a first early classification exit, a second feature extraction module, a second early classification exit, a third feature extraction module, a third early classification exit, a first MBConv module, a convolutional layer, an average pooling layer, and a fully connected layer; the first early classification exit, the second early classification exit, and the third early classification exit each consist of a global average pooling layer and a fully connected classifier; the first feature extraction module, the second feature extraction module, and the third feature extraction module each consist of a lightweight attention module and two MBConv modules; The interference image is input into the improved EfficientNet-B0 model through the data processing unit to obtain the transmission quality data of the vortex light, specifically including: The interference image is input into the initial convolutional layer for feature extraction to obtain an initial feature map; The initial feature map is input into the first feature extraction module for weighted feature extraction to obtain the first feature map; The first feature map is input to the first early classification exit to obtain the first confidence level and the transmission quality data of the first vortex light; if the first confidence level is greater than the confidence level threshold, the transmission quality data of the first vortex light is determined to be the transmission quality data of the vortex light; if the first confidence level is less than or equal to the confidence level threshold, the first feature map is input to the second feature extraction module for weighted feature extraction to obtain the second feature map. The second feature map is input to the second early classification exit to obtain the second confidence level and the transmission quality data of the second vortex light; if the second confidence level is greater than the confidence level threshold, the transmission quality data of the second vortex light is determined to be the transmission quality data of the vortex light; if the second confidence level is less than or equal to the confidence level threshold, the second feature map is input to the third feature extraction module for weighted feature extraction to obtain the third feature map. The third feature map is input to the third early classification exit to obtain the third confidence level and the transmission quality data of the third vortex light; if the third confidence level is greater than the confidence level threshold, the transmission quality data of the third vortex light is determined to be the transmission quality data of the vortex light; if the third confidence level is less than or equal to the confidence level threshold, the third feature map is input to the first MBConv module for weighted feature extraction to obtain the fourth feature map; The fourth feature map is sequentially passed through the convolutional layer, the average pooling layer, and the fully connected layer for feature extraction, dimensionality reduction, and classification to obtain the transmission quality data of the vortex light.

Images