A method and apparatus for detecting and locating a deviation in the pointing of a light beam

Abstract

The application provides a detection and positioning method and device for beam pointing deviation, and relates to the field of optical measurement. The method comprises the following steps: adjusting the spatial posture of a probe type non-polarization beam splitting component to an attitude locking state based on echo power data; under the attitude locking state, performing image preprocessing on main spot sampling data and calibration spot sampling data based on a spatial correspondence relationship to obtain standardized spot distribution data, and inputting an improved two-dimensional elliptical Gaussian fitting model to output main spot center coordinates, calibration spot center coordinates and spot shape parameters; and constructing an error correction mechanism for the pointing deviation based on the main spot center coordinates and the calibration spot center coordinates, and positioning a target section with deviation in the optical path through the error correction mechanism. The application solves the problem of difficult positioning of the real deviation source of the beam pointing deviation.

Description

Technical Field

[0001] This application relates to the field of optical measurement, and in particular to a method and apparatus for detecting and locating beam pointing deviation. Background Technology

[0002] In the field of cutting-edge laser and fiber optic technology, the spatial pointing stability and multi-port coupling accuracy of the beam are core technical indicators that determine the signal-to-noise ratio, energy transfer efficiency, and long-term reliability of the entire optical system. As optical systems evolve towards ultra-long operating distances, kilowatt-level high power, and highly miniaturized integration, the tolerance for beam pointing deviations has increased from the traditional milliradian level to the microradian level or even the level of a few arcseconds. Against this backdrop, achieving high-precision, reproducible pointing deviation detection and stable adjustment without interfering with the main optical path has become a critical problem that urgently needs to be solved.

[0003] Existing beam pointing deviation detection systems typically employ two methods for detecting and adjusting laser beam pointing deviation. One method involves permanently installing a beam splitter in the main optical path to guide a portion of the beam to a paraxial detector, indirectly determining the beam direction by observing changes in the beam spot position. The other method temporarily inserts a movable reflector or beam splitter into the optical path during the detection phase, using a position-sensitive detector to acquire beam spot information for manual or semi-automatic adjustment. Furthermore, in some complex optical paths, an autocollimator or laser tracking device is also used to measure the optical axis to assist in determining the optical path offset.

[0004] Although the above system can achieve a certain degree of beam pointing detection, it still has obvious defects: the long-term setting of the beam splitting element will introduce fixed losses in the main optical path, and may cause non-common mode errors due to thermal effects or device deformation, causing the detection results to deviate from the true optical axis state. Therefore, in the actual complex optical path transmission and multi-level optical coupling process, the existing beam pointing deviation detection system is difficult to locate the true source of beam pointing deviation.

[0005] Therefore, there is an urgent need for a method and device for detecting and locating beam pointing deviation. Summary of the Invention

[0006] This application provides a method and apparatus for detecting and locating beam pointing deviation, which solves the problem that existing beam pointing deviation detection systems have difficulty locating the true source of beam pointing deviation in actual complex optical path transmission and multi-level optical coupling processes.

[0007] The first aspect of this application provides a method for detecting and locating beam pointing deviation. The method includes: acquiring main beam sampling data, calibration beam sampling data, and echo power data; adjusting the spatial attitude of a probe-type non-polarizing beam splitter to an attitude-locked state based on the echo power data; using the probe-type non-polarizing beam splitter to establish a spatial correspondence between the main beam sampling data and the calibration beam sampling data; in the attitude-locked state, performing image preprocessing on the main beam sampling data and the calibration beam sampling data based on the spatial correspondence to obtain standardized beam distribution data; inputting the standardized beam distribution data into an improved two-dimensional elliptic Gaussian fitting model, and outputting the center coordinates of the main beam and the center coordinates of the calibration beam based on the improved two-dimensional elliptic Gaussian fitting model, while simultaneously acquiring beam shape parameters; constructing an error correction mechanism for pointing deviation based on the center coordinates of the main beam and the center coordinates of the calibration beam, and locating the target segment in the optical path that produces the deviation through the error correction mechanism.

[0008] Optionally, acquiring calibration spot sampling data specifically includes: acquiring application scenario information of the target optical path, including fiber-coupled scenarios and free-space optical path scenarios; in the fiber-coupled scenario, constructing a reference light coaxial with the main optical path using a target fiber collimator, and forming a first calibration spot on the detection plane using the reference light; extracting the sampling data corresponding to the first calibration spot as the first calibration spot sampling data; in the free-space optical path scenario, outputting a reference light using a preset reference collimator, and allowing the reference light to propagate through space and enter a preset detection path to form a second calibration spot on the detection plane; extracting the sampling data corresponding to the second calibration spot as the second calibration spot sampling data; and using either the first calibration spot sampling data or the second calibration spot sampling data as the calibration spot sampling data.

[0009] Optionally, adjusting the spatial attitude of the probe-type unpolarized beamsplitter to an attitude-locked state based on echo power data specifically includes: determining whether the echo power data meets a preset extreme value condition; if the echo power data does not meet the preset extreme value condition, introducing a small-amplitude periodic perturbation and obtaining an echo power response change sequence; constructing local gradient information based on the echo power response change sequence, and determining the adjustment direction and step size of the angular attitude parameters based on the local gradient information; the angular attitude parameters include pitch angle and yaw angle; adaptively adjusting the probe-type unpolarized beamsplitter based on the determined angular attitude parameters, and updating the echo power data after each adjustment; when the echo power data meets the preset extreme value condition, establishing an attitude-locked state based on the current angular attitude parameters.

[0010] Optionally, an improved two-dimensional elliptic Gaussian fitting model is constructed, specifically including: determining the spot distribution constraint type based on application scenario information, where the spot distribution constraint type includes fiber coupling constraint type and free space propagation constraint type; under the fiber coupling constraint type, outputting symmetrical distribution features based on the main spot sampling data and calibration spot sampling data, and constructing a first set of constraint parameters through the symmetrical distribution features; the symmetrical distribution features include energy radial distribution consistency features and principal axis direction stability features; under the free space propagation constraint type, outputting asymmetric extension features based on the main spot sampling data and calibration spot sampling data, and constructing a second set of constraint parameters through the asymmetric extension features; the asymmetric extension features include energy distribution skewness features and principal axis direction deflection features; and constructing an improved two-dimensional elliptic Gaussian fitting model based on the first set of constraint parameters or the second set of constraint parameters.

[0011] Optionally, an error correction mechanism for pointing deviation is constructed based on the center coordinates of the main spot and the center coordinates of the calibration spot. This mechanism is then used to locate the target segment in the optical path that exhibits deviation. Specifically, this includes: calculating the spatial offset based on the center coordinates of the main spot and the center coordinates of the calibration spot, and performing angle mapping processing on the spatial offset using the equivalent propagation distance to obtain initial pointing deviation parameters; constructing time-series pointing deviation data based on the continuously acquired initial pointing deviation parameters from multiple frames, and performing dynamic filtering on the time-series pointing deviation data to obtain stable pointing deviation parameters; and constructing an error discrimination relationship based on the spot morphology parameters. The system performs error compensation processing on the stable pointing deviation parameter based on the error discrimination relationship to obtain the corrected pointing deviation parameter. When the application scenario is an optical fiber coupling scenario, the constructed error discrimination relationship includes a first discrimination relationship based on symmetry characteristics and a second discrimination relationship based on energy distribution stability. When the application scenario is a free space optical path scenario, the constructed error discrimination relationship includes a third discrimination relationship based on morphological change characteristics and a fourth discrimination relationship based on directional change characteristics. A spatial propagation mapping model is constructed based on the corrected pointing deviation parameter, and the target segment in the optical path that produces the deviation is located through the spatial propagation mapping model.

[0012] Optionally, a time-series pointing deviation data is constructed based on the initial pointing deviation parameters of multiple continuously acquired frames, and dynamic filtering is performed on the time-series pointing deviation data to obtain stable pointing deviation parameters. Specifically, this includes: organizing the initial pointing deviation parameters of multiple continuously acquired frames in chronological order to construct time-series pointing deviation data; performing trend term and random disturbance component decomposition processing on the time-series pointing deviation data to obtain trend deviation components and disturbance deviation components; adjusting the weight of the disturbance deviation components based on the trend deviation components, and calculating the predicted pointing deviation value at the current time based on the weighted disturbance deviation components; fusing the predicted pointing deviation value with the actual pointing deviation value at the current time to obtain a fused deviation parameter; performing adaptive smoothing processing on the fused deviation parameter, and using the processed fused deviation parameter as the stable pointing deviation parameter.

[0013] Optionally, a spatial propagation mapping model is constructed based on the corrected pointing deviation parameter, and the target segment in the optical path that generates the deviation is located using the spatial propagation mapping model. Specifically, this includes: constructing a spatial propagation correlation relationship corresponding to the optical path structure based on the corrected pointing deviation parameter; decomposing the corrected pointing deviation parameter into multiple segment deviation components based on the spatial propagation correlation relationship, and establishing a spatial mapping correspondence between the segment deviation components and the corresponding optical path segments; calculating the deviation contribution value of the corresponding optical path segment through the segment deviation components according to the spatial mapping correspondence; sorting the various deviation contribution values, and selecting the optical path segments that meet the preset ranking conditions as target segments according to the sorting results.

[0014] A second aspect of this application provides a device for detecting and locating beam pointing deviation, the device including an acquisition module and a processing module, wherein, The acquisition module is used to acquire the main spot sampling data, the calibration spot sampling data, and the echo power data; The acquisition module is used to adjust the spatial attitude of the probe-type non-polarization beam splitter to an attitude-locked state based on the echo power data; the probe-type non-polarization beam splitter is used to establish the spatial correspondence between the main spot sampling data and the calibration spot sampling data. The acquisition module is used to perform image preprocessing on the main spot sampling data and calibration spot sampling data based on spatial correspondence in the attitude-locked state to obtain standardized spot distribution data.

[0015] The processing module is used to input standardized spot distribution data into the improved two-dimensional elliptical Gaussian fitting model, and output the coordinates of the main spot center and the coordinates of the calibration spot center based on the improved two-dimensional elliptical Gaussian fitting model, while simultaneously acquiring the spot morphology parameters. The processing module is used to construct an error correction mechanism for pointing deviation based on the center coordinates of the main spot and the center coordinates of the calibration spot, and to locate the target segment in the optical path that has deviation through the error correction mechanism.

[0016] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0017] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program, the computer program being executed by a processor using any of the methods described above.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Adjust the spatial attitude of the probe-type non-polarization beam splitter to attitude lock state based on echo power data; in attitude lock state, perform image preprocessing on the main spot sampling data and calibration spot sampling data based on spatial correspondence to obtain standardized spot distribution data, and input it into an improved two-dimensional elliptical Gaussian fitting model to output the center coordinates of the main spot, the center coordinates of the calibration spot, and the spot morphology parameters; calculate the spatial offset based on the center coordinates of the main spot and the calibration spot and obtain the initial pointing deviation parameters by angle mapping; construct time series pointing deviation data and dynamically filter to obtain stable pointing deviation parameters; perform error compensation based on the spot morphology parameters to obtain corrected pointing deviation parameters to locate the target segment, thereby achieving high-precision measurement, stable characterization, and segment-level positioning of the source of deviation for beam pointing deviation, improving the calibration accuracy and operational reliability of the optical system.

[0019] 2. Based on application scenario information, determine the beam distribution constraint type, which includes fiber coupling constraint type and free space propagation constraint type. Under the fiber coupling constraint type, output symmetrical distribution characteristics based on the main beam sampling data and calibration beam sampling data, and construct a first constraint parameter set through the symmetrical distribution characteristics. The symmetrical distribution characteristics include energy radial distribution consistency characteristics and principal axis direction stability characteristics. Under the free space propagation constraint type, output asymmetric extension characteristics based on the main beam sampling data and calibration beam sampling data, and construct a second constraint parameter set through the asymmetric extension characteristics. The asymmetric extension characteristics include energy distribution skewness characteristics and principal axis direction deflection characteristics. Based on the first constraint parameter set or the second constraint parameter set, construct an improved two-dimensional elliptical Gaussian fitting model, so that the improved two-dimensional elliptical Gaussian fitting model can adapt to the beam distribution characteristics under different application scenarios, improve the extraction accuracy of the main beam center coordinates and calibration beam center coordinates while ensuring fitting stability, and enhance the ability of beam morphology parameters to represent the actual beam propagation state.

[0020] 3. The initial pointing deviation parameters of multiple consecutively acquired frames are organized in chronological order to construct time-series pointing deviation data. The time-series pointing deviation data is decomposed into trend term and random disturbance component to obtain trend deviation component and disturbance deviation component. The disturbance deviation component is weighted based on the trend deviation component, and the predicted pointing deviation value at the current moment is calculated based on the weighted disturbance deviation component. The predicted pointing deviation value and the actual pointing deviation value at the current moment are fused to obtain the fused deviation parameter. The fused deviation parameter is adaptively smoothed, and the processed fused deviation parameter is used as the stable pointing deviation parameter. This effectively separates the slow-changing trend and high-frequency disturbance components in the pointing deviation, suppresses the influence of random noise and instantaneous fluctuations on the measurement results, improves the stability and continuity of pointing deviation estimation, and enhances the dynamic tracking capability of real beam pointing changes. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a method for detecting and locating beam pointing deviation provided in an embodiment of this application; Figure 2 This is a schematic diagram of a beam pointing deviation detection structure under optical fiber coupling conditions provided in an embodiment of this application; Figure 3 This is a schematic diagram of a beam pointing deviation detection structure under free space optical path conditions provided in an embodiment of this application; Figure 4 This is a schematic diagram of a module for a beam pointing deviation detection and positioning device provided in an embodiment of this application; Figure 5This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached drawings: 41. Acquisition module; 42. Processing module; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification 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.

[0024] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0025] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0026] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] Please refer to Figure 1 The flowchart illustrates a method for detecting and locating beam pointing deviation provided in an embodiment of this application. The flowchart mainly includes the following steps: S101 to S105.

[0028] Step S101: Acquire the main spot sampling data, calibration spot sampling data, and echo power data.

[0029] Specifically, in existing technologies, the detection and adjustment of laser beam pointing deviation typically employs two methods. One method involves permanently installing a beam splitter in the main optical path to guide a portion of the beam to a paraxial detector, indirectly determining the beam direction by observing changes in the beam spot position. The other method involves temporarily inserting a movable reflector or beam splitter into the optical path during the detection phase, using a position-sensitive detector to acquire beam spot information for manual or semi-automatic adjustment. Furthermore, in some complex optical paths, an autocollimator or laser tracking device is also used to measure the optical axis to assist in determining the optical path offset.

[0030] While the aforementioned existing technologies can achieve a certain degree of beam pointing detection, they still have significant drawbacks. The method of permanently setting up the beam splitter introduces fixed losses in the main optical path and may cause non-common-mode errors due to thermal effects or device deformation, leading to deviations from the true optical axis. The method of temporarily inserting detection elements relies on mechanical positioning accuracy, which easily introduces repeatability errors, causing the measurement results to be mixed with the device's own errors, making it difficult to distinguish the true source of deviation. In complex free-space optical paths, traditional beam pointing deviation detection systems lack a unified reference standard, making it difficult to establish an effective correspondence between the detection results and specific optical path segments, resulting in difficulty in locating the source of deviation. Furthermore, existing technologies typically rely solely on single-frame spot size or simple centroid calculations for judgment, lacking the comprehensive processing capability for spot morphology changes, dynamic disturbances, and system errors, making it difficult to guarantee detection stability and reliability under microradian-level accuracy requirements.

[0031] To address the aforementioned issues, this application proposes a beam pointing deviation detection method based on a probe-type non-polarization beam splitter. By constructing a unified system of spot sampling data and echo power data, optical locking of the probe attitude is achieved, thereby eliminating mechanical insertion errors. Furthermore, pointing deviation is obtained by comparing the main spot and the calibration spot on the same detection plane. Combined with time-series filtering, spot morphology compensation, and system error correction, high-precision and stable calculations are achieved. Further, by constructing a spatial propagation mapping model, the pointing deviation is correlated with the optical path structure, enabling the segmental location of the deviation source. This scheme achieves a unified high-precision detection and fault tracing capability without introducing permanent optical path loss, effectively overcoming the problems of measurement error aliasing, insufficient accuracy, and difficulty in location in existing technologies.

[0032] To achieve the above technical solution, it is first necessary to acquire main beam sampling data, calibration beam sampling data, and echo power data. The main beam sampling data is acquired through a position-sensitive beam detector or image sensor. Essentially, this data represents the spatial distribution information of the main beam on the detector surface, typically including a two-dimensional intensity distribution matrix, beam center coordinates, and beam size parameters. The calibration beam sampling data is acquired in conjunction with the reference beam generation component and detector. While similar in form to the main beam sampling data, this data reflects the performance of the reference beam on the same detector surface and also typically includes a two-dimensional intensity distribution and reference beam center coordinates. The echo power data is acquired through an optical power detector. This data is a scalar sequence that changes over time or during adjustment, reflecting the coupling efficiency of the reference beam as it returns along the optical path. Typical data include the current echo power value, the curve of echo power change with attitude adjustment, peak power, and power change rate.

[0033] In one possible implementation, step S101 further includes: acquiring application scenario information of the target optical path, the application scenario information including fiber-coupled scenario and free-space optical path scenario; in the fiber-coupled scenario, constructing a reference light coaxial with the main optical path using a target fiber collimator, and forming a first calibration spot on the detection plane using the reference light; extracting the sampling data corresponding to the first calibration spot as the first calibration spot sampling data; in the free-space optical path scenario, outputting a reference light using a preset reference collimator, and allowing the reference light to propagate through space and enter a preset detection path to form a second calibration spot on the detection plane; extracting the sampling data corresponding to the second calibration spot as the second calibration spot sampling data; and using the first calibration spot sampling data or the second calibration spot sampling data as the calibration spot sampling data.

[0034] Specifically, one improvement of this application is its ability to adapt to optical path structure scenarios under different application conditions. For fiber coupling scenarios, which are typical structural scenarios where the main light source needs to be coupled into an optical fiber or fiber collimator, such as the injection end of a high-power fiber laser, the transmitting end of an optical fiber communication system, or a precision optical fiber transmission system, the main optical path typically uses the collimator as the coupling target. The beam must be incident strictly along the optical axis of the collimator; otherwise, the coupling efficiency will decrease or even damage the device.

[0035] In fiber optic coupling scenarios, please refer to... Figure 2This illustration shows a schematic diagram of a beam pointing deviation detection structure under fiber coupling conditions provided in an embodiment of this application. In this structure, the calibration light is reflected back through a collimator, propagates along a direction coaxial with the main light path, and enters a probe-type unpolarized beam splitter, forming a calibration spot on the detection plane. Simultaneously, the main beam emitted by the main light source is split by the probe-type unpolarized beam splitter, forming a main spot on the same detection plane. Since the calibration light and the main light maintain a coaxial relationship in the spatial path, the formed calibration spot can serve as an ideal pointing reference. Therefore, by comparing the positional difference between the main spot and the calibration spot, accurate detection of beam pointing deviation is achieved, i.e., the reference light forms a first calibration spot on the detection plane.

[0036] After the reference light forms the first calibration spot on the detector plane, it is necessary to acquire and process the sampling data of the first calibration spot to obtain the first calibration spot sampling data that can be used for subsequent calculations. The first calibration spot is the spatial light intensity distribution area of ​​the reference light on the detector plane, and its center position corresponds to the ideal pointing position of the reference light, usually exhibiting an approximately Gaussian distribution or an elliptical distribution characteristic.

[0037] In the specific processing, the first calibration spot is first acquired by a spot position detector or image sensor set at the detection plane to obtain the original spot image data. The original spot image data can be represented as a two-dimensional light intensity distribution matrix, where the value of each pixel corresponds to the light intensity at that spatial location, thus completely characterizing the spatial energy distribution of the first calibration spot.

[0038] After obtaining the two-dimensional light intensity distribution matrix, feature extraction processing is performed on the first calibration spot. By analyzing the light intensity distribution, the center coordinates of the first calibration spot can be extracted to represent the actual pointing position of the reference light on the detection plane; at the same time, the morphological feature parameters of the spot can also be extracted, including the peak intensity, energy concentration, length of the major and minor axes, and principal axis direction, to characterize the spatial distribution morphology and stability of the spot.

[0039] Subsequently, the two-dimensional light intensity distribution matrix, along with the center coordinates and morphological feature parameters, are uniformly organized and encapsulated to form a structured dataset, thereby obtaining the first calibration spot sampling data. Through the above processing, the first calibration spot sampling data not only contains complete light intensity distribution information but also key positional parameters that reflect the pointing characteristics of the reference light, providing a foundation for subsequent comparative analysis with the main spot.

[0040] In a free-space light path scene, please refer to... Figure 3This illustration shows a schematic diagram of a beam pointing deviation detection structure under free-space optical path conditions provided in an embodiment of this application. In this structure, the reference light is output from a preset reference collimator, guided by a circulator, and spatially correlated with the main light path by a sampling mirror. It then propagates along a preset detection path into a probe-type unpolarized beam splitter, forming a corresponding calibration spot on the detection plane. Simultaneously, the main beam emitted by the main light source propagates along the main light path, is split by the probe-type unpolarized beam splitter, and also forms a main spot on the detection plane. Since the reference light does not propagate along the original main light path but is introduced into the detection path through the sampling mirror, it forms a spot distribution on the detection plane that has a spatial correspondence with the main light; that is, the reference light forms a second calibration spot on the detection plane. Figure 2 or Figure 3 In this context, probe-type non-polarizing beam splitters are all optical path folding structures composed of an integrated non-polarizing beam splitter and a reflector.

[0041] After acquisition, feature extraction processing is performed on the second calibration spot. This process is similar to the extraction process of the first calibration spot sampling data. Both processes involve collecting the light intensity distribution and extracting the center position and morphological parameters of the spot to obtain the second calibration spot sampling data.

[0042] Based on the different application scenarios mentioned above, the first or second calibration spot sampling data is selected as the calibration spot sampling data, and subsequent spot center coordinate extraction, pointing deviation parameter calculation, and pointing deviation compensation processing are performed.

[0043] Step S102: Adjust the spatial attitude of the probe-type non-polarization beam splitter to the attitude lock state based on the echo power data.

[0044] Specifically, the spatial attitude of the probe-type unpolarized beam splitter is closed-loop adjusted using echo power data. By continuously adjusting the angular attitude of the probe-type unpolarized beam splitter and monitoring the echo power changes in real time, the echo power is gradually brought closer to a preset extreme value. When the extreme value is reached, the spatial attitude of the probe-type unpolarized beam splitter is locked, thus establishing a stable reference at the optical level. In this process, the probe-type unpolarized beam splitter is used to establish the spatial correspondence between the main beam sampling data and the calibration beam sampling data. The spatial correspondence refers to the one-to-one geometric mapping between the positions of the main beam and the calibration beam on the same detection plane; that is, the relative position difference between the two only reflects the pointing deviation of the main beam relative to the reference light, and does not include the additional offset introduced by probe attitude error. For example, in... Figure 1 In the process, the reference light propagates in the opposite direction along the principal light path and enters the probe-type non-polarizing beam splitter coaxially with the principal light. After beam splitting and reflection, it images with the principal light on the same detection plane, thus forming a coaxial mapping relationship. Figure 2In this process, the reference light is introduced into the detection path through the sampling mirror and then enters the probe-type unpolarized beam splitter. It then undergoes the same beam splitting and folding path to be imaged on the detection plane, forming an equivalent spatial correspondence with the principal light on the detection plane. Therefore, while the spatial correspondence may differ physically along different scenarios, the geometric mapping on the detection plane remains consistent.

[0045] In one possible implementation, step S102 further includes: determining whether the echo power data meets a preset extreme value condition; if the echo power data does not meet the preset extreme value condition, introducing a micro-amplitude periodic perturbation and obtaining an echo power response change sequence; constructing local gradient information based on the echo power response change sequence, and determining the adjustment direction and step size of the angular attitude parameters based on the local gradient information; the angular attitude parameters include pitch angle and yaw angle; adaptively adjusting the probe-type non-polarization beam splitter based on the determined angular attitude parameters, and updating the echo power data after each adjustment; when the echo power data meets the preset extreme value condition, establishing an attitude locking state based on the current angular attitude parameters.

[0046] Specifically, in this embodiment, a closed-loop control process for attitude adjustment is constructed by using echo power data as a feedback signal. First, the currently acquired echo power data is subjected to extreme value determination. When the echo power data does not reach the preset extreme value condition, in order to avoid the problem that traditional single-direction search is prone to getting trapped in local extreme values ​​or being interfered with by noise, a micro-amplitude periodic perturbation is introduced near the current attitude. This causes the probe-type non-polarization beam splitter to produce small periodic changes in the pitch and yaw angle directions, and the corresponding echo power change data is acquired simultaneously, thereby obtaining the echo power response change sequence.

[0047] After obtaining the echo power response change sequence, local variation analysis is performed on this sequence to extract the power response trend to attitude changes, thereby constructing local gradient information. Specifically, an approximate gradient can be obtained by performing a difference operation on the discrete data of echo power changing with angle. The gradient reflects the directionality of power changes near the current attitude. The gradient can be determined in the following way:

[0048]

[0049] in, This represents the power change gradient corresponding to the angular attitude. This represents the current angular attitude parameter, which can be either pitch or yaw. This indicates the amplitude of the applied small disturbance. This represents the echo power data under the corresponding attitude. The formula calculates the local slope using the central difference method, which is used to determine the increasing or decreasing trend of echo power with attitude changes.

[0050] Based on local gradient information, the direction of attitude adjustment can be determined. A positive gradient indicates that the power in the current direction increases with the angle, and adjustment should continue in that direction. A negative gradient indicates that adjustment should be made in the opposite direction. Simultaneously, to avoid oscillations or overshoot during the adjustment process, the adjustment step size can be adaptively determined based on the gradient magnitude. A larger gradient indicates distance from the extreme point, and the step size can be appropriately increased; a smaller gradient indicates proximity to the extreme point, and the step size should be decreased for fine-tuning. The step size can be determined as follows:

[0051]

[0052] in, Indicates the current adjustment step size. This is a proportionality coefficient used to adjust the sensitivity to changes in step size. This represents the absolute value of the gradient. This relationship enables the system to converge quickly when far from the optimum and to perform a detailed search when approaching the optimum.

[0053] Subsequently, based on the adjustment direction and step size, the pitch and yaw angles of the probe-type unpolarized beam splitter are adaptively adjusted, and echo power data is reacquired after each adjustment, thereby continuously updating the echo power data and repeating the above process. In this way, the attitude parameters gradually approach the position in two-dimensional angular space that maximizes the echo power.

[0054] When the echo power data meets the preset extreme value condition, it indicates that the return path of the reference light and the target optical axis have reached the optimal alignment state. At this time, the current pitch angle and yaw angle are used as reference attitude parameters, and an attitude lock state is established based on these reference attitude parameters. This attitude lock state represents the optimal alignment position of the probe-type unpolarized beam splitter in an optical sense, thus providing a stable basis for establishing the spatial correspondence between the main beam spot and the calibration beam spot.

[0055] Step S103: In the attitude-locked state, image preprocessing is performed on the main spot sampling data and calibration spot sampling data based on the spatial correspondence to obtain standardized spot distribution data.

[0056] Specifically, in the attitude-locked state, since the spatial attitude of the probe-type non-polarization beam splitter has been stably locked, the main spot sampling data and the calibration spot sampling data have a stable one-to-one correspondence on the same detection plane. Therefore, under the constraint of this spatial correspondence, the two types of spot data can be jointly preprocessed to obtain standardized spot distribution data that can be used for subsequent high-precision calculations.

[0057] First, spatial registration processing is performed on the sampling data of the main light spot and the calibration light spot. Specifically, based on the spatial correspondence, the two-dimensional light intensity distribution data of the two types of light spots are mapped to a unified coordinate system, so that the relative positional relationship between the main light spot and the calibration light spot on the detection plane is consistent, thereby eliminating the offset effect caused by the difference in the initial sampling position.

[0058] Subsequently, background suppression processing was performed on the registered spot data. By statistically analyzing the light intensity distribution in the non-spot region of the detector plane, a background noise model was constructed, and the background component was subtracted from the original light intensity matrix to reduce the impact of ambient light interference and detector dark current on the spot data.

[0059] After background suppression, the light spot data is normalized. By scaling the light intensity values ​​within the light spot area, the light intensity distribution across different sampling frames has a uniform numerical range, thus avoiding amplitude differences caused by light source fluctuations or detector response variations from affecting subsequent processing.

[0060] Furthermore, outlier suppression processing is performed on the normalized spot data. For any isolated noise points or abnormal pixels that may exist in the spot image, neighborhood consistency discrimination or threshold filtering methods are used to correct these abnormal pixels, thereby improving the continuity and stability of the spot distribution data.

[0061] Finally, the processed main spot data and calibration spot data are uniformly packaged to form standardized spot distribution data.

[0062] Step S104: Input the standardized spot distribution data into the improved two-dimensional elliptical Gaussian fitting model, and output the coordinates of the main spot center and the coordinates of the calibration spot center based on the improved two-dimensional elliptical Gaussian fitting model, while simultaneously acquiring the spot morphology parameters.

[0063] Specifically, standardized light spot distribution data is input into an improved two-dimensional elliptical Gaussian fitting model. The spatial light intensity distribution of the light spot is parametrically fitted using the improved two-dimensional elliptical Gaussian fitting model. During the fitting process, scene constraints are combined to limit the model parameters, so that the center position and morphological features of the light spot can be stably solved. After the model converges, the coordinates of the main light spot center and the calibration light spot center are output, and the light spot morphological parameters are obtained simultaneously, thus providing basic data for subsequent pointing deviation calculation and error discrimination.

[0064] In one possible implementation, step S104 further includes: determining the beam distribution constraint type based on application scenario information, wherein the beam distribution constraint type includes fiber coupling constraint type and free space propagation constraint type; under the fiber coupling constraint type, outputting symmetrical distribution features based on the main beam sampling data and calibration beam sampling data, and constructing a first constraint parameter set through the symmetrical distribution features; the symmetrical distribution features include energy radial distribution consistency features and principal axis direction stability features; under the free space propagation constraint type, outputting asymmetric extension features based on the main beam sampling data and calibration beam sampling data, and constructing a second constraint parameter set through the asymmetric extension features; the asymmetric extension features include energy distribution skewness features and principal axis direction deflection features; and constructing an improved two-dimensional elliptic Gaussian fitting model based on the first constraint parameter set or the second constraint parameter set.

[0065] Specifically, the type of beam distribution constraint is determined based on application scenario information. Application scenario information characterizes whether the current optical path belongs to an fiber-coupled scenario or a free-space optical path scenario. In a fiber-coupled scenario, the beam is typically output through a collimator and exhibits high spatial symmetry; in a free-space optical path scenario, the beam undergoes multiple levels of reflection, refraction, or sampling structures, easily leading to asymmetric propagation. Therefore, the beam distribution constraint type is classified into fiber-coupled constraint type and free-space propagation constraint type.

[0066] Under fiber coupling constraints, the sampling data of the main spot and the calibration spot are analyzed to output symmetric distribution characteristics. These characteristics include radial energy distribution uniformity and principal axis stability. Radial energy distribution uniformity characterizes the uniformity of energy distribution in different radial directions, which can be obtained by comparing the energy integral differences in different directions. Principal axis stability characterizes the stability of the principal axis direction, i.e., the degree of change in the major axis direction across different sampling frames. Based on these characteristics, a first set of constraint parameters is constructed to limit the shape parameters of the fitted model, making it closer to a symmetric distribution.

[0067] Under free-space propagation constraints, the sampling data of the main beam and the calibration beam are analyzed to output asymmetric propagation features. These asymmetric propagation features include energy distribution skewness characteristics and principal axis deflection characteristics. The energy distribution skewness characteristic characterizes the energy shift of the beam in different directions and can be obtained by calculating the offset between the beam's centroid and geometric center. The principal axis deflection characteristic characterizes the degree of deflection of the beam's principal axis relative to the reference direction. Based on these features, a second set of constraint parameters is constructed to describe the morphological distortion of the beam during propagation.

[0068] After obtaining either the first or second set of constraint parameters, an improved two-dimensional elliptic Gaussian fitting model is constructed. The model is based on an extension of the two-dimensional elliptic Gaussian function, and its basic expression is as follows:

[0069]

[0070] in, Indicates coordinates The light intensity value at that location, This indicates the peak intensity of the light spot. Indicates the coordinates of the center of the light spot. and These represent the extent of the light spot's expansion in two orthogonal directions. The model describes the distribution of light intensity as it gradually decreases from the center outwards using an exponential decay method.

[0071] Based on this, the rotation angle parameter is introduced. To characterize the principal axis direction of the light spot, a rotational transformation is performed on the coordinates:

[0072] in, and The coordinates are after rotation. This represents the angle of the principal axis of the light spot. This transformation can be used to describe elliptical light spots in any direction.

[0073] By combining the set of constraint parameters, restrictions are imposed on the model parameters. For example, under the fiber coupling constraint type, the following constraints are applied: and The difference is constrained to make it consistent, so as to reflect symmetry; at the same time, The range of variation is limited to keep the principal axis direction stable. Under the free-space propagation constraint type, then... and There are differences, and a skew modulation term is introduced to describe the uneven energy distribution.

[0074] During the model solving process, iterative optimization methods can be used to estimate the parameters, minimizing the error between the model output and the standardized spot distribution data. Through this process, the coordinates of the main spot center and the calibration spot center can be obtained, along with spot morphology parameters, including the principal axis direction, extent of expansion, and symmetry index.

[0075] In this application, the improved two-dimensional elliptical Gaussian fitting model transforms the original spot distribution data into a stable and physically meaningful parameter representation, enabling subsequent pointing deviation calculations to be performed based on high-precision center coordinates. At the same time, it utilizes morphological parameters to provide a basis for error discrimination, thereby achieving high-precision beam pointing detection and stability analysis in different scenarios.

[0076] Step S105: Based on the center coordinates of the main spot and the center coordinates of the calibration spot, an error correction mechanism for pointing deviation is constructed, and the target segment in the optical path that has deviation is located through the error correction mechanism.

[0077] In one possible implementation, step S105 further includes steps S1051 to S1054: Step S1051: Calculate the spatial offset based on the coordinates of the main spot center and the coordinates of the calibration spot center, and perform angle mapping processing on the spatial offset in combination with the equivalent propagation distance to obtain the initial pointing deviation parameter.

[0078] Specifically, the spatial offset of the main beam center coordinates and the calibration beam center coordinates on the detection plane is calculated. The main beam center coordinates characterize the actual pointing position of the beam, while the calibration beam center coordinates characterize the ideal pointing position of the reference beam. Therefore, the difference between the two reflects the actual beam offset. The spatial offset can be represented as a two-dimensional offset vector, and its calculation method is as follows:

[0079]

[0080] in, and These represent the positional offsets in the horizontal and vertical directions, respectively. Indicates the coordinates of the center of the main light spot. This represents the coordinates of the calibration spot center. This expression yields the relative positional offset of the two spots on the detection plane through direct difference calculation.

[0081] After obtaining the two-dimensional offset, the magnitude of the spatial offset can be further calculated:

[0082] in, This represents the Euclidean distance between the main beam spot and the calibration beam spot, used to characterize the overall offset. Since there is a geometric relationship between the positional offset on the detector plane and the angular deviation of the beam, the spatial offset needs to be mapped to the angular deviation using the equivalent propagation distance. The equivalent propagation distance represents the equivalent propagation length of the beam from the reference reference to the detector plane; its value can be determined based on the system's optical path structure, such as the distance between the detector and the reference collimator or the equivalent optical focal length. The angular mapping relationship can be expressed as:

[0083]

[0084] in, Indicates the beam pointing deviation angle. This represents the equivalent propagation distance. This relationship is based on the principles of geometric optics, where the arctangent of the right triangle formed by the planar offset and the propagation distance is calculated to obtain the angular deviation.

[0085] In practical applications, when the offset is small relative to the propagation distance, it can be approximated as follows:

[0086] This approximation is applicable to scenarios with small angular deviations, helping to simplify the calculation process and improve computational efficiency. Furthermore, to obtain directional information, the angular deviations in the horizontal and vertical directions can be calculated separately.

[0087]

[0088] in, and These represent the pointing deviation components in two orthogonal directions. Through the above calculations, the spatial offset can be converted into angular deviation parameters, thus obtaining the initial pointing deviation parameters. These parameters contain not only the magnitude of the deviation but also its direction information, providing the basic input for subsequent dynamic filtering, error compensation, and deviation localization.

[0089] Step S1052: Construct time-series pointing deviation data based on the initial pointing deviation parameters of multiple continuously acquired frames, and perform dynamic filtering on the time-series pointing deviation data to obtain stable pointing deviation parameters.

[0090] Specifically, by organizing and modeling the initial pointing deviation parameters of multiple continuously acquired frames in the time dimension, time-series pointing deviation data is constructed to characterize the dynamic change process of beam pointing over a period of time. On this basis, dynamic filtering is performed on the time-series pointing deviation data to suppress random errors caused by environmental disturbances, system noise and instantaneous fluctuations, and the stable trend of change is extracted to obtain stable pointing deviation parameters, providing a reliable basis for subsequent deviation compensation and positioning analysis.

[0091] In one possible implementation, step S1052 further includes: organizing the initial pointing deviation parameters of multiple consecutively acquired frames in chronological order and constructing time-series pointing deviation data; performing trend term and random disturbance component decomposition processing on the time-series pointing deviation data to obtain trend deviation component and disturbance deviation component; adjusting the weight of the disturbance deviation component based on the trend deviation component, and calculating the predicted pointing deviation value at the current time based on the weighted disturbance deviation component; fusing the predicted pointing deviation value with the actual pointing deviation value at the current time to obtain a fused deviation parameter; performing adaptive smoothing processing on the fused deviation parameter, and using the processed fused deviation parameter as a stable pointing deviation parameter.

[0092] Specifically, the initial pointing deviation parameters from multiple consecutively acquired frames are organized chronologically to construct a time-series pointing deviation data. To more accurately describe its dynamic changes, the time series is represented as a continuous-time function, denoted as […]. ,in Represents a time variable. This indicates the pointing deviation value at the corresponding moment. This representation method can simultaneously reflect the slow drift trend of the beam pointing as well as the rapid fluctuations caused by environmental factors.

[0093] Based on this, the time series pointing deviation data undergoes joint decomposition of trend term and random disturbance component. A variational optimization model is constructed to simultaneously solve for the trend and disturbance components:

[0094]

[0095] in, This represents the trend deviation component, used to characterize the slow change trend of the beam direction; It represents the disturbance deviation component, used to characterize rapid fluctuations caused by air disturbances, mechanical vibrations, etc. and The parameters are adjusted to balance trend smoothness and perturbation sparsity; The first derivative of the trend component is used to constrain the smoothness of trend changes; Indicates the first A frequency domain component extraction operator is used to extract high-frequency components from the disturbance components. This term enhances the ability to separate random disturbances through frequency domain constraints.

[0096] After obtaining the trend deviation component and the disturbance deviation component, the disturbance deviation component is weighted based on the trend deviation component. To ensure that the weights reflect the degree of disturbance impact over different time periods, a weighting function based on energy distribution is introduced:

[0097]

[0098] in, This represents the time-related weighting function. Indicates the length of the time window, used to define the energy calculation interval. To prevent tiny positive numbers with a denominator of zero, this weight is calculated by comparing trend energy with disturbance energy. When the disturbance is strong, the weight is reduced, thus mitigating its impact.

[0099] Based on the weighted perturbation bias components, the prediction pointing bias value at the current time is calculated. This prediction uses an integral form with time decay characteristics:

[0100]

[0101] in, This represents the prediction bias value. The exponential decay term is used to enhance the impact of recent disturbances on the prediction results while weakening the effect of historical disturbances, thereby improving the timeliness of the prediction.

[0102] Subsequently, the predicted pointing deviation value is fused with the actual pointing deviation value at the current moment. To avoid the excessive influence of sudden disturbances on the results, a nonlinear fusion expression is introduced:

[0103]

[0104] in, Indicates the fusion deviation parameter. This is an adjustment coefficient used to control the degree of influence of the residuals on the fusion result. This expression prevents large fluctuations from being directly transmitted to the final result by nonlinearly compressing the residuals.

[0105] Finally, the fusion bias parameter is adaptively smoothed to further suppress high-frequency noise and enhance continuity. This is achieved by constructing an optimization model constrained by second-order derivatives.

[0106]

[0107] in, This represents the final stable pointing deviation parameter. This is the smoothing coefficient, used to adjust the smoothing intensity. This represents the second derivative, used to constrain the curvature variation of the result, thereby avoiding abrupt changes and ensuring the continuity of the result.

[0108] Through the above continuous domain modeling and dynamic filtering process, the trend changes and random disturbances in the time series pointing deviation data can be effectively separated, so that the final stable pointing deviation parameter can not only reflect the true pointing deviation, but also suppress environmental interference, thereby significantly improving the overall detection accuracy and stability.

[0109] Step S1053: Construct an error discrimination relationship based on the spot morphology parameters, and perform error compensation processing on the stable pointing deviation parameters based on the error discrimination relationship to obtain the corrected pointing deviation parameters.

[0110] Specifically, the reliability of the current pointing deviation result and the source of error are determined by using the spot morphology parameters. By constructing an error discrimination relationship, the non-pointing error caused by the change of spot morphology is distinguished from the true pointing deviation. On this basis, the stable pointing deviation parameter is compensated and corrected according to the error discrimination relationship to eliminate the additional error caused by spot distortion, uneven energy distribution or propagation disturbance, thereby obtaining a corrected pointing deviation parameter that is closer to the true beam pointing.

[0111] In one possible implementation, step S1053 further includes: when the application scenario information is an optical fiber coupling scenario, the constructed error discrimination relationship includes a first discrimination relationship based on symmetry characteristics and a second discrimination relationship based on energy distribution stability; when the application scenario information is a free space optical path scenario, the constructed error discrimination relationship includes a third discrimination relationship based on morphological change characteristics and a fourth discrimination relationship based on directional change characteristics.

[0112] Specifically, when the application scenario is fiber-optic coupling, since the beam is typically output via a collimator, its spatial distribution tends to be axisymmetric. Therefore, the error mainly originates from slight mismatches or fluctuations in coupling efficiency, rather than significant propagation distortion. In this scenario, a first discriminant relationship based on symmetry characteristics is first constructed. The consistency of the beam's energy distribution in different radial directions is used to determine whether the beam maintains symmetry. This can be described by the following symmetry metric:

[0113]

[0114] in, Represents the light intensity distribution in polar coordinates. Indicator of symmetry deviation, when When the value approaches 0, it indicates that the light spot is approximately symmetrical. An increase indicates the presence of asymmetric distortion. This discriminant relation is used to determine whether the current deviation mainly originates from changes in the spot morphology.

[0115] A second discriminant relation based on energy distribution stability is further constructed to evaluate whether the energy of the light spot is concentrated and stable. This can be represented by an energy concentration function:

[0116]

[0117] in, Indicates the central area of ​​the light spot. This represents the entire light spot area. Indicates energy concentration. When Stable indicates that the shape of the light spot is stable. Significant changes indicate that the light spot has spread or distorted, which may introduce additional errors.

[0118] Based on the first and second discriminant relationships mentioned above, the stable pointing deviation parameter can be weighted and corrected. When the symmetry deviation or energy instability is large, the deviation value is suppressed or corrected, thereby obtaining the corrected pointing deviation parameter.

[0119] When the application scenario is a free-space optical path scenario, the light beam may undergo multiple levels of reflection, refraction, or partial obstruction during propagation, making the light spot more prone to directional stretching or morphological distortion, thus increasing the complexity of error sources. In this scenario, a third discriminant relation based on morphological change characteristics is first constructed, and the presence of propagation distortion is determined by analyzing the rate of change of the light spot morphology. This can be expressed by the following rate of change:

[0120]

[0121] in, and This represents the expansion parameters of the light spot in two directions. This indicates the rate of change in shape; a larger value indicates that the shape of the light spot is in a dynamic state of change.

[0122] Simultaneously, a fourth discriminant relation based on directional change characteristics is constructed to evaluate the deflection of the principal axis direction of the light spot, which can be expressed as:

[0123] in, Indicates the angle of the principal axis of the light spot. This represents the rate of change of direction, and this value is used to reflect whether there is a directional disturbance in the optical path.

[0124] Based on the third and fourth discriminant relations, unstable factors caused by optical path propagation can be identified, and the stable pointing deviation parameter can be dynamically compensated, such as by reducing the weight of high-frequency changing components or suppressing abnormal deviations, thereby obtaining the corrected pointing deviation parameter.

[0125] By constructing error discrimination relationships based on different application scenarios, the error compensation process can be adaptively adjusted for different physical mechanisms, thereby improving the system's adaptability to complex optical path environments while ensuring detection accuracy.

[0126] Step S1054: Construct a spatial propagation mapping model based on the corrected pointing deviation parameters, and use the spatial propagation mapping model to locate the target segment in the optical path that has the deviation.

[0127] Specifically, based on the propagation relationship between the corrected pointing deviation parameter and the optical path structure, a spatial propagation mapping model is constructed to describe the influence of different optical path segments on the pointing deviation. On this basis, the overall deviation is decomposed into each optical path segment through the spatial propagation mapping model, and the deviation contribution of each segment is quantitatively evaluated to determine the optical path segment with the greatest impact on the corrected pointing deviation parameter, thereby realizing the positioning of the target segment in the optical path that generates the deviation.

[0128] In one possible implementation, step S1054 further includes: constructing a spatial propagation correlation relationship corresponding to the optical path structure based on the corrected pointing deviation parameter; decomposing the corrected pointing deviation parameter into multiple segment deviation components based on the spatial propagation correlation relationship, and establishing a spatial mapping correspondence between the segment deviation components and the corresponding optical path segments; calculating the deviation contribution value of the corresponding optical path segment through the segment deviation components according to the spatial mapping correspondence; sorting each deviation contribution value, and selecting the optical path segments that meet the preset ranking conditions as target segments according to the sorting results.

[0129] Specifically, by jointly modeling the corrected pointing deviation parameter with the spatial structure information of the optical path, a hierarchical spatial propagation mapping model is constructed, thereby achieving an interpretable decomposition from the overall deviation to the segment deviation and completing the target segment positioning.

[0130] First, a spatial propagation correlation corresponding to the optical path structure is constructed based on the corrected pointing deviation parameter. The optical path is divided into multiple continuous segments according to its physical structure, with each segment corresponding to a local propagation unit, such as a collimator segment, a reflection segment, and a transmission segment. The corrected pointing deviation parameter is represented as a two-dimensional angle vector.

[0131]

[0132] in, and These represent the pointing deviations in two orthogonal directions. The overall deviation can be considered as the result of the superposition of propagation errors in each segment.

[0133] Based on this, the first layer of the spatial propagation mapping model, namely the "segment propagation layer," is constructed to describe the contribution relationship between the errors of each segment and the final deviation. The optical path is divided into... Each segment corresponds to a deviation component. The overall deviation can then be expressed as:

[0134]

[0135] in, Indicates the first Local deviation components of each optical path segment Indicates the first The propagation mapping matrix of each segment is used to describe the amplification, rotation, or projection effect of the error in that segment on the final deviation during propagation.

[0136] Furthermore, a second layer of the model is constructed, namely the "spatial mapping layer," which establishes the spatial mapping correspondence between segmental deviation components and specific optical path segments. This layer achieves spatial attribution of the deviation by binding each segmental deviation component to its actual position in the optical path. This mapping can be represented by an index relationship as follows:

[0137]

[0138] in, Indicates the first In each optical path segment, a one-to-one correspondence was established between the deviation components and the actual structure.

[0139] After constructing the mapping relationship, the corrected pointing deviation parameter is decomposed and solved. The deviation components of each segment can be estimated using the least squares optimization method.

[0140]

[0141] This optimization process is used to solve for the segmental deviation components that best fit the overall deviation, where each Characterizes the independent contribution of the corresponding segment to the deviation.

[0142] After obtaining the segmental deviation components, the deviation contribution value of each optical path segment is calculated according to the spatial mapping correspondence. The deviation contribution value can be measured by the amplitude of the deviation components.

[0143]

[0144] in, Indicates the first The deviation contribution value of each optical path segment is a factor, and the larger the value, the more significant the influence of that segment on the overall deviation.

[0145] Finally, the contribution values ​​of each deviation are sorted, and the optical path segments that meet the preset ranking criteria are selected as target segments based on the sorting results. For example, the segment with the largest contribution value can be selected. Each segment is used as a candidate source of deviation, thereby enabling the localization of key segments in the optical path that generate deviations.

[0146] Please refer to Figure 4 This illustration shows a schematic diagram of a beam pointing deviation detection and positioning device provided in an embodiment of this application. The device includes an acquisition module 41 and a processing module 42, wherein... The acquisition module 41 is used to acquire the main spot sampling data, the calibration spot sampling data, and the echo power data.

[0147] The acquisition module 41 is used to adjust the spatial attitude of the probe-type non-polarization beam splitter to the attitude lock state based on the echo power data; the probe-type non-polarization beam splitter is used to establish the spatial correspondence between the main spot sampling data and the calibration spot sampling data.

[0148] The acquisition module 41 is used to perform image preprocessing on the main spot sampling data and calibration spot sampling data based on spatial correspondence in the attitude-locked state to obtain standardized spot distribution data.

[0149] The processing module 42 is used to input the standardized spot distribution data into the improved two-dimensional elliptical Gaussian fitting model, and output the coordinates of the main spot center and the calibration spot center based on the improved two-dimensional elliptical Gaussian fitting model, while simultaneously acquiring the spot morphology parameters.

[0150] The processing module 42 is used to construct an error correction mechanism for pointing deviation based on the center coordinates of the main spot and the center coordinates of the calibration spot, and to locate the target segment in the optical path that has a deviation through the error correction mechanism.

[0151] In one possible implementation, the acquisition module 41 is used to acquire calibration spot sampling data, specifically including: acquiring application scenario information of the target optical path, the application scenario information including fiber-coupled scenario and free-space optical path scenario; in the fiber-coupled scenario, using a target fiber collimator to construct a reference light coaxial with the main optical path, and making the reference light form a first calibration spot on the detection plane; extracting the sampling data corresponding to the first calibration spot as the first calibration spot sampling data; in the free-space optical path scenario, using a preset reference collimator to output a reference light, and making the reference light propagate through space and enter a preset detection path to form a second calibration spot on the detection plane; extracting the sampling data corresponding to the second calibration spot as the second calibration spot sampling data; and using the first calibration spot sampling data or the second calibration spot sampling data as the calibration spot sampling data.

[0152] In one possible implementation, the acquisition module 41 is used to adjust the spatial attitude of the probe-type unpolarized beam splitter to an attitude-locked state based on the echo power data. Specifically, this includes: determining whether the echo power data meets a preset extreme value condition; if the echo power data does not meet the preset extreme value condition, introducing a micro-amplitude periodic perturbation and acquiring the echo power response change sequence; constructing local gradient information based on the echo power response change sequence, and determining the adjustment direction and step size of the angular attitude parameters based on the local gradient information; the angular attitude parameters include pitch angle and yaw angle; adaptively adjusting the probe-type unpolarized beam splitter based on the determined angular attitude parameters, and updating the echo power data after each adjustment; and establishing an attitude-locked state based on the current angular attitude parameters when the echo power data meets the preset extreme value condition.

[0153] In one possible implementation, the processing module 42 is used to construct an improved two-dimensional elliptic Gaussian fitting model, specifically including: determining the spot distribution constraint type based on application scenario information, the spot distribution constraint type including fiber coupling constraint type and free space propagation constraint type; under the fiber coupling constraint type, outputting symmetric distribution features based on the main spot sampling data and calibration spot sampling data, and constructing a first set of constraint parameters through the symmetric distribution features; the symmetric distribution features include energy radial distribution consistency features and principal axis direction stability features; under the free space propagation constraint type, outputting asymmetric extension features based on the main spot sampling data and calibration spot sampling data, and constructing a second set of constraint parameters through the asymmetric extension features; the asymmetric extension features include energy distribution skewness features and principal axis direction deflection features; and constructing an improved two-dimensional elliptic Gaussian fitting model based on the first set of constraint parameters or the second set of constraint parameters.

[0154] In one possible implementation, the processing module 42 is used to construct an error correction mechanism for pointing deviation based on the center coordinates of the main spot and the center coordinates of the calibration spot, and to locate the target segment in the optical path that has a deviation through the error correction mechanism. Specifically, this includes: calculating the spatial offset based on the center coordinates of the main spot and the center coordinates of the calibration spot, and performing angle mapping processing on the spatial offset in combination with the equivalent propagation distance to obtain the initial pointing deviation parameter; constructing time-series pointing deviation data based on the initial pointing deviation parameters of multiple consecutively acquired frames, and performing dynamic filtering processing on the time-series pointing deviation data to obtain a stable pointing deviation parameter; and based on the spot morphology parameters... An error discrimination relation is constructed, and error compensation processing is performed on the stable pointing deviation parameter based on the error discrimination relation to obtain the corrected pointing deviation parameter. When the application scenario is an optical fiber coupling scenario, the constructed error discrimination relation includes a first discrimination relation based on symmetry characteristics and a second discrimination relation based on energy distribution stability. When the application scenario is a free space optical path scenario, the constructed error discrimination relation includes a third discrimination relation based on morphological change characteristics and a fourth discrimination relation based on directional change characteristics. A spatial propagation mapping model is constructed based on the corrected pointing deviation parameter, and the target segment in the optical path that produces the deviation is located through the spatial propagation mapping model.

[0155] In one possible implementation, the processing module 42 is used to construct time-series pointing deviation data based on the initial pointing deviation parameters of multiple continuously acquired frames, and to perform dynamic filtering on the time-series pointing deviation data to obtain stable pointing deviation parameters. Specifically, this includes: organizing the initial pointing deviation parameters of multiple continuously acquired frames in chronological order to construct time-series pointing deviation data; performing trend term and random disturbance component decomposition processing on the time-series pointing deviation data to obtain trend deviation components and disturbance deviation components; adjusting the weight of the disturbance deviation components based on the trend deviation components, and calculating the predicted pointing deviation value at the current time based on the weighted disturbance deviation components; fusing the predicted pointing deviation value with the actual pointing deviation value at the current time to obtain a fused deviation parameter; performing adaptive smoothing processing on the fused deviation parameter, and using the processed fused deviation parameter as the stable pointing deviation parameter.

[0156] In one possible implementation, the processing module 42 is used to construct a spatial propagation mapping model based on the corrected pointing deviation parameter, and to locate the target segment in the optical path that generates the deviation through the spatial propagation mapping model. Specifically, this includes: constructing a spatial propagation correlation relationship corresponding to the optical path structure based on the corrected pointing deviation parameter; decomposing the corrected pointing deviation parameter into multiple segment deviation components based on the spatial propagation correlation relationship, and establishing a spatial mapping correspondence between the segment deviation components and the corresponding optical path segments; calculating the deviation contribution value of the corresponding optical path segment through the segment deviation components according to the spatial mapping correspondence; sorting the various deviation contribution values, and selecting the optical path segments that meet the preset ranking conditions as target segments according to the sorting results.

[0157] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0158] This application also provides an electronic device. (See reference...) Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 501, at least one communication bus 502, a user interface 503, at least one network interface 504, and a memory 505.

[0159] The communication bus 502 is used to enable communication between these components.

[0160] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0161] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0162] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 501 and may be implemented as a separate chip.

[0163] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 5The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application for detecting and locating beam pointing deviations.

[0164] exist Figure 5 In the illustrated electronic device, the user interface 503 is primarily used to provide an input interface for the user and acquire user input data; while the processor 501 can be used to call the application program for detecting and locating beam pointing deviation stored in the memory 505. When executed by one or more processors 501, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0165] This application also provides a non-transitory computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0166] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0167] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0168] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0170] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0171] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0172] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for detecting and locating beam pointing deviation, characterized in that, The method includes: Acquire main spot sampling data, calibration spot sampling data, and echo power data; The spatial attitude of the probe-type non-polarization beam splitter is adjusted to an attitude-locked state based on the echo power data; the probe-type non-polarization beam splitter is used to establish the spatial correspondence between the main spot sampling data and the calibration spot sampling data; In the attitude lock state, image preprocessing is performed on the main spot sampling data and calibration spot sampling data based on the spatial correspondence to obtain standardized spot distribution data; The standardized spot distribution data is input into the improved two-dimensional elliptical Gaussian fitting model, and the coordinates of the main spot center and the coordinates of the calibration spot center are output based on the improved two-dimensional elliptical Gaussian fitting model, while the spot morphology parameters are obtained simultaneously. An error correction mechanism for pointing deviation is constructed based on the coordinates of the main spot center and the coordinates of the calibration spot center, and the target segment in the optical path that has deviation is located through the error correction mechanism.

2. The method according to claim 1, characterized in that, Acquire calibration spot sampling data, specifically including: Obtain application scenario information of the target optical path, including fiber optic coupling scenario and free space optical path scenario; In the fiber coupling scenario, a reference light coaxial with the main optical path is constructed using a target fiber collimator, and the reference light forms a first calibration spot on the detection plane. Extract the sampling data corresponding to the first calibration spot as the first calibration spot sampling data; In the free space optical path scenario, a reference light is output using a preset reference collimator, and the reference light is propagated through space and enters a preset detection path to form a second calibration spot on the detection plane. Extract the sampling data corresponding to the second calibration spot as the second calibration spot sampling data; The first calibration spot sampling data or the second calibration spot sampling data is used as the calibration spot sampling data.

3. The method according to claim 1, characterized in that, The adjustment of the spatial attitude of the probe-type non-polarization beam splitter to an attitude-locked state based on the echo power data specifically includes: Determine whether the echo power data meets the preset extreme value condition; If the echo power data does not meet the preset extreme value condition, a small-amplitude periodic perturbation is introduced and the echo power response change sequence is obtained; Local gradient information is constructed based on the echo power response change sequence, and the adjustment direction and step size of the angular attitude parameters are determined according to the local gradient information; the angular attitude parameters include pitch angle and yaw angle. The probe-type non-polarization beam splitter is adaptively adjusted based on the determined angular attitude parameters, and the echo power data is updated after each adjustment. When the echo power data meets the preset extreme value condition, the attitude lock state is established based on the current angular attitude parameters.

4. The method according to claim 2, characterized in that, Constructing an improved two-dimensional elliptic Gaussian fitting model, specifically including: The type of light spot distribution constraint is determined based on the application scenario information. The type of light spot distribution constraint includes fiber coupling constraint type and free space propagation constraint type. Under the fiber coupling constraint type, a symmetrical distribution feature is output based on the main spot sampling data and the calibration spot sampling data, and a first constraint parameter set is constructed through the symmetrical distribution feature; the symmetrical distribution feature includes energy radial distribution consistency feature and principal axis direction stability feature. Under the free-space propagation constraint type, an asymmetric propagation feature is output based on the main spot sampling data and the calibration spot sampling data, and a second constraint parameter set is constructed through the asymmetric propagation feature; the asymmetric propagation feature includes energy distribution skewness feature and principal axis direction deflection feature. The improved two-dimensional elliptic Gaussian fitting model is constructed based on the first set of constraint parameters or the second set of constraint parameters.

5. The method according to claim 2, characterized in that, The step of constructing an error correction mechanism for pointing deviation based on the center coordinates of the main spot and the center coordinates of the calibration spot, and locating the target segment in the optical path that has deviation through the error correction mechanism, specifically includes: The spatial offset is calculated based on the coordinates of the main spot center and the coordinates of the calibration spot center, and the spatial offset is then subjected to angle mapping processing in combination with the equivalent propagation distance to obtain the initial pointing deviation parameter. Based on the initial pointing deviation parameters collected from multiple consecutive frames, a time-series pointing deviation data is constructed, and dynamic filtering is performed on the time-series pointing deviation data to obtain stable pointing deviation parameters. An error discrimination relationship is constructed based on the light spot morphology parameters, and error compensation processing is performed on the stable pointing deviation parameter based on the error discrimination relationship to obtain the corrected pointing deviation parameter; when the application scenario information is the optical fiber coupling scenario, the constructed error discrimination relationship includes a first discrimination relationship based on symmetry characteristics and a second discrimination relationship based on energy distribution stability; when the application scenario information is the free space optical path scenario, the constructed error discrimination relationship includes a third discrimination relationship based on morphological change characteristics and a fourth discrimination relationship based on directional change characteristics. A spatial propagation mapping model is constructed based on the corrected pointing deviation parameter, and the target segment in the optical path that has a deviation is located using the spatial propagation mapping model.

6. The method according to claim 5, characterized in that, The step of constructing time-series pointing deviation data based on the initial pointing deviation parameters collected from multiple consecutive frames, and performing dynamic filtering on the time-series pointing deviation data to obtain stable pointing deviation parameters, specifically includes: The initial pointing deviation parameters of multiple consecutively acquired frames are organized in chronological order to construct the time-series pointing deviation data; The time series pointing deviation data is decomposed into trend term and random disturbance component to obtain trend deviation component and disturbance deviation component; The perturbation deviation component is weighted based on the trend deviation component, and the prediction pointing deviation value at the current moment is calculated based on the weighted perturbation deviation component. The predicted pointing deviation value is fused with the actual pointing deviation value at the current moment to obtain the fused deviation parameter; The fusion deviation parameter is adaptively smoothed, and the processed fusion deviation parameter is used as the stable pointing deviation parameter.

7. The method according to claim 5, characterized in that, The step of constructing a spatial propagation mapping model based on the corrected pointing deviation parameter, and locating the target segment in the optical path that has deviation through the spatial propagation mapping model, specifically includes: Based on the corrected pointing deviation parameter, a spatial propagation correlation relationship corresponding to the optical path structure is constructed; Based on the spatial propagation correlation, the corrected pointing deviation parameter is decomposed into multiple segment deviation components, and a spatial mapping correspondence between the segment deviation components and the corresponding optical path segments is established. According to the spatial mapping correspondence, the deviation contribution value of the corresponding optical path segment is calculated through the segment deviation component; The deviation contribution values ​​are sorted, and the optical path segments that meet the preset ranking conditions are selected as the target segments according to the sorting results.

8. A device for detecting and locating beam pointing deviation, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to acquire main spot sampling data, calibration spot sampling data, and echo power data; The acquisition module is used to adjust the spatial attitude of the probe-type non-polarization beam splitter to an attitude-locked state based on the echo power data; the probe-type non-polarization beam splitter is used to establish a spatial correspondence between the main spot sampling data and the calibration spot sampling data; The acquisition module is used to perform image preprocessing on the main spot sampling data and calibration spot sampling data based on the spatial correspondence in the attitude lock state to obtain standardized spot distribution data. The processing module is used to input the standardized spot distribution data into the improved two-dimensional elliptical Gaussian fitting model, and output the main spot center coordinates and calibration spot center coordinates based on the improved two-dimensional elliptical Gaussian fitting model, and simultaneously acquire spot morphology parameters. The processing module is used to construct an error correction mechanism for pointing deviation based on the center coordinates of the main spot and the center coordinates of the calibration spot, and to locate the target segment in the optical path that has a deviation through the error correction mechanism.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to enable communication between the components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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