Star chain signal configuration multi-dimensional verification method and system

Abstract

The invention discloses a star chain signal configuration multi-dimensional verification method and a star chain signal configuration multi-dimensional verification system, relates to the technical field of communication signal processing, and the star chain signal configuration multi-dimensional verification method and the star chain signal configuration multi-dimensional verification system disclosed by the invention can realize multi-dimensional verification of star chain signals through technical means of adaptive threshold processing, polygon approximation, multi-dimensional template matching, distribution rule verification and the like. The geometric characteristics and the spatial distribution rule of the signal configuration are comprehensively evaluated, the accuracy and the robustness of star chain signal detection can be improved, signal configuration abnormity caused by frequency offset, noise and environmental interference can be effectively handled, and manual intervention requirements are reduced.

Description

Technical Field

[0001] This application relates to the field of communication signal processing technology, and in particular to a method and system for multi-dimensional verification of Starlink signal configuration. Background Technology

[0002] In satellite communication systems, Starlink signal transmission often suffers from significant anomalies in signal configuration due to factors such as frequency offset, noise, and environmental interference. These anomalies manifest as edge spikes, long-side height jitter, and missing local areas. Such interference severely damages the integrity and consistency of the signal configuration, posing a major challenge to the accuracy of signal detection. Traditional detection methods mainly employ manual visual comparison or basic image processing algorithms, but these have obvious limitations. Manual visual comparison relies heavily on the operator's professional experience and is prone to visual fatigue during continuous operation, resulting in highly subjective and inconsistent detection results, making it difficult to guarantee long-term reliability. Basic image processing algorithms, such as fixed threshold segmentation and single-scale template matching, lack dynamic adaptability and are particularly inadequate in complex interference scenarios: fixed threshold segmentation cannot handle grayscale distribution fluctuations caused by edge spikes, easily leading to over-segmentation or omission of key features; single-scale template matching struggles to accurately locate signal features under conditions of long-side height jitter, often misclassifying valid signals as noise interference or ignoring locally missing areas. More importantly, existing technologies have not established a collaborative analysis framework for multi-dimensional features such as contour accuracy, regional distribution, and symmetry. This makes it impossible to comprehensively evaluate the geometric characteristics and spatial distribution patterns of signal configurations, resulting in systemic defects in the robustness and accuracy of the verification process. Consequently, it is difficult to meet the needs of modern communication systems for high-precision and automated signal detection.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a multi-dimensional verification method and system for Starlink signal configuration, which aims to improve the accuracy and robustness of Starlink signal detection.

[0005] To achieve the above objectives, this application proposes a multi-dimensional verification method for Starlink signal configurations. The method includes: acquiring matching configuration graphic data through an FPGA programmable logic domain; performing adaptive threshold binarization on the matching configuration graphic data to obtain binary image data; performing Canny edge detection on the binary image data to obtain contour data; performing polygon approximation on the contour data to obtain simplified contour data; performing template matching on the simplified contour data and preset Starlink signal standard configuration template data to obtain rectangular bounding box data; calculating the total pixel data of the rectangular region based on the rectangular bounding box data; and calculating the length and slope data of each side of the rectangle to identify... Separate the long side data; define the lower edge of the rectangular bounding box as the bottom edge; calculate the height value of each pixel in the long side data relative to the bottom edge; perform adaptive threshold segmentation on the height value data to obtain low point region data and high point region data; based on the low point region data and the high point region data, calculate the area ratio of the total number of pixels in the low point region to the total number of pixels in the rectangular region; if the area ratio data exceeds a preset threshold, perform distribution rule verification processing on the low point region data and the high point region data to obtain distribution rule verification result data; based on the distribution rule verification result data, determine whether the Starlink signal exists to obtain Starlink signal existence determination data. In one embodiment, the steps of performing adaptive threshold binarization on the matched configuration graphic data to obtain binary image data; performing Canny edge detection on the binary image data to obtain contour data; and performing polygon approximation on the contour data to obtain simplified contour data include: performing adaptive threshold binarization on the matched configuration graphic data to obtain binary image data; performing Canny edge detection on the binary image data by smoothing the image with Gaussian filtering, calculating gradient magnitude and direction, applying non-maximum suppression to refine edges, and performing dual threshold filtering to obtain contour data; and performing polygon approximation on the contour data by setting an approximation error threshold to simplify the contour and obtain simplified contour data with four-sided features. In one embodiment, the steps of performing template matching on the simplified contour data and preset Starlink signal standard configuration template data to obtain rectangular bounding box data include: performing normalized cross-correlation matching on the simplified contour data and preset Starlink signal standard configuration template data to calculate pixel grayscale similarity and obtain similarity data; and selecting the region with the highest similarity based on the similarity data and outputting the rectangular bounding box data. In one embodiment, the method further includes: scaling the preset Starlink signal standard configuration template data to generate a multi-scale template dataset within a preset range; matching the multi-scale template dataset with the simplified contour data, selecting the best matching scale, and obtaining rectangular bounding box data.In one embodiment, the method further includes: rotating the preset starlink signal standard configuration template data to generate a multi-angle template dataset within a preset angle range; matching the multi-angle template dataset with the simplified contour data and selecting the best matching angle to obtain rectangular bounding box data. In one embodiment, the step of calculating the length and slope data of each side of the rectangle and identifying the long side data includes: calculating the length and slope data of each side based on the rectangular bounding box data; identifying the two sides with the longest length and a slope difference less than a preset threshold as the long side data. In one embodiment, the step of performing adaptive threshold segmentation processing on the height value data to obtain low-point region data and high-point region data includes: determining a segmentation threshold based on the height value data by analyzing the grayscale distribution of the height value; classifying pixels with height values ​​lower than the segmentation threshold as low-point region data and pixels with height values ​​higher than the segmentation threshold as high-point region data to obtain low-point region data and high-point region data. In one embodiment, the step of performing distribution rule verification processing on the low-point region data and the high-point region data to obtain distribution rule verification result data includes: based on the low-point region data and the high-point region data, checking whether the low-point region is continuously distributed along the long side and whether the length of a single continuous segment does not exceed a preset proportion of the total length of the long side; checking whether the positional deviation of the low-point region data on the two long sides relative to the central axis of the rectangle is less than a preset pixel threshold; when the conditions of continuous distribution of the low-point region along the long side, continuous length of a single segment not exceeding a preset proportion of the total length of the long side, and positional deviation of the low-point region data on the two long sides relative to the central axis of the rectangle being less than a preset pixel threshold are simultaneously met, the distribution rule verification result data of the valid starlink signal configuration is output; otherwise, the distribution rule verification result data of the invalid starlink signal configuration is output. In one embodiment, the step of checking whether the positional deviation of the low-point region data of the two long sides relative to the central axis of the rectangle is less than a preset pixel threshold further includes: based on the Starlink signal feature template, checking whether the low-point region data of the two long sides satisfies the symmetrical distribution feature on both sides of the central axis of the rectangle; if the symmetrical distribution feature is satisfied, then the distribution rule verification result data that satisfies the positional symmetry condition is output; otherwise, the distribution rule verification result data that does not satisfy the positional symmetry condition is output. Furthermore, to achieve the above objective, this application also proposes a Starlink signal configuration multi-dimensional verification system, which includes: a memory, a processor, and a Starlink signal configuration multi-dimensional verification program stored in the memory and executable on the processor. The Starlink signal configuration multi-dimensional verification program is configured to implement the steps of the Starlink signal configuration multi-dimensional verification method.

[0006] The Starlink signal configuration multi-dimensional verification method and system proposed in this application comprehensively evaluate the geometric characteristics and spatial distribution patterns of the signal configuration through adaptive threshold processing, polygon approximation, multi-dimensional template matching and distribution rule verification. This can improve the accuracy and robustness of Starlink signal detection and effectively cope with signal configuration anomalies caused by frequency offset, noise and environmental interference, reducing the need for manual intervention. Attached Figure Description

[0007] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating an embodiment of the Starlink signal configuration multi-dimensional verification method of this application; Figure 2 This is a structural schematic diagram of an embodiment of the Starlink signal configuration multi-dimensional verification system of this application.

[0010] Explanation of icon numbers: 10. Memory; 20. Processor.

[0011] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0012] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] In existing technologies, traditional detection schemes mainly employ manual visual comparison or basic image processing algorithms, but these methods have significant limitations. Manual visual comparison heavily relies on the operator's accumulated professional experience and is prone to visual fatigue during continuous operation, resulting in highly subjective and inconsistent detection results, making it difficult to guarantee long-term reliability. Basic image processing algorithms, such as fixed threshold segmentation and single-scale template matching, lack dynamic adaptability and are particularly inadequate in complex interference scenarios: fixed threshold segmentation cannot handle grayscale distribution fluctuations caused by edge spikes, easily leading to over-segmentation or omission of key features; single-scale template matching struggles to accurately locate signal features under conditions of long-side height jitter, often misclassifying valid signals as noise interference or ignoring locally missing regions. More critically, existing technologies lack a collaborative analysis framework for multi-dimensional features such as contour accuracy, regional distribution, and symmetry, failing to comprehensively evaluate the geometric characteristics and spatial distribution patterns of signal configurations. This results in systemic deficiencies in the robustness and accuracy of the verification process, making it difficult to meet the demands of modern communication systems for high-precision, automated signal detection.

[0015] Based on this, embodiments of this application provide a multi-dimensional verification method for Starlink signal configuration, referring to... Figure 1 The Starlink signal configuration multi-dimensional verification method includes steps S100 to S400, wherein: Step S100: Obtain matching configuration graphic data through the FPGA programmable logic domain; perform adaptive threshold binarization processing on the matching configuration graphic data to obtain binary image data; perform Canny edge detection processing on the binary image data to obtain contour data; perform polygon approximation processing on the contour data to obtain simplified contour data; Step S200: Perform template matching processing on the simplified contour data and preset starlink signal standard configuration template data to obtain rectangular bounding box data; Step S300: Calculate the total pixel data of the rectangular region based on the rectangular bounding box data; calculate the length and slope data of each side of the rectangle, identify the long side data; define the rectangle. The lower edge of the bounding box is the bottom edge; the height value of each pixel in the long side data relative to the bottom edge is calculated; the height value data is subjected to adaptive threshold segmentation processing to obtain low point region data and high point region data; in step S400, based on the low point region data and the high point region data, the area ratio of the total number of pixels in the low point region to the total number of pixels in the rectangular region is calculated; if the area ratio data exceeds a preset threshold, the low point region data and the high point region data are subjected to distribution rule verification processing to obtain distribution rule verification result data; based on the distribution rule verification result data, it is determined whether the starlink signal exists to obtain starlink signal existence determination data. In this embodiment, the FPGA programmable logic domain can be understood as a hardware acceleration platform that can achieve high-speed data processing through parallel computing capabilities. For example, specific data acquisition tasks can be completed by configuring the internal logic circuit of the FPGA, or intermediate calculation results can be cached using its built-in memory module, thereby improving data processing efficiency. In practical applications, the acquisition of matching configuration graphic data can also be achieved through other hardware devices, such as application-specific integrated circuits (ASICs) or high-performance graphics processors (GPUs), which also have the ability to quickly process complex data. Adaptive threshold binarization is a method that dynamically adjusts the segmentation threshold based on local image characteristics. Specifically, the segmentation threshold for each pixel can be determined by analyzing the brightness distribution characteristics of local image regions and using the mean, median, or Gaussian weighted method. Furthermore, morphological operations can be introduced to post-process the binarization results to eliminate isolated noise points or fill small holes, thereby improving the quality of the binary image. In this embodiment, the core of Canny edge detection processing lies in extracting edge features from the image. In practical applications, in addition to using Gaussian filtering to smooth the image, bilateral filtering or guided filtering techniques can be used to suppress noise interference while preserving edge information. The calculation of gradient magnitude and direction can be achieved using the Sobel operator, Prewitt operator, or other difference operators, and non-maximum suppression and double threshold filtering mechanisms can also enhance edge detection performance by adjusting parameters or optimizing the algorithm structure. Polygon approximation processing is a technique used to simplify contour shapes.Specifically, the degree of contour simplification can be controlled by setting different error thresholds. For example, the Douglas-Peucker algorithm or the Ramer-Douglas-Peucker algorithm can be used to segment and fit the contour, thereby preserving key geometric features. Furthermore, Fourier descriptors or principal component analysis methods can be combined to reduce the dimensionality of the contour, further highlighting the main shape information. In this embodiment, the core of template matching processing lies in locating the target region through a similarity measurement method. In practical applications, in addition to normalized cross-correlation matching, squared difference matching, correlation coefficient matching, or feature point-based matching methods, such as SIFT, SURF, or ORB algorithms, can also be used to improve matching accuracy and robustness. Simultaneously, template data generation can be achieved through manual annotation, automatic learning, or a hybrid approach to adapt to different scenario requirements. This application constructs a multi-dimensional adaptive verification mechanism to achieve precise processing from data acquisition to final judgment, addressing the complex configuration changes of Starlink signals after interference. Compared to traditional methods that rely on manual visual comparison or simple image processing algorithms, this method effectively overcomes problems such as edge spikes, long-side height jitter, and local region loss by introducing adaptive threshold segmentation, polygon approximation, template matching, and distribution rule verification, significantly improving the accuracy and robustness of Starlink signal verification. In this embodiment, matching configuration graphic data is acquired through the FPGA programmable logic domain, and the signal configuration is captured in real time using hardware acceleration capabilities, ensuring high efficiency and real-time performance of data processing. Furthermore, the matching configuration graphic data undergoes adaptive threshold binarization processing. Since interference often causes uneven image brightness, the adaptive threshold dynamically adjusts the segmentation points according to local pixel characteristics, avoiding the failure of fixed thresholds in noisy environments, thereby obtaining more accurate binary image data. Canny edge detection processing is performed on the binary image data. Based on the simplified binary image data, Gaussian filtering is used to smooth noise, gradient calculation is used to enhance edge features, and non-maximum suppression and dual threshold filtering mechanisms are combined to effectively suppress edge spike interference and extract clear contours. Specifically, the contour data undergoes polygon approximation processing. An error threshold is set based on the contour's geometric characteristics to simplify the shape, retaining core features such as the four sides, eliminating the influence of small-scale spurs, and highlighting the essential contour of the signal configuration. In this embodiment, the simplified contour data is matched with a preset Starlink signal standard configuration template. The similarity between the simplified contour and the standard template is calculated to quickly locate the signal region, accurately acquiring rectangular bounding box data even with local missing parts. Based on the rectangular bounding box data, the total pixel data of the rectangular region is calculated to quantify the signal region size, providing a basis for subsequent interference level assessment. Furthermore, the length and slope data of each side of the rectangle are calculated, and the long side data is identified. For typical Starlink signal configuration features, the long side is identified through length-slope correlation analysis, serving as a key reference for high-level jitter analysis.The bottom edge of the rectangular bounding box is defined as the base edge, establishing a unified height benchmark to ensure consistency in subsequent calculations. In this embodiment, the height value of each pixel in the long side data relative to the base edge is calculated, directly quantifying the height jitter phenomenon of the long side and extracting key parameters to address the fluctuation problem caused by interference. The height value data undergoes adaptive threshold segmentation processing, dynamically determining the segmentation point based on the height distribution characteristics to distinguish between low and high point regions, effectively addressing the challenge of anomaly point identification caused by height jitter. Based on the low and high point region data, the area ratio data is calculated to evaluate the interference coverage ratio. Distribution rule verification is triggered only when the ratio exceeds a preset threshold to avoid invalid calculations. Furthermore, the low and high point region data undergo distribution rule verification processing to check whether the low point region is continuously distributed along the long side, whether the single segment length is limited, and whether the positional deviation is small, ensuring that it conforms to the distribution law of the starlink signal configuration. In this embodiment, this scheme comprehensively verifies the signal authenticity from the dimensions of contour accuracy, regional distribution, and symmetry. The existence of the signal is determined based on the distribution rule verification results. Through a multi-dimensional feature joint analysis mechanism, the accuracy and robustness of verification are significantly improved, reducing the risk of false detection and false negative detection. As a preferred implementation, this technical solution constructs a multi-dimensional adaptive verification mechanism to achieve precise processing of the entire process from data acquisition to final judgment, effectively overcoming the limitations of traditional methods in scenarios with edge spikes, high jitter, and missing regions.

[0016] In one feasible implementation, the steps of performing adaptive threshold binarization on the matched configuration graphic data to obtain binary image data; performing Canny edge detection on the binary image data to obtain contour data; and performing polygon approximation on the contour data to obtain simplified contour data include: performing adaptive threshold binarization on the matched configuration graphic data to obtain binary image data; performing Canny edge detection on the binary image data by smoothing the image with Gaussian filtering, calculating gradient magnitude and direction, applying non-maximum suppression to refine edges, and performing dual threshold filtering to obtain contour data; and performing polygon approximation on the contour data by setting an approximation error threshold to simplify the contour and obtain simplified contour data with four-sided features.

[0017] In this embodiment, adaptive threshold binarization refers to a technique that dynamically adjusts the segmentation threshold based on the local brightness distribution of the image. This can be implemented using algorithms based on local mean or local variance, aiming to avoid segmentation deviations caused by fixed thresholds in noisy environments. Gaussian filtering smoothing refers to a technique that uses a Gaussian kernel function to convolve the image to suppress noise. This can be achieved by adjusting the Gaussian kernel size and standard deviation, aiming to effectively remove spurious interference and prevent edge breakage. Gradient magnitude and direction calculation is a process of extracting edge information based on the pixel grayscale variation law of the smoothed image. This can be implemented using the Sobel operator or other gradient operators, aiming to accurately capture edge direction. Non-maximum suppression refers to a technique that filters local maxima along the gradient direction to eliminate redundant details. This can be achieved through local window comparison, aiming to ensure single-pixel width of edges and improve contour clarity. Dual threshold filtering is a technique that sets high and low thresholds based on the gradient magnitude distribution to distinguish between strong and weak edges and connect weak edges to strong edges. This can be implemented using an iterative threshold selection algorithm, aiming to enhance contour continuity and maintain edge integrity. In polygon approximation processing, the approximation error threshold is a parameter that controls the degree of contour simplification. It can be dynamically adjusted based on the geometric complexity of the contour to avoid excessive edge counts caused by glitch and ensure that the simplified contour retains four-sided features. In this embodiment, the above scheme achieves accuracy and reliability in contour extraction when the Starlink signal is interfered with through the organic combination of multiple steps. First, adaptive threshold binarization lays a clean binary image foundation for subsequent edge detection, exhibiting strong robustness, especially in noisy environments. Based on this, the four key steps of Canny edge detection are executed sequentially: Gaussian filtering effectively suppresses glitch interference, gradient calculation accurately captures edge direction, non-maximum suppression eliminates redundant details, and dual threshold filtering enhances contour continuity. These steps work together to ensure the clarity and integrity of the contour data. Subsequently, polygon approximation processing dynamically controls the degree of simplification by setting the approximation error threshold, thereby generating simplified contour data that conforms to the standard configuration of the Starlink signal. This process not only solves the problem of redundant details in the contour data but also ensures the standardization of the simplified contour, providing reliable input for subsequent template matching. Furthermore, the above method is closely related to the acquisition of matching configuration graphic data and subsequent template matching processing mentioned in the pre-information, which further improves the overall accuracy and anti-interference capability of Starlink signal verification.

[0018] In one feasible implementation, the step of performing template matching processing between the simplified contour data and the preset Starlink signal standard configuration template data to obtain rectangular bounding box data includes: performing normalized cross-correlation matching processing between the simplified contour data and the preset Starlink signal standard configuration template data, calculating pixel grayscale similarity to obtain similarity data; and based on the similarity data, selecting the region with the highest similarity and outputting the rectangular bounding box data.

[0019] In this embodiment, normalized cross-correlation matching processing refers to a matching method that eliminates the influence of image brightness and contrast fluctuations by normalizing pixel grayscale values. It can be implemented using a normalization algorithm based on the mean and standard deviation or a normalization algorithm based on the statistical characteristics of local windows, aiming to improve the reliability of template matching in interference environments. Pixel grayscale similarity is a quantitative indicator measuring the degree of matching between simplified contour data and template data. It can be achieved by calculating the difference in normalized grayscale values ​​or the correlation coefficient, aiming to compensate for the impact of signal intensity changes on the matching results. Similarity data refers to the set of values ​​used to evaluate the quality of matching regions. It can be implemented using histogram distribution analysis or a weighted scoring mechanism, aiming to provide a highly reliable basis for subsequent localization. In this embodiment, this technical solution significantly improves the reliability of template matching in interference environments through normalized cross-correlation matching processing. First, simplified contour data is matched with a pre-defined Starlink signal standard configuration template using normalized cross-correlation. This step eliminates the influence of image brightness and contrast fluctuations by normalizing pixel grayscale values, ensuring stable similarity calculation even when Starlink signals have noise or edge spikes. This avoids similarity distortion caused by local interference in simple correlation methods, thus laying an accurate foundation for the matching process. Next, pixel grayscale similarity is calculated to obtain similarity data. The degree of matching between the contour and the template is quantified based on the similarity measure of normalized cross-correlation. A key feature of this method is that normalization compensates for signal intensity variations, making similarity assessment more robust under frequency offset or noise interference, effectively preventing mismatches caused by grayscale deviations. Finally, the region with the highest similarity is selected based on the similarity data to output rectangular bounding box data. This step uses the highly reliable similarity data generated by normalized cross-correlation to accurately locate the matching position. Even when the signal configuration is missing or deformed, the rectangular bounding box accurately reflects the actual signal position, providing crucial input for subsequent verification and solving the problem of inaccurate positioning in complex interference by traditional single matching methods. In this embodiment, the above technical solution is combined with the aforementioned steps for obtaining simplified contour data. Simplified contour data with four-sided features is obtained through polygon approximation processing, further improving the efficiency and accuracy of normalized cross-correlation matching. This combined approach effectively addresses issues such as edge spikes and long-side height jitter in Starlink signal interference scenarios, thereby achieving comprehensive dimensional verification from contour accuracy to regional distribution, significantly improving the accuracy and robustness of the detection results.

[0020] In one feasible implementation, the method further includes: scaling the preset Starlink signal standard configuration template data to generate a multi-scale template dataset within a preset range; matching the multi-scale template dataset with the simplified contour data and selecting the best matching scale to obtain rectangular bounding box data.

[0021] In this embodiment, the multi-scale template dataset refers to a set of template data generated by scaling Starlink signal standard configuration template data at different ratios. In practical applications, the scale scaling of the template data can be achieved through interpolation algorithms (such as bilinear interpolation or bicubic interpolation). The purpose is to dynamically expand the scale coverage of the template, thereby adapting to the scale changes of Starlink signal configuration caused by frequency offset and noise interference during transmission. The preset range can be understood as a reasonable interval set based on the limited scale changes in the actual interference scenario of Starlink signal. Its function is to avoid computational redundancy caused by blind search, while ensuring coverage of possible scaling fluctuations in the signal configuration. In this embodiment, the scheme solves the scale adaptability problem of Starlink signal configuration caused by interference by introducing a multi-scale template matching mechanism. First, based on the preset Starlink signal standard configuration template data, a multi-scale template dataset is generated using an interpolation algorithm. This process fully considers the scale change characteristics that Starlink signal may experience in actual transmission and improves computational efficiency by limiting the preset range. Subsequently, the generated multi-scale template dataset is matched with simplified contour data to select the best matching scale. In this process, the simplified contour data retains four-sided features after polygon approximation, exhibiting high geometric stability and effectively improving matching accuracy. By traversing multi-scale template sets and identifying the matching results with the highest similarity, the failure defect of single-scale templates when the configuration size changes is overcome, ultimately accurately obtaining rectangular bounding box data. Furthermore, this scheme, combined with the aforementioned simplified contour data processing steps, further enhances the robustness of the overall technical solution, especially demonstrating significant advantages in scenarios with edge burrs or missing local areas, laying a reliable foundation for subsequent height value analysis and distribution rule verification.

[0022] In one feasible implementation, the method further includes: rotating the preset Starlink signal standard configuration template data to generate a multi-angle template dataset within a preset angle range; matching the multi-angle template dataset with the simplified contour data, selecting the best matching angle, and obtaining rectangular bounding box data.

[0023] In this embodiment, the preset angle range refers to the angle interval set according to the amplitude of rotational interference that may occur during the transmission of the Starlink signal. It can be determined by empirical values ​​or experimental calibration. The multi-angle template dataset refers to a set of templates covering different angles generated by performing multiple rotation operations on the standard configuration template data of the Starlink signal. Its purpose is to provide multiple candidate templates to adapt to the actual orientation changes of the signal. The optimal matching angle refers to the template angle that best matches the simplified contour data after similarity calculation. It can be achieved through normalized cross-correlation or other similarity evaluation methods. In this embodiment, the scheme effectively addresses the matching challenge caused by signal configuration rotation by dynamically adjusting the template angle, ensuring the accuracy of bounding box acquisition under interference environment. First, the preset standard configuration template data of the Starlink signal is rotated and a multi-angle template dataset is generated within the preset angle range. This provides template candidates covering multiple angles to address the possible rotational interference of the signal, enabling the templates to dynamically adapt to the actual orientation changes of the signal. Subsequently, the multi-angle template dataset is matched with the simplified contour data, and the optimal matching angle is selected. The signal rotation state is dynamically determined based on the similarity calculation of the actual data, thereby ensuring that the acquired rectangular bounding box accurately corresponds to the signal configuration. This process avoids positioning errors in fixed-angle matching under rotational conditions, significantly improving the robustness and accuracy of the verification process. Furthermore, combining this scheme with other steps in the aforementioned Starlink signal configuration multi-dimensional verification method further enhances the reliability of the overall detection results, especially demonstrating excellent adaptability in complex scenarios such as edge burrs and long-side height jitter.

[0024] In one feasible implementation, the steps of calculating the length and slope data of each side of the rectangle and identifying the long side data include: calculating the length and slope data of each side based on the rectangle bounding box data; and determining the two sides with the longest length and slope difference less than a preset threshold as the long side data.

[0025] In this embodiment, slope data refers to data captured by quantizing the directional features of the edge lines. This can be achieved by calculating the slope after fitting a straight line to each edge, aiming to avoid length distortion caused by local jitter when relying solely on length measurement. The slope difference refers to the absolute difference between the slopes of two edges. A reasonable tolerance range can be set to determine whether two edges are approximately parallel, aiming to eliminate the risk of misjudgment due to abrupt slope changes, while also accommodating reasonable tolerances under slight interference. In this embodiment, the scheme first calculates the length and slope data of each edge based on the rectangular bounding box data. This process is particularly important in interference-affected signals because Starlink signal configurations often produce edge spikes or high jitter due to noise, resulting in irregular bounding box edges. By introducing slope data, the directional features of the edge lines can be effectively quantified, thus providing a more stable geometric basis for subsequent identification. Based on this, the two longest edges with a slope difference less than a preset threshold are identified as the long edges. The core of this mechanism lies in integrating the dual criteria of length and slope consistency. In interference scenarios, the long edges of the actual Starlink signal configuration should remain approximately parallel, but glitches or jitter can cause fluctuations in the edge slope. If only the longest edge is taken, the short edge with severe jitter may be misidentified as the long edge. The setting of the slope difference threshold ensures that the two identified edges are highly consistent in direction, which not only solves the reliability problem of long edge identification but also lays an accurate foundation for subsequent definition of the bottom edge and height value calculation. In this embodiment, the above technical solution can effectively solve the problem of long edge misjudgment caused by edge glitches and long edge height jitter in Starlink signal interference scenarios, significantly improving the accuracy of bottom edge definition and height value calculation, thereby providing reliable technical support for multi-dimensional verification of Starlink signal configuration.

[0026] In one feasible implementation, the step of performing adaptive threshold segmentation on the height value data to obtain low-point region data and high-point region data includes: determining a segmentation threshold based on the height value data by analyzing the grayscale distribution of the height values; classifying pixels with height values ​​lower than the segmentation threshold as low-point region data, and classifying pixels with height values ​​higher than the segmentation threshold as high-point region data, thereby obtaining low-point region data and high-point region data.

[0027] In this embodiment, height value data refers to the height information of each pixel in the Starlink signal relative to the bottom edge of the rectangular bounding box, which can be obtained by calculating the vertical distance between the pixel and the bottom edge. In practical applications, the grayscale distribution of height values ​​refers to the statistical distribution characteristics of height value data, which can be achieved by histogram analysis or probability density function fitting, with the aim of dynamically capturing the changing characteristics of the height value distribution. The segmentation threshold is the critical value used to distinguish between low-point and high-point data, which can be determined by adaptive methods such as the maximum inter-class variance method or the mean shift algorithm, with the aim of ensuring that the segmentation result can adapt to the dynamic changes in the height value distribution. In this embodiment, the scheme dynamically derives a segmentation threshold suitable for the current data distribution by analyzing the grayscale distribution characteristics of the height value data, thereby avoiding the limitations of a fixed threshold in complex interference scenarios. Based on this, pixels with height values ​​below the segmentation threshold are classified as low-point data, which usually correspond to signal loss or interference areas; while pixels with height values ​​above the segmentation threshold are classified as high-point data, which correspond to effective signal areas. This classification mechanism fully leverages the actual characteristics of altitude distribution, ensuring the accuracy of low-point and high-point region division and providing a reliable data foundation for subsequent distribution rule verification. In this embodiment, the scheme, through dynamic analysis of altitude data, can also effectively address the potential altitude distribution shifts or fluctuations that may occur after Starlink signals are interfered with. For example, in scenarios with edge spikes or long-side altitude jitter, the altitude distribution may exhibit non-uniform characteristics. In such cases, by analyzing the actual distribution characteristics to derive the segmentation threshold, the segmentation critical point can be accurately captured, avoiding misjudgments. Simultaneously, region division based on the relative relationship between altitude values ​​and the segmentation threshold can significantly improve the robustness of low-point and high-point region classification, thereby enhancing the reliability of Starlink signal verification under complex interference scenarios.

[0028] In one feasible implementation, the step of performing distribution rule verification processing on the low-point region data and the high-point region data to obtain distribution rule verification result data includes: based on the low-point region data and the high-point region data, checking whether the low-point region is continuously distributed along the long side, and whether the length of a single continuous segment does not exceed a preset proportion of the total length of the long side; checking whether the positional deviation of the low-point region data on the two long sides relative to the central axis of the rectangle is less than a preset pixel threshold; when the conditions of continuous distribution of the low-point region along the long side, continuous length of a single segment not exceeding a preset proportion of the total length of the long side, and positional deviation of the low-point region data on the two long sides relative to the central axis of the rectangle being less than a preset pixel threshold are simultaneously met, the distribution rule verification result data of the valid starlink signal configuration is output; otherwise, the distribution rule verification result data of the invalid starlink signal configuration is output.

[0029] In this embodiment, the low-point region refers to the set of pixels with height values ​​below the segmentation threshold. This can be achieved using statistical analysis or histogram segmentation, aiming to accurately distinguish between low and high-point regions in the signal. The high-point region refers to the set of pixels with height values ​​above the segmentation threshold, which can be achieved using similar techniques, aiming to provide basic data support for subsequent joint analysis. Continuous distribution refers to the arrangement characteristics of the low-point region along its long side, which can be achieved using sliding window detection or connected component analysis, aiming to ensure that the distribution of the low-point region conforms to the inherent patterns of the Starlink signal. Positional deviation refers to the offset of the low-point region data along the two long sides relative to the central axis of the rectangle, which can be achieved using geometric symmetry analysis or distance measurement, aiming to evaluate the symmetry characteristics of the low-point region. In this embodiment, the scheme achieves refined determination of the Starlink signal configuration through a multi-dimensional distribution rule verification mechanism. First, based on low-point and high-point region data, it checks whether the low-point regions are continuously distributed along the long side and whether the length of a single continuous segment does not exceed a preset proportion of the total length of the long side. This process uses the continuity of the low-point region distribution along the long side as a key criterion. By setting a length proportion threshold, it avoids mistaking local spikes caused by transient interference for valid signal features, thus ensuring the reliability of verification in edge spike scenarios. Second, it checks whether the positional deviation of the low-point region data relative to the central axis of the rectangle on the two long sides is less than a preset pixel threshold. This process introduces the central axis as a symmetry benchmark to evaluate the positional consistency of the low-point regions on the two long sides, ensuring that the distribution conforms to the inherent symmetry characteristics of the Starlink signal and effectively suppressing misjudgments caused by height jitter on the long side. Finally, a valid result is output when the above continuity, length constraint, and positional deviation conditions are simultaneously met; otherwise, an invalid result is output. This process uses multi-condition logic for joint judgment, comprehensively considering the continuity of the low-point region distribution, length limit, and symmetry requirements, avoiding the limitations of a single rule, achieving comprehensive verification of the signal configuration, and significantly improving the robustness and accuracy of the verification process in complex interference environments. Furthermore, this approach, combined with the aforementioned simplified contour data and rectangular bounding box data, further enhances the joint analysis capability of multi-dimensional features of Starlink signal configuration, solving the problem that traditional methods struggle to handle interfered signals.

[0030] In one feasible implementation, the step of checking whether the positional deviation of the low point region data of the two long sides relative to the central axis of the rectangle is less than a preset pixel threshold further includes: based on the starlink signal feature template, checking whether the low point region data of the two long sides satisfies the symmetrical distribution feature on both sides of the central axis of the rectangle; if the symmetrical distribution feature is satisfied, output the distribution rule verification result data that satisfies the positional symmetry condition; otherwise, output the distribution rule verification result data that does not satisfy the positional symmetry condition.

[0031] In this embodiment, the Starlink signal feature template refers to a reference model that includes a symmetry benchmark of the standard Starlink signal configuration. It can be implemented using a preset mathematical formula or a graphical template. In practical applications, symmetrical distribution characteristics can be judged by analyzing the spatial distribution patterns of low-point region data on both sides of the rectangular central axis. The purpose is to improve the accuracy of verification by combining the inherent physical characteristics of the Starlink signal. Furthermore, the distribution rule verification result data can be understood as a logical state identifier used to characterize whether the low-point region distribution conforms to the standard Starlink signal configuration. This can be implemented using Boolean values ​​or classification labels. In this embodiment, the scheme overcomes the limitations of judging a single positional deviation threshold by introducing symmetrical distribution feature checks. First, based on the Starlink signal feature template, the symmetry of the low-point region data on both sides of the rectangular central axis is evaluated, which can effectively identify asymmetrical abnormal distributions caused by noise or frequency offset interference. Second, if the symmetry is confirmed to be satisfied, the result data that meets the conditions is output, providing a more reliable basis for subsequent signal existence determination; conversely, when the symmetry is not satisfied, abnormal distributions are promptly excluded, ensuring a more rigorous verification process. Building upon this foundation, the system's ability to distinguish between valid Starlink signals and noise interference is significantly enhanced through dynamic verification of symmetry dimensions and joint analysis of positional deviations. Furthermore, this method is organically integrated with the contour accuracy and regional distribution aspects of the aforementioned multi-dimensional Starlink signal configuration verification methods, thereby achieving refined verification of Starlink signal configurations from multiple dimensions and further enhancing the robustness and accuracy of the detection results.

[0032] In the embodiments of this application, the Starlink signal configuration multi-dimensional verification method comprehensively evaluates the geometric characteristics and spatial distribution patterns of the signal configuration through adaptive threshold processing, polygon approximation, multi-dimensional template matching and distribution rule verification, which can improve the accuracy and robustness of Starlink signal detection and effectively cope with signal configuration anomalies caused by frequency offset, noise and environmental interference, reducing the need for manual intervention.

[0033] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the multi-dimensional verification method of Starlink signal configuration in this application. Any simple transformations based on this technical concept are within the protection scope of this application.

[0034] This application also provides a multi-dimensional verification system for Starlink signal configuration, see reference. Figure 2 The Starlink signal configuration multi-dimensional verification system includes: a memory 10, a processor 20, and a Starlink signal configuration multi-dimensional verification program stored on the memory 10 and executable on the processor 20. The Starlink signal configuration multi-dimensional verification program is configured to implement the steps of the Starlink signal configuration multi-dimensional verification method.

[0035] The Starlink signal configuration multi-dimensional verification system provided in this application, employing the Starlink signal configuration multi-dimensional verification method in the above embodiments, can improve the accuracy and robustness of Starlink signal detection. Compared with the prior art, the beneficial effects of the Starlink signal configuration multi-dimensional verification system provided in this application are the same as those of the Starlink signal configuration multi-dimensional verification method provided in the above embodiments, and other technical features of the Starlink signal configuration multi-dimensional verification system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0036] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A multi-dimensional verification method for Starlink signal configuration, characterized in that, The method includes: The matching configuration graphic data is obtained through the FPGA programmable logic domain; the matching configuration graphic data is subjected to adaptive threshold binarization to obtain binary image data; the binary image data is subjected to Canny edge detection to obtain contour data; the contour data is subjected to polygon approximation to obtain simplified contour data. The simplified contour data is matched with the preset Starlink signal standard configuration template data to obtain rectangular bounding box data. Based on the rectangular bounding box data, calculate the total pixel data of the rectangular region; calculate the length and slope data of each side of the rectangle, and identify the long side data; define the lower edge of the rectangular bounding box as the bottom edge; calculate the height value data of each pixel in the long side data relative to the bottom edge; perform adaptive threshold segmentation processing on the height value data to obtain low point region data and high point region data; Based on the low-point region data and the high-point region data, calculate the area ratio of the total number of pixels in the low-point region to the total number of pixels in the rectangular region; if the area ratio exceeds a preset threshold, perform distribution rule verification processing on the low-point region data and the high-point region data to obtain distribution rule verification result data; based on the distribution rule verification result data, determine whether the Starlink signal exists to obtain Starlink signal existence determination data.

2. The Starlink signal configuration multi-dimensional verification method as described in claim 1, characterized in that, The matched configuration graphic data is subjected to adaptive threshold binarization to obtain binary image data; the binary image data is then subjected to Canny edge detection to obtain contour data. The steps of performing polygon approximation processing on the contour data to obtain simplified contour data include: The matched configuration graphic data is subjected to adaptive threshold binarization to obtain binary image data. The binary image data is processed by Canny edge detection, which involves smoothing the image with Gaussian filtering, calculating the gradient magnitude and direction, applying non-maximum suppression to refine the edges, and using double threshold filtering to obtain contour data. The contour data is subjected to polygon approximation processing. The contour is simplified by setting an approximation error threshold to obtain simplified contour data with four-sided features.

3. The Starlink signal configuration multi-dimensional verification method as described in claim 1, characterized in that, The step of performing template matching processing between the simplified contour data and the preset starlink signal standard configuration template data to obtain rectangular bounding box data includes: The simplified contour data is subjected to normalized cross-correlation matching processing with the preset starlink signal standard configuration template data to calculate pixel grayscale similarity and obtain similarity data. Based on the similarity data, the region with the highest similarity is selected, and the rectangular bounding box data is output.

4. The Starlink signal configuration multi-dimensional verification method as described in claim 3, characterized in that, The method further includes: The preset Starlink signal standard configuration template data is scaled to generate a multi-scale template dataset within a preset range; The multi-scale template dataset is matched with the simplified contour data, and the best matching scale is selected to obtain rectangular bounding box data.

5. The Starlink signal configuration multi-dimensional verification method as described in claim 3, characterized in that, The method further includes: The preset Starlink signal standard configuration template data is rotated to generate a multi-angle template dataset within a preset angle range; The multi-angle template dataset is matched with the simplified contour data, and the best matching angle is selected to obtain rectangular bounding box data.

6. The Starlink signal configuration multi-dimensional verification method as described in claim 1, characterized in that, The steps for calculating the length and slope of each side of a rectangle and identifying the longest side include: Based on the rectangular bounding box data, calculate the length and slope of each side; The two longest edges with a slope difference less than a preset threshold are identified as the long edge data.

7. The Starlink signal configuration multi-dimensional verification method as described in claim 1, characterized in that, The steps of performing adaptive threshold segmentation on the height value data to obtain low-point region data and high-point region data include: Based on the height value data, the segmentation threshold is determined by analyzing the grayscale distribution of the height values; Pixels with height values ​​below the segmentation threshold are classified as low-point region data, and pixels with height values ​​above the segmentation threshold are classified as high-point region data, in order to obtain low-point region data and high-point region data.

8. The Starlink signal configuration multi-dimensional verification method as described in claim 1, characterized in that, The steps of performing distribution rule verification processing on the low-point region data and the high-point region data to obtain distribution rule verification result data include: Based on the low point region data and the high point region data, check whether the low point region is continuously distributed along the long side, and whether the length of a single continuous segment does not exceed a preset proportion of the total length of the long side. Check whether the positional deviation of the low point region data of the two long sides relative to the central axis of the rectangle is less than the preset pixel threshold; When the following conditions are met simultaneously: the low point region is continuously distributed along the long side, the length of a single continuous segment does not exceed the total length of the long side, and the positional deviation of the low point region data of the two long sides relative to the central axis of the rectangle is less than the preset pixel threshold, the distribution rule verification result data of the effective star chain signal configuration is output. Otherwise, output the distribution rule verification result data of the invalid starlink signal configuration.

9. The Starlink signal configuration multi-dimensional verification method as described in claim 8, characterized in that, The step of checking whether the positional deviation of the low point region data of the two long sides relative to the central axis of the rectangle is less than a preset pixel threshold also includes: Based on the starlink signal feature template, check whether the low point region data on the two long sides satisfy the symmetrical distribution characteristics on both sides of the central axis of the rectangle. If the symmetrical distribution characteristic is satisfied, the distribution rule verification result data that satisfies the positional symmetry condition will be output; otherwise, the distribution rule verification result data that does not satisfy the positional symmetry condition will be output.

10. A multi-dimensional verification system for Starlink signal configuration, characterized in that, The Starlink signal configuration multi-dimensional verification system includes: a memory, a processor, and a Starlink signal configuration multi-dimensional verification program stored in the memory and executable on the processor, wherein the Starlink signal configuration multi-dimensional verification program is configured to implement the steps of the Starlink signal configuration multi-dimensional verification method as described in any one of claims 1 to 9.

Images