Arc path three-dimensional reconstruction method and system based on biaxial solid-state single photon array

Abstract

The invention discloses an arc path three-dimensional reconstruction method and system based on a double-axis solid-state single photon array, and belongs to the technical field of fault detection of high-voltage electrical equipment. Performing time domain sliding window integration and Gaussian fitting peak value extraction on the time scale data, performing morphological top-hat transformation and adaptive threshold segmentation, and determining light intensity distribution characteristics of the arc in the axial direction and the radial direction; spatial light intensity distribution reconstruction is carried out based on the light intensity distribution characteristics of the arc in the axial direction and the radial direction, an initial three-dimensional reconstruction result of an arc path is generated, a dynamic time warping algorithm is adopted to match double-array time sequence characteristics, and three-dimensional reconstruction of the arc path is completed. According to the method, high-precision extraction of light intensity distribution can be realized, the spatial resolution and the anti-noise capability are remarkably improved, the problem of asynchronous detection time sequences of multiple photoelectric units is solved, the space-time continuity of dynamic evolution of an electric arc is ensured, and accurate three-dimensional reconstruction of a complex path is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-voltage electrical equipment fault detection, and in particular relates to a method and system for three-dimensional reconstruction of arc paths based on a dual-axis solid-state single-photon array. Background Art

[0002] In high-voltage electrical systems, arc faults are extremely destructive abnormal phenomena. The generation of arcs is often accompanied by a huge release of energy. If its development path cannot be detected and located in a timely and accurate manner, it can easily cause serious consequences such as equipment damage, fire, and even large-scale power outages. Existing arc detection technology has many limitations and cannot meet the stringent requirements of high-voltage electrical equipment for high-precision and high-timeliness arc monitoring. At present, existing photoelectric sensors, such as photomultiplier tubes, although widely used in the field of arc detection, are only limited to determining whether an arc exists, and there is no way to obtain spatial information about the arc development path. This is like only knowing that there is light, but not being able to determine the specific location and movement trajectory of the light source, which has obvious defects in practical applications.

[0003] High-speed camera observation was once highly anticipated for arc monitoring. However, its frame rate is typically below 1 MHz, making it difficult to accurately capture critical moments in the microsecond-scale dynamics of arc development. Furthermore, high-speed cameras are extremely sensitive to optical interference. In complex high-voltage electrical environments, various stray light signals can easily interfere with their normal operation, resulting in degraded image quality and an increased risk of misjudgment. Furthermore, the high cost of high-speed camera equipment places significant economic pressure on large-scale application. Fiber array detection, which uses a spatially arranged fiber array to sense arc light signals, improves temporal resolution to some extent. However, due to limitations in fiber density, the spatial resolution is less than 0.5 mm. Arc path capture is often costly and requires demanding measurement conditions, making it difficult to provide sufficiently detailed data for subsequent fault analysis. Existing multi-sensor fusion methods attempt to improve arc monitoring by integrating the strengths of different sensor types. However, their discrete arrangement leads to data synchronization errors between sensors, often exceeding 10 μs. This significantly reduces path reconstruction accuracy and prevents accurate reconstruction of the arc's true trajectory.

[0004] Furthermore, the arc radiation spectrum has multi-band characteristics, and conventional photoelectric sensors experience selective attenuation during detection. Existing technologies address this issue by using optical filters. However, due to the characteristics of these filters, some effective optical signals are lost, further weakening the accuracy and reliability of arc monitoring. This makes it difficult to effectively guarantee the three-dimensional reconstruction accuracy of the arc path, thus restricting the development of high-voltage electrical equipment fault detection technology. In summary, current high-voltage electrical equipment fault detection cannot accurately monitor the dynamic development trajectory of arcs in high-voltage environments, which in turn affects the safe operation and reliability of high-voltage electrical equipment. Summary of the Invention

[0005] The present invention provides a three-dimensional reconstruction method and system for arc paths based on a dual-axis solid-state single-photon array. The purpose is to solve the current problem in high-voltage electrical equipment fault detection that the dynamic development trajectory of arcs in high-voltage environments cannot be monitored relatively accurately, thereby affecting the safe operation and reliability of high-voltage electrical equipment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for three-dimensional reconstruction of arc paths based on a dual-axis solid-state single-photon array, comprising the following steps: S1, synchronously collect the time-scale data of the arc light signal through a dual-axis solid-state single-photon array, Wherein, the dual-axis solid-state single-photon array includes a first linear array and a second linear array; S2. Perform time domain sliding window integration and Gaussian fitting peak extraction on the collected first linear array time-scale data to determine the light intensity distribution characteristics of the arc in the axial direction; and simultaneously perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array time-scale data to determine the light intensity distribution characteristics of the arc in the radial direction; S3. Based on the axial and radial light intensity distribution characteristics of the arc, the improved Voronoi interpolation algorithm is used to reconstruct the spatial light intensity distribution and generate the initial three-dimensional reconstruction result of the arc path; S4. Based on the initial 3D reconstruction results of the arc path, a dynamic time warping algorithm is used to match the timing characteristics of the dual arrays to complete the 3D reconstruction of the arc path.

[0007] In some implementations, in S1 , a dual-channel sampling system is used to collect time-scale data of the arc light signal, and the time-scale data of the arc light signal is optimized by a sliding window algorithm.

[0008] In some implementations, in S2, the window width of the time domain sliding window integration is 10 ns, and the adaptive threshold segmentation is a threshold that is dynamically adjusted according to background noise.

[0009] In some implementations, S3 specifically includes: S31, using the light intensity feature points extracted from the first linear array and the second linear array as generation points, constructing a Voronoi diagram to divide the three-dimensional space around the arc path; S32. In each Voronoi region, construct an interpolation function and introduce a dynamic factor; S33. For the point to be interpolated, according to the Voronoi region to which the point to be interpolated belongs, the light intensity value of the interpolation point is calculated using the weight factors of the generated point and the point to be interpolated in the Voronoi region, and then the spatial light intensity distribution is reconstructed to generate the initial three-dimensional reconstruction result of the arc path.

[0010] Furthermore, in S32, the dynamic weight factor introduced is as follows: (1); in, =5ns, is the signal-to-noise ratio of each unit, is the weight factor.

[0011] Furthermore, in S33, the light intensity value of the interpolation point is calculated using the following formula (2): (2); in, is the light intensity value at the interpolation point, For the The light intensity value of the generated point, To interpolate the point to The weight factor of each spawn point.

[0012] Furthermore, it also includes S34, using the moving least square method to smooth the interpolation result, and introducing a regularization constraint term to optimize the reconstruction path.

[0013] Furthermore, in S34, the regularization constraint term is introduced, specifically including: The extended Kalman filter is used for state estimation, and the regularization constraint term of the following formula (4) is introduced: (3); in, yes The second derivative with respect to time, that is, acceleration, is the square of the modulus of acceleration. As a regularization term, the algorithm tends to choose a smoother path with smaller acceleration. λ It is a regularization parameter used to balance the weight between the original objective function and the regularization term. It is set through experimental data and prior knowledge, and can also be determined through cross-validation or empirical adjustment.

[0014] In some embodiments, in S4, the arc development dynamics model of the following formula (4) is used to perform three-dimensional reconstruction of the arc path: (4); in, Indicates arc position About time The derivative of , that is, the speed of the arc, represents the driving force of the electric field on the arc movement, where α is the coefficient, It depends on the location The changing electric field strength, Indicates the resistance encountered by the arc during its travel, where is the coefficient, is the square of the position.

[0015] The present invention also provides an arc path three-dimensional reconstruction system based on a dual-axis solid-state single-photon array, the system comprising a solid-state single-photon linear array with orthogonal axes, a data processing module, a light intensity distribution reconstruction module, and a three-dimensional reconstruction module; wherein: The solid-state single-photon linear array comprises an axially arranged first linear array and a radially orthogonally arranged second linear array; each of the first linear array or the second linear array is composed of a solid-state single-photon photoelectric unit, each of which is integrated with a microlens system; each of the solid-state single-photon photoelectric units is integrated with a gallium nitride-based single-photon avalanche diode; the lens system comprises a three-piece aspheric lens group; wherein: The optical aperture of the aspheric lens group is 0.75~0.85mm, the acceptance half-angle is 17°~19°, and the optical modulation transfer function is greater than 0.6 at 50lp / mm; the quantum efficiency of the solid-state single-photon photoelectric unit is greater than 35% in the 500~900nm band; Axis-orthogonally arranged solid-state single-photon linear array: used to synchronously acquire time-scale data of arc light signals through a dual-axis solid-state single-photon array; Data processing module: used to perform time domain sliding window integration and Gaussian fitting peak extraction on the collected first linear array time-scale data to determine the arc's axial light intensity distribution characteristics; at the same time, perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array time-scale data to determine the arc's radial light intensity distribution characteristics; Light intensity distribution reconstruction module: It is used to reconstruct the spatial light intensity distribution based on the light intensity distribution characteristics of the arc in the axial and radial directions, using the improved Voronoi interpolation algorithm to generate the initial three-dimensional reconstruction results of the arc path; 3D reconstruction module: Based on the initial 3D reconstruction results of the arc path, it uses a dynamic time warping algorithm to match the timing characteristics of the dual arrays to complete the 3D reconstruction of the arc path.

[0016] Compared with the existing technology, the arc path three-dimensional reconstruction method and system based on the dual-axis solid-state single-photon array of the present invention has the following beneficial effects: The present invention is based on a three-dimensional arc path reconstruction method using a dual-axis solid-state single-photon array, which can improve the high sensitivity and rapid signal capture capabilities of arc detection. By adopting a dual-axis solid-state single-photon array and a distributed lens system, the present invention can efficiently capture arc dynamic signals in low-light-intensity environments without the need for image intensification or additional photoelectric enhancement, nor does it require the use of a high-cost high-speed enhanced imaging system. The dual-axis orthogonal design synchronously collects axial and radial light intensity features. By integrating multiple algorithms such as time-domain sliding window integration, Gaussian fitting, and morphological top-hat transformation, high-precision extraction of light intensity distribution is achieved, significantly improving spatial resolution and noise resistance. The present invention can improve the accuracy and anti-interference performance of dynamic three-dimensional reconstruction. It adopts an improved interpolation algorithm to introduce dynamic weight factors, combined with moving least squares smoothing and regularization constraints, effectively suppressing noise interference and optimizing path smoothness. The dual-axis time series features are matched through a dynamic time warping algorithm to solve the problem of asynchronous detection time series of multiple photoelectric units, ensure the spatiotemporal coherence of the dynamic evolution of the arc, and achieve accurate three-dimensional reconstruction of complex paths.

[0017] Furthermore, this invention improves system robustness and environmental adaptability. The use of arc-proof shielded cavities and solid-state optoelectronic devices significantly reduces external electromagnetic interference. Furthermore, the combination of a solid-state single-photon array and an aspheric lens ensures high light collection efficiency, enabling the measurement system to operate normally in strong magnetic and high-frequency electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0019] Figure 1 Schematic diagram of the process of the arc path three-dimensional reconstruction method based on the dual-axis solid-state single-photon array of the present invention; Figure 2 Schematic diagram of the architecture of the arc path three-dimensional reconstruction system based on the dual-axis solid-state single-photon array of the present invention; Figure 3 Schematic diagram of CCD shooting results (a) and reconstruction inversion results (b) in an embodiment of the arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0022] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0023] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0025] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0026] How to accurately monitor the dynamic development trajectory of arcs under high-voltage environments to provide reliable protection for the safe operation of high-voltage electrical equipment.

[0027] Based on this, Figure 1 As shown, the present invention is based on a method for three-dimensional reconstruction of arc paths using a dual-axis solid-state single-photon array, comprising the following steps: S1, synchronously collect the time-scale data of the arc light signal through a dual-axis solid-state single-photon array, Wherein, the dual-axis solid-state single-photon array includes a first linear array and a second linear array; S2. Perform time domain sliding window integration and Gaussian fitting peak extraction on the collected first linear array time-scale data to determine the light intensity distribution characteristics of the arc in the axial direction; and simultaneously perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array time-scale data to determine the light intensity distribution characteristics of the arc in the radial direction; S3. Based on the axial and radial light intensity distribution characteristics of the arc, the improved Voronoi interpolation algorithm is used to reconstruct the spatial light intensity distribution and generate the initial three-dimensional reconstruction result of the arc path; S4. Based on the initial 3D reconstruction results of the arc path, a dynamic time warping algorithm is used to match the timing characteristics of the dual arrays to complete the 3D reconstruction of the arc path.

[0028] In some embodiments, the present invention is based on a method for three-dimensional reconstruction of arc paths using a dual-axis solid-state single-photon array. Specifically: (a) Time domain signal processing; This invention utilizes a dual-channel sampling system with a sampling rate of up to 200MSa / s and a temporal resolution of 5ns, ensuring rapid and accurate sampling of arc light signals, without missing any critical moments in the arc's dynamic development. A sliding window correlation algorithm eliminates clock deviations in the dual arrays. By setting a sliding window of appropriate size, point-by-point correlation calculations are performed on the dual array signals along the time axis to identify the time point with minimal clock deviation, with an accuracy of up to 0.1ns. This allows for precise synchronization of the dual array signals and provides an accurate time reference for subsequent spatial reconstruction.

[0029] (b) spatial reconstruction algorithm; Establishing light intensity distribution model: ,in Indicates the location x and time t The light intensity at A i ( t ) is the i The amplitude of the peak light intensity, x i is the position of the peak light intensity, σ is the standard deviation of the light intensity distribution. This model can accurately describe the distribution characteristics of arc light intensity in space and time.

[0030] An improved Voronoi interpolation algorithm is introduced to reconstruct the spatial light intensity distribution. Traditional Voronoi interpolation algorithms suffer from insufficient precision when dealing with complex spatial distributions. This invention improves upon this by optimizing the Voronoi diagram generation rules and interpolation weight calculation method, enhancing interpolation accuracy and more accurately reconstructing the continuous spatial distribution of arc light intensity.

[0031] The dynamic time warping (DTW) algorithm is used to match the timing characteristics of the dual arrays. The DTW algorithm effectively handles nonlinear time distortion between different time series. In this invention, the DTW distance between the timing of the dual array optical signals is calculated to find the optimal time matching path, achieving precise matching of the dual array timing characteristics and further improving the accuracy of spatial reconstruction.

[0032] like Figure 2 As shown, the present invention also provides a three-dimensional arc path reconstruction system based on a dual-axis solid-state single-photon array, the system comprising: a) a first linear array arranged axially and a second linear array arranged radially and orthogonally, each array consisting of a solid-state single-photon photoelectric unit, each unit integrating a microlens system, the arrays being spaced 10 cm ± 5% from the electrode surface, and forming a spatially orthogonal relationship between the arrays; b) The microlens system consists of a three-piece aspheric lens group with a clear aperture of 0.8±0.05mm, an acceptance half-angle of 18°±1°, and an optical modulation transfer function greater than 0.6 at 50lp / mm; c) The photoelectric units are arranged linearly with a pitch of 2 mm, the array length is greater than or equal to 120% of the electrode pitch, and the unit quantum efficiency is greater than 35% in the 500-900 nm band; d) Used to synchronously acquire single-photon event time-stamp data from each unit of the dual array and perform time-domain signal processing, including building a dual-channel sampling system with a sampling rate of 200MSa / s and a time resolution of 5ns, and using a sliding window correlation algorithm to eliminate dual-array clock deviations with an accuracy of 0.2ns.

[0033] Furthermore, each photoelectric unit of the first linear array and the second linear array of the present invention integrates a gallium nitride-based single photon avalanche diode (SPAD); The system also includes an arc interference protection shielding chamber for installing the array, ensuring that the distance d between the array and the center axis of the electrode satisfies d=10cm±5%.

[0034] Use the above arc path three-dimensional reconstruction system to synchronously collect the single photon event time-scale data of each unit in the dual array; Perform time domain sliding window integration (window width 10ns) and Gaussian fitting peak extraction on the first linear array data to determine the arc's axial light intensity distribution characteristics; perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array data to determine the arc's radial light intensity distribution characteristics; The spatial light intensity distribution is reconstructed based on the improved Voronoi interpolation algorithm, specifically including: The light intensity feature points extracted from the first linear array and the second linear array are used as generating points to construct a Voronoi diagram to divide the three-dimensional space around the arc path; In each Voronoi region, an interpolation function is constructed and a dynamic weight factor is introduced: (1); in, =5ns, is the signal-to-noise ratio of each unit, is the weight factor.

[0035] For the point to be interpolated, according to the Voronoi region to which it belongs, the light intensity value of the interpolation point is calculated using the generated points in the region and their weight factors: (2); in, is the light intensity value at the interpolation point, For the The light intensity value of the generated point, To interpolate the point to The weight factor of each spawn point.

[0036] The moving least squares method is used to smooth the interpolation results, and a regularization constraint is introduced: (3); The smoothness and continuity of the reconstructed path are optimized by formula (3); yes The second derivative with respect to time, that is, acceleration, is the square of the modulus of acceleration. As a regularization term, the algorithm tends to choose a smoother path with smaller acceleration. λ It is a regularization parameter used to balance the weight between the original objective function and the regularization term. It is set through experimental data and prior knowledge, and can also be determined through cross-validation or empirical adjustment.

[0037] The three-dimensional reconstruction of the arc path is completed by matching the timing characteristics of the dual arrays based on the dynamic time warping (DTW) algorithm.

[0038] In some embodiments, the window width of the time domain sliding window integration is 10ns, which is used to enhance the intensity of the a array signal, the morphological top hat transform is used to highlight the arc light intensity peak in the b array signal, and the adaptive threshold segmentation is used to separate the arc light intensity signal from the background noise.

[0039] Furthermore, in the improved Voronoi interpolation algorithm, the generating points are the characteristic points of the peak position and intensity information of the light intensity after feature extraction; the calculation of the dynamic weight factor is based on the time characteristics and signal-to-noise ratio of the generating points, and a greater weight is given to the generating points that are closer to the interpolation point in time and have a higher signal-to-noise ratio; the moving least squares smoothing process optimizes the interpolation result by fitting a smoothing function through the local weighted least squares method.

[0040] Furthermore, the 3D reconstruction of the arc path relies not only on measured data but also incorporates the dynamics of arc motion. The 3D reconstruction also includes establishing a dynamic model of arc development: (4); in, Indicates arc position x About time t The derivative of , that is, the speed of the arc. represents the driving force of the electric field on the arc movement, where α is a coefficient that can be determined by varying the electrode voltage and measuring the arc travel speed α， Usually in arrive m / (Vs). E ( x ) is position dependent x Changing electric field strength, electric field strength E ( x ) Usually in arrive In the range of V / m. Indicates the resistance encountered by the arc during its travel, where is a coefficient that can be determined by measuring the arc's speed at different locations. is the square of the position.

[0041] The extended Kalman filter is used for state estimation, and the regularization constraint is introduced: .in, yes The second derivative with respect to time is acceleration. is the square of the modulus of acceleration. As a regularization term, the algorithm tends to choose a smoother path with smaller acceleration. λ It is a regularization parameter used to balance the weight between the original objective function and the regularization term. It is set by experimental data and prior knowledge, and can also be determined by cross-validation or empirical adjustment. It is usually arrive within the range.

[0042] The system of the present invention further comprises a data processing unit for executing data processing steps. The data processing unit comprises a signal preprocessing module, a feature extraction module and a path reconstruction module.

[0043] The present invention can achieve high-precision three-dimensional reconstruction of the arc path with a time resolution of 0.1μs and a spatial positioning accuracy of 0.8mm (axial) and 1.2mm (radial). The system is suitable for arc fault diagnosis of high-voltage switchgear and can effectively improve the safety and reliability of the equipment.

[0044] The arc path three-dimensional reconstruction method and system based on the dual-axis solid-state single-photon array of the present invention are further described in detail below through specific embodiments.

[0045] like Figure 2 and Figure 3 As shown, the system of the present invention utilizes two orthogonally arranged solid-state single-photon linear arrays. The first linear array is arranged axially, and the second linear array is arranged radially. The two linear arrays work together to capture arc light signals in all directions. Each array contains 128 independent photoelectric cells, with the cell spacing precisely controlled at 0.5mm, ensuring high-density sampling of spatial information.

[0046] Each optoelectronic unit integrates an advanced gallium nitride-based single-photon avalanche diode (SPAD), boasting extremely high single-photon detection sensitivity. This allows for stable operation even in weak light signal conditions, accurately sensing arc photon events. Furthermore, an aspheric microlens array with a 0.8mm aperture and a 1.2mm focal length effectively focuses the incoming light signal, improving light collection efficiency. An angle limiter restricts the receiving angle to within 18°±1°, preventing stray light interference and ensuring accurate detection direction.

[0047] The solid-state single-photon linear array is installed in an arc-interference shielded cavity. The distance d from the electrode surface has been precisely simulated and verified and is set to 10cm±5%. At this distance, the spatial resolution of the device reaches the optimal state, and it can accurately capture the tiny displacement of the arc in three-dimensional space, providing a high-precision data basis for subsequent path reconstruction.

[0048] In this embodiment, the specific experimental settings are: Electrode parameters: Tungsten-copper alloy rod electrodes with a diameter of 8mm and a spacing of 50mm are used. This electrode material has good conductivity and arc erosion resistance and can generate arc stably.

[0049] Arc parameters: The arc is generated by industrial frequency AC breakdown, with a peak current of up to 5kA and an arcing time of 8ms. Under these conditions, the arc development process has typical dynamic characteristics, which can fully test the performance of the device and method of the present invention.

[0050] Array parameters: Each array contains 128 units, the unit spacing is 2mm, and the sampling rate is 200MHz, ensuring high-density and high-speed sampling of arc light signals.

[0051] Data processing procedures; Signal preprocessing: Time domain sliding average filtering (window width 10ns): By performing time domain sliding average filtering on the original signal, the signal is smoothed on the time axis to effectively remove high-frequency noise interference and retain the main characteristics of the arc light signal.

[0052] Background noise subtraction: By subtracting background noise, the arc light signal is highlighted and the signal-to-noise ratio is improved.

[0053] Feature extraction: The axial array uses cubic spline interpolation to locate the front end position of the arc: The cubic spline interpolation algorithm is used to interpolate the peak position of the light intensity extracted by the linear array to accurately determine the front end position of the arc in the axial and radial directions, providing key spatial coordinate information for path reconstruction.

[0054] Path reconstruction; Time alignment: A cross-correlation algorithm is used to determine the dual-array delay, and the measured Δt=3.2ns. Through time alignment, the temporal synchronization of the dual-array signals is ensured, providing an accurate time reference for subsequent spatial fusion.

[0055] Spatial fusion: joint probability density function can be constructed ; in and are the probability density functions of array a and array b in position and time respectively. Through spatial fusion, the feature information extracted by the dual arrays is integrated to form complete three-dimensional spatial information.

[0056] 3D reconstruction: The moving least squares method is used to generate a continuous path. The moving least squares method can generate a smooth 3D path curve based on discrete feature points, intuitively displaying the arc development trajectory and providing visual data support for high-voltage electrical equipment fault analysis.

[0057] Experiments have shown that the proposed three-dimensional arc path reconstruction system, based on a dual-axis solid-state single-photon array, achieves a temporal resolution of 0.1 μs, more than 10 times higher than that of a high-speed camera. This system can accurately capture the dynamic microsecond evolution of arcs, providing strong support for early warning and rapid response to arc faults. With an axial positioning accuracy of 1.2 mm (RMS) and a radial positioning accuracy of 0.8 mm (RMS), this high-precision spatial positioning accurately restores the arc's position and shape in three dimensions, meeting the precision requirements for arc monitoring in high-voltage electrical equipment. Furthermore, the path length captured by the CCD high-speed camera is 48.7 mm, while the measured value is 49.3 mm, with an error of only +1.2%. The radial detection error is less than 5%, demonstrating that the proposed device and method can accurately reconstruct the arc path, providing a reliable basis for fault diagnosis in high-voltage electrical equipment. The proposed three-dimensional arc path reconstruction method, based on a dual-axis solid-state single-photon array, utilizes a noise suppression algorithm within its feature enhancement design to increase the signal-to-noise ratio of the original signal from 6 dB to 22 dB, significantly improving signal quality and enhancing the detectability of arc light signals. On the Xeon E5-2680v4 processor, the processing time for a single arcing event is less than 20ms, with real-time processing capabilities to meet the needs of online monitoring of high-voltage electrical equipment.

[0058] Finally, it should be noted that the above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as shown in the specification and described above. Any equivalent changes, modifications and evolutions made by using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

Claims

1. A three-dimensional reconstruction method of arc path based on a dual-axis solid-state single-photon array, characterized in that: The steps include: S1, synchronously collect the time-scale data of the arc light signal through a dual-axis solid-state single-photon array, Wherein, the dual-axis solid-state single-photon array includes a first linear array and a second linear array; S2. Perform time domain sliding window integration and Gaussian fitting peak extraction on the collected first linear array time-scale data to determine the light intensity distribution characteristics of the arc in the axial direction; and simultaneously perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array time-scale data to determine the light intensity distribution characteristics of the arc in the radial direction; S3. Based on the axial and radial light intensity distribution characteristics of the arc, the improved Voronoi interpolation algorithm is used to reconstruct the spatial light intensity distribution and generate the initial three-dimensional reconstruction result of the arc path; S4. Based on the initial 3D reconstruction results of the arc path, a dynamic time warping algorithm is used to match the timing characteristics of the dual arrays to complete the 3D reconstruction of the arc path.

2. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 1, characterized in that: In S1, a dual-channel sampling system is used to collect time-scale data of the arc light signal, and the time-scale data of the arc light signal is optimized by a sliding window algorithm.

3. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 1, characterized in that: In S2, the window width of the time domain sliding window integration is 10 ns, and the adaptive threshold segmentation is a threshold that is dynamically adjusted according to the background noise.

4. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 1, characterized in that: The S3 specifically includes: S31, using the light intensity feature points extracted from the first linear array and the second linear array as generation points, constructing a Voronoi diagram to divide the three-dimensional space around the arc path; S32. In each Voronoi region, construct an interpolation function and introduce a dynamic factor; S33. For the point to be interpolated, according to the Voronoi region to which the point to be interpolated belongs, the light intensity value of the interpolation point is calculated using the weight factors of the generated point and the point to be interpolated in the Voronoi region, and then the spatial light intensity distribution is reconstructed to generate the initial three-dimensional reconstruction result of the arc path.

5. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 4, characterized in that: In the S32, the dynamic weight factor introduced is as follows: （1）； in, =5ns, is the signal-to-noise ratio of each unit, is the weight factor.

6. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 4, characterized in that: In S33, the light intensity value of the interpolation point is calculated using the following formula (2): （2）； in, is the light intensity value at the interpolation point, For the The light intensity value of the generated point, To interpolate the point to The weight factor of each spawn point.

7. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 4, characterized in that: It also includes S34, which uses the moving least squares method to smooth the interpolation results and introduces regularization constraints to optimize the reconstruction path.

8. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 7, characterized in that: In said S34, the introduction of regularization constraint items specifically includes: The extended Kalman filter is used for state estimation, and the regularization constraint term of the following formula (3) is introduced: （3）； in, yes The second derivative with respect to time, that is, acceleration, is the square of the modulus of acceleration. As a regularization term, the algorithm tends to choose a smoother path with smaller acceleration. λ It is a regularization parameter used to balance the weight between the original objective function and the regularization term. It is set through experimental data and prior knowledge, and can also be determined through cross-validation or empirical adjustment.

9. The arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to claim 1, characterized in that: In the S4, the arc development dynamics model of the following formula (4) is used to perform three-dimensional reconstruction of the arc path: （4）； in, Indicates arc position About time The derivative of , that is, the speed of the arc, represents the driving force of the electric field on the arc movement, where α is the coefficient, It depends on the location The changing electric field strength, Indicates the resistance encountered by the arc during its travel, where is the coefficient, is the square of the position.

10. A system based on the arc path three-dimensional reconstruction method based on a dual-axis solid-state single-photon array according to any one of claims 1 to 9, characterized in that: The system includes a solid-state single-photon linear array with orthogonal axes, a data processing module, a light intensity distribution reconstruction module, and a three-dimensional reconstruction module; wherein: The solid-state single-photon linear array comprises an axially arranged first linear array and a radially orthogonally arranged second linear array; each of the first linear array or the second linear array is composed of a solid-state single-photon photoelectric unit, each of which is integrated with a microlens system; each of the solid-state single-photon photoelectric units is integrated with a gallium nitride-based single-photon avalanche diode; the lens system comprises a three-piece aspheric lens group; wherein: The optical aperture of the aspheric lens group is 0.75~0.85mm, the acceptance half-angle is 17°~19°, and the optical modulation transfer function is greater than 0.6 at 50lp / mm; the quantum efficiency of the solid-state single-photon photoelectric unit is greater than 35% in the 500~900nm band; Axis-orthogonally arranged solid-state single-photon linear array: used to synchronously acquire time-scale data of arc light signals through a dual-axis solid-state single-photon array; Data processing module: used to perform time domain sliding window integration and Gaussian fitting peak extraction on the collected first linear array time-scale data to determine the arc's axial light intensity distribution characteristics; at the same time, perform morphological top-hat transformation and adaptive threshold segmentation on the second linear array time-scale data to determine the arc's radial light intensity distribution characteristics; Light intensity distribution reconstruction module: It is used to reconstruct the spatial light intensity distribution based on the light intensity distribution characteristics of the arc in the axial and radial directions, using the improved Voronoi interpolation algorithm to generate the initial three-dimensional reconstruction results of the arc path; 3D reconstruction module: Based on the initial 3D reconstruction results of the arc path, it uses a dynamic time warping algorithm to match the timing characteristics of the dual arrays to complete the 3D reconstruction of the arc path.

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