Three-dimensional track reconstruction method and device for muon imaging

Abstract

The invention discloses a three-dimensional track reconstruction method and device for muon imaging, and relates to the field of signal detection and ray imaging, and the method comprises the steps: obtaining an original waveform signal of muon imaging, and carrying out the preprocessing of the original waveform signal; performing channel waveform analysis on the preprocessed original waveform signal by adopting a waveform identification technology to obtain a channel waveform analysis result; in combination with a detector-electronics channel mapping table, decoding continuous channel information by utilizing hit continuity characteristic channel clustering; on the basis of the continuous channel information, utilizing a spatial point clustering algorithm to take spatially adjacent electronic signals in the continuous clusters as the same electronic cloud cluster; and based on the three-dimensional barycentric coordinates of the plurality of electronic clouds, completing three-dimensional track reconstruction by using a screening reconstruction algorithm. According to the method, the problem of fuzzy reconstruction of a small-angle incident muon example is solved through channel waveform analysis, hit cluster searching and three-dimensional track reconstruction.

Description

Technical Field

[0001] This application relates to the fields of signal detection and X-ray imaging, and in particular to a method and apparatus for three-dimensional track reconstruction using muon imaging. Background Technology

[0002] Cosmic ray muons are naturally available and possess advantages such as strong penetrating power and wide coverage. As an advanced non-destructive testing technology, muon imaging technology has shown great application potential in many fields, including large-scale material imaging (such as seeing through the internal structure of volcanoes and detecting large underground cavities), nuclear material and heavy metal detection (achieving non-invasive detection of sealed containers such as containers and vehicles), and archaeological research (such as scanning the internal chambers of pyramids). The core of its imaging quality lies in its ability to reconstruct high-precision three-dimensional muon tracks.

[0003] Currently, most muon track detection devices reconstruct three-dimensional muon tracks by constructing multi-layer detector arrays. The basic principle is that each detector layer (usually composed of two sets of orthogonally arranged detector units) measures the two-dimensional coordinates (X, Y) of the muon passing through that layer; by using the coordinates of multiple layers (at least the top and bottom layers), a three-dimensional straight line can be fitted, thus determining the incident track. However, this approach has significant limitations: achieving high spatial resolution often requires increasing the number of detector layers, leading to a complex system structure and large size. Furthermore, the system's geometric acceptability is limited, and the effective number of muon events available for imaging is limited by the cumulative efficiency of the multi-layer detectors, directly affecting imaging time and accuracy. The time projection chamber (TPC) combined with a microstructured gas readout plane (such as micromegas or GEM) offers a promising alternative approach. A time projection chamber is a gas detector capable of continuously recording charged particle tracks in three-dimensional space. The working principle is as follows: when muons pass through the sensitive volume of the TPC, they interact with the working gas, causing the gas molecules to ionize and generate electron-ion pairs. Under the influence of a uniform electric field, the electrons move towards the readout plane at a constant drift velocity. The readout plane employs a high spatial resolution microstructure gas detector (such as Micromegas or GEM) to accurately measure the position and time information of the electron cloud arrival. This working mechanism allows the TPC to acquire three-dimensional continuous sampling information of the track in a single detection without relying on a multi-layer detector structure. However, existing track reconstruction systems lack a complete and efficient algorithm for reconstructing muon three-dimensional tracks, especially for reconstructing muon events with small-angle incident events, which presents a challenge.

[0004] Traditional reconstruction algorithms are inefficient in processing electron clouds. Most algorithms obtain three-dimensional tracks by reconstructing two-dimensional plane tracks separately, without effectively utilizing the correlation information of the electron cloud's three-dimensional coordinates. Furthermore, the projection width of muon tracks at small incident angles on the readout plane is significantly compressed, and the longitudinal drift distances of the electron clouds are similar, making it difficult to extract effective positional and temporal information. Track features on both projection planes are weakened, easily leading to matching failures or the generation of false tracks. Summary of the Invention

[0005] The purpose of this application is to provide a three-dimensional track reconstruction method and device for muon imaging, which can solve the problems of low reconstruction efficiency and blurred reconstruction of small-angle incident muon events when traditional reconstruction algorithms process muon tracks detected by TPC.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for three-dimensional track reconstruction using muon imaging, comprising: The original waveform signal is acquired; the original waveform signal is acquired through a muon imaging system; the muon imaging system is a muon imaging system based on a time projection chamber detector; The original waveform signal is preprocessed to obtain the preprocessed original waveform signal; Waveform recognition technology is used to perform channel waveform analysis on the preprocessed original waveform signal to obtain the channel waveform analysis results. Based on the channel waveform analysis results and the detector-electronics channel mapping table, continuous clusters are decoded by channel clustering using the hit continuity characteristics, and continuous channel information is cached. Based on the continuous channel information, a spatial point clustering algorithm is used to classify spatially adjacent electronic signals in a continuous cluster as the same electronic cloud; the three-dimensional coordinates of the electronic signal are determined by the continuous channel information. The three-dimensional centroid coordinates of each electron cloud are obtained by charge weighting; Based on the three-dimensional centroid coordinates of multiple electron cloud clusters, a screening and reconstruction algorithm is used to complete the reconstruction of three-dimensional tracks.

[0007] Optionally, the original waveform signal is preprocessed to obtain a preprocessed original waveform signal, specifically including: The original waveform signal is subjected to baseline subtraction to obtain an intermediate processed signal; Digital filtering techniques are used to remove high-frequency noise from the intermediate processed signal to obtain the preprocessed original waveform signal.

[0008] Optionally, waveform recognition technology is used to perform channel waveform analysis on the preprocessed original waveform signal to obtain the channel waveform analysis results, specifically including: The local maximum method is used to detect multiple local peaks in the preprocessed original waveform signal; Based on the peak values ​​of the multiple local peaks, the peak-valley minimum point segmentation method is used to determine multiple independent sub-peak fitting windows; Extract the sub-peak signal corresponding to each independent sub-peak fitting window from the preprocessed original waveform signal; Each sub-peak signal is fitted using a preset function to obtain waveform information for multiple channels; the channels and sub-peak signals correspond one-to-one; the waveform information includes: the leading edge time and peak amplitude of the sub-peak signal; A multi-index joint screening method was used to screen the waveform information of multiple channels to obtain the channel waveform analysis results.

[0009] Optionally, the local maximum method is used to detect multiple local peaks in the preprocessed original waveform signal, specifically including: The local maximum method is used to detect peaks in the preprocessed original waveform signal. When a plateau peak feature is detected, the peak point is determined based on the center position. When a non-plateau peak feature is detected, parabolic interpolation and parabolic fitting are used to determine the peak value.

[0010] Optionally, based on the channel waveform analysis results and the detector-electronics channel mapping table, continuous clusters are decoded using the hit continuity characteristics of channel clustering, and continuous channel information is cached, specifically including: Based on the peak amplitude information of the channel, the channel is filtered for over-thresholding; Import the detector-electronics channel mapping table; Based on the detector-electronics channel mapping table, continuous clusters are decoded by channel clustering using the hit continuity characteristics, and continuous channel information is cached; the continuous channel information includes the channel position and time amplitude information of the continuous clusters.

[0011] Optionally, based on the continuous channel information, a spatial point clustering algorithm is used to classify spatially adjacent electronic signals in continuous clusters as the same electronic cloud, specifically including: The three-dimensional coordinates of the electron signal in the continuous cluster are determined using the continuous channel information; the two-dimensional planar coordinates in the three-dimensional coordinates are determined based on the channel position; the longitudinal depth coordinates in the three-dimensional coordinates are obtained based on electron drift time conversion. Based on the three-dimensional coordinates of electronic signals in continuous clusters, a spatial point clustering algorithm is used to classify spatially adjacent electronic signals in continuous clusters as the same electronic cloud. Physical constraints are introduced during the clustering process to obtain multiple electronic clouds. The coordinates and charge of all signal points in the cached electron cloud are stored.

[0012] Optionally, the three-dimensional centroid coordinates of the electron cloud are: ; in, Let be the three-dimensional centroid coordinates of the i-th electron cloud. Let be the charge at the j-th signal point in the i-th electron cloud; Let J be the coordinates of the j-th signal point within the i-th electron cloud. The number of signal points in the i-th electron cloud.

[0013] Optionally, based on the three-dimensional centroid coordinates of multiple electron cloud clusters, a filtering reconstruction algorithm is used to complete the three-dimensional track reconstruction, specifically including: The random sample consensus algorithm is used for iterative random sampling and consensus set evaluation. The set of interior points that conform to the linear model is selected from the 3D centroid point cloud, and noisy outliers in the interior point set are removed. The eigenvectors of the covariance matrix of the point cloud of the interior point set are calculated using the principal component analysis algorithm. The direction vector corresponding to the largest eigenvalue is determined as the trajectory direction; Based on the track direction, the mean center of the point cloud in the track direction is fitted to obtain the optimal spatial straight line equation; The optimal spatial straight line equation is determined as the result of three-dimensional track reconstruction.

[0014] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the three-dimensional track reconstruction method for muon imaging as described above.

[0015] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the three-dimensional track reconstruction method for muon imaging as described above.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and device for three-dimensional track reconstruction using muon imaging. First, high-precision waveform analysis technology is used to extract time and charge information to locate impact clusters. Then, three-dimensional clustering and charge weighting are used to obtain the three-dimensional spatial coordinates of the electron cloud. Finally, a track fitting method based on direct processing of the three-dimensional point cloud is used to screen candidate point clouds and perform straight-line fitting, thereby directly obtaining a high-precision three-dimensional straight track. This method accurately reconstructs the three-dimensional spatial information of the electron cloud by efficiently processing waveform information and performs optimal three-dimensional track reconstruction of the three-dimensional point cloud through a screening and reconstruction algorithm. It solves the problems of information loss and mismatch in the traditional two-dimensional projection reconstruction process, while improving the reconstruction efficiency of muon tracks at small angles of incidence. It achieves high-precision, high-efficiency, full-angle range three-dimensional muon track reconstruction, providing a core method for the development of compact muon detection devices. In the future, it is expected to be applied in complex industrial environments and geological exploration scenarios.

[0017] This application establishes a complete data processing workflow to efficiently process waveform information and reconstruct the three-dimensional spatial information of electron clouds. It also employs a filtering reconstruction algorithm to perform optimal three-dimensional track reconstruction of the three-dimensional point cloud. This improves the efficiency of reconstructing muon events at small angles, achieving high-precision, high-efficiency, and full-angle three-dimensional track reconstruction of muons. This will provide strong support for the development and application of muon imaging technology, and compared to existing technologies, it has significant beneficial effects, specifically in the following aspects: (i) Improving the accuracy and efficiency of track reconstruction: This application first extracts the time and amplitude information of the channel efficiently and accurately by using multi-peak judgment and waveform fitting, thereby improving the accuracy of information extraction; then, it uses channel amplitude over-threshold and hit channel continuity for screening and judgment, and uses algorithms such as sorting and clustering to find hit clusters for post-processing, eliminating the interference of isolated noise and improving the efficiency of case processing; based on the correlation between time information and two-dimensional position information, it uses spatial point clustering algorithm to group signals with adjacent three-dimensional coordinates into the same electron cloud; further, it uses charge weighted centering method to reconstruct the three-dimensional spatial coordinates of each electron cloud, which effectively suppresses the interference of noise points while improving coordinate accuracy; three-dimensional track fitting uses three-dimensional point cloud to perform spatial line fitting through screening and reconstruction algorithm, fully utilizing the three-dimensional geometric distribution information of electron cloud to achieve efficient and high-precision muon three-dimensional track reconstruction.

[0018] (II) Improved Reconstruction of Small-Angle Incident Muon Tracks: In the waveform extraction stage, multi-peak identification and fitting techniques are used to accurately analyze and extract multiple temporal information from complex waveforms in a single channel, thereby effectively capturing the signal overlap characteristics unique to small-angle tracks. Subsequently, spatial clustering algorithms are used to group signal points with three-dimensional spatial correlation into the same electron cloud set, and the precise three-dimensional centroid coordinates are calculated using the charge-weighted method, ensuring the accuracy of electron cloud positioning. Finally, direct spatial line fitting is performed on the three-dimensional point cloud, avoiding matching errors caused by information compression in traditional two-dimensional projection reconstruction, and achieving high-precision three-dimensional reconstruction of small-angle incident tracks.

[0019] (III) Complete and meticulous data processing workflow: From channel waveform information extraction to cluster detection, data quality is optimized layer by layer through multi-level signal processing until electron cloud clustering and accurate coordinate reconstruction in three-dimensional space are completed, ultimately achieving three-dimensional track fitting. The entire process is closely connected and logically closed-loop, which improves processing efficiency while ensuring the effective transmission and accurate utilization of data information, providing a systematic algorithmic foundation for high-quality muon imaging. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a three-dimensional track reconstruction method for muon imaging according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the principle of a three-dimensional track reconstruction method for muon imaging in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a TPC detector in one embodiment of this application; Figure 4 This is a diagram showing the reconstruction result of a three-dimensional track of a large-angle muon event reconstructed by the algorithm in one embodiment of this application. Figure 5 This is a diagram showing the reconstruction result of a three-dimensional track of a small-angle muon event reconstructed by the algorithm in one embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] In one exemplary embodiment, such as Figure 1 As shown, a three-dimensional track reconstruction method for muon imaging is provided, applied to a muon imaging system based on a time-projection-room (TPC) detector. The muon imaging system includes a TPC detector, an anode readout plate, and an electronic readout system for acquiring muon events. The detector is a TPC detector, characterized by high temporal and positional resolution, suitable for high-precision muon imaging. The electronic readout system includes a front-end amplification module, a waveform digitization module, and a data aggregation module, used to amplify, digitize, and aggregate the signal output from the TPC detector, providing it for further processing in waveform analysis and three-dimensional track reconstruction.

[0025] The method mainly includes channel waveform analysis (steps 102 and 103), cluster hit detection (step 104), and three-dimensional track reconstruction (steps 105 to 107). The muon imaging system is used to detect muon signals and record the original waveform signals. The channel waveform analysis uses waveform recognition technology to accurately extract the time and amplitude information of the readout channel. The cluster hit detection uses amplitude information to determine the validity of the channel and uses spatial continuity and sorting clustering algorithms to find the clusters. The three-dimensional track reconstruction is based on the channel coordinates and time amplitude information. The clustering algorithm is used to obtain the accurate three-dimensional coordinates of the electron cloud, and then the track reconstruction algorithm is used to reconstruct the three-dimensional track.

[0026] Methods for three-dimensional track reconstruction using muon imaging include: Step 101: Acquire the raw waveform signal. The raw waveform signal is acquired using a muon imaging system. The muon imaging system is a muon imaging system based on a time projection chamber detector.

[0027] Step 102: Preprocess the original waveform signal to obtain the preprocessed original waveform signal.

[0028] The original waveform signal is subjected to baseline subtraction to obtain an intermediate processed signal. Digital filtering techniques are then used to remove high-frequency noise from the intermediate processed signal, yielding the preprocessed original waveform signal.

[0029] Step 103: Use waveform recognition technology to perform channel waveform analysis on the preprocessed original waveform signal to obtain the channel waveform analysis results.

[0030] Multiple local peaks in the preprocessed original waveform signal are detected using the local maxima method. When a plateau peak is detected, the peak point is determined based on its center position. When a non-plateau peak is detected, parabolic interpolation and parabolic fitting are used to determine the peak value.

[0031] Based on the peak values ​​of multiple local peaks, a peak-valley minimum point segmentation method is used to determine multiple independent sub-peak fitting windows. The sub-peak signal corresponding to each independent sub-peak fitting window is extracted from the preprocessed original waveform signal. A preset function is used to fit each sub-peak signal, obtaining waveform information for multiple channels. There is a one-to-one correspondence between channels and sub-peak signals. The waveform information includes the leading edge time and peak amplitude of the sub-peak signal. A multi-index joint screening method is used to filter the waveform information of multiple channels, obtaining the channel waveform analysis results. The multi-index joint screening method uses chi-square value, degrees of freedom, Pearson correlation coefficient, and the number of points used for fitting as indicators.

[0032] The channel waveform analysis steps specifically include preprocessing, waveform peak identification, waveform time and amplitude extraction, and waveform information filtering and caching. The preprocessing stage involves baseline subtraction of the signal. Waveform peak identification first uses digital filtering to remove high-frequency noise, then utilizes local maxima to detect peak values. For detected plateau peaks, the center position is used to determine the peak point. Waveform leading-edge time extraction divides the entire data into independent sub-peaks based on the identified multiple local peaks. A specific fitting function is used to fit the data of each sub-peak to obtain information such as the leading-edge time and peak amplitude. Waveform information filtering and caching uses parameters obtained from the fitting process, such as goodness of fit, to filter and retain high-quality waveform information for post-processing.

[0033] Step 104: Based on the channel waveform analysis results and the detector-electronics channel mapping table, use the hit continuity characteristics to decode continuous clusters by channel clustering and cache continuous channel information.

[0034] Based on the peak amplitude information of the channels, over-threshold filtering is performed on the channels. A detector-electronics channel mapping table is imported. Based on the detector-electronics channel mapping table, continuous clusters are decoded using the hit continuity characteristic, and continuous channel information is cached. Continuous channel information includes the channel position and temporal amplitude information of the continuous clusters.

[0035] The cluster hit detection process specifically includes channel threshold screening, channel continuity judgment, and information caching. First, threshold screening is performed using amplitude information extracted from waveform analysis. Then, the spatial continuity of the muon hit detector channels is used for identification. Electronic channels are mapped to the corresponding hit channels of the detector, and the channel numbers are sorted and clustered to identify continuously hit clusters. Continuous channel information caching involves storing the hit channel information and temporal amplitude information of each cluster to facilitate subsequent 3D track reconstruction.

[0036] Step 105: Based on continuous channel information, a spatial point clustering algorithm is used to classify spatially adjacent electron signals in continuous clusters as the same electron cloud. The three-dimensional coordinates of the electron signals are determined using continuous channel information.

[0037] The three-dimensional coordinates of electron signals in continuous clusters are determined using continuous channel information. The two-dimensional planar coordinates in the three-dimensional coordinates are determined based on the channel positions. The longitudinal depth coordinates in the three-dimensional coordinates are obtained based on electron drift time transformation. Based on the three-dimensional coordinates of electron signals in continuous clusters, a spatial point clustering algorithm is used to group spatially adjacent electron signals in continuous clusters as the same electron cloud. Physical constraints are introduced during the clustering process to obtain multiple electron clouds. The coordinates and charge of all signal points in each electron cloud are cached.

[0038] Step 106: Obtain the three-dimensional centroid coordinates of each electron cloud by charge weighting.

[0039] The three-dimensional centroid coordinates of the electron cloud are: .

[0040] in, Let be the three-dimensional centroid coordinates of the i-th electron cloud. Let be the charge at the j-th signal point in the i-th electron cloud. Let be the coordinates of the j-th signal point in the i-th electron cloud. The number of signal points in the i-th electron cloud.

[0041] Step 107: Based on the three-dimensional centroid coordinates of multiple electron cloud clusters, a screening and reconstruction algorithm is used to complete the three-dimensional track reconstruction.

[0042] Iterative random sampling and consensus set evaluation are performed using a random sample consensus algorithm to select interior point sets that conform to a linear model from the 3D centroid point cloud, and noisy outliers in the interior point set are removed. Principal component analysis is used to calculate the eigenvectors of the covariance matrix of the interior point set's point cloud. The direction vector corresponding to the largest eigenvalue is determined as the track direction. Based on the track direction, the mean center of the point cloud along the track direction is fitted to obtain the optimal spatial linear equation. The optimal spatial linear equation is determined as the 3D track reconstruction result.

[0043] The three-dimensional track reconstruction steps specifically include spatial point clustering, charge-weighted calculation of the three-dimensional coordinates of electron clouds, and three-dimensional track fitting and reconstruction. Spatial point clustering uses time information to represent the longitudinal coordinates of electron clouds, and uses a clustering algorithm to group signals with adjacent three-dimensional spatial coordinates into a group as the signal set of the same electron cloud. In the charge-weighted calculation stage, the charge amount represented by peak amplitude information is used to calculate the three-dimensional centroid coordinates of each electron cloud using a charge-weighted method. The three-dimensional track fitting and reconstruction involves filtering the centroid coordinates of electron clouds and reconstructing the optimal three-dimensional track.

[0044] The following section uses the muon signal acquired by the TPC detector based on the Micromegas detector as an example to specifically illustrate the three-dimensional track reconstruction method for muon imaging provided in this application. The specific implementation results of the three-dimensional track reconstruction algorithm are as follows: Figures 2-5 As shown; the specific implementation of the 3D track reconstruction algorithm mainly includes the following steps: Muon Imaging System Based on Time Projection Chamber Detector: Detecting Muon Signals and Recording Raw Waveform Information.

[0045] Waveform preprocessing: Baseline subtraction is performed on the original waveform signal during the preprocessing stage.

[0046] Waveform peak identification: First, digital filtering techniques (such as wavelet denoising) are used to smooth and remove high-frequency noise; then, local maxima are used to detect peaks. For plateau peaks, the center position is used to determine the peak point. For non-plateau peaks, parabolic interpolation and fitting are used to refine the peak location.

[0047] Waveform fitting: Based on the identified local peaks, the peak-valley minimum point segmentation method is used to determine the fitting window for independent sub-peaks; predefined functions (such as the Fermi-Dirac distribution function, Gaussian function, etc.) are used for fitting to accurately extract information such as the leading edge time and peak amplitude; finally, multiple indicators are used for joint screening (such as goodness of fit). Fitting error It preserves high-quality waveform information for post-processing.

[0048] Hit cluster finding: First, the channels are filtered for overthreshold using the channel amplitude obtained by waveform fitting; then, the detector-electronics channel mapping table is imported and the continuous hit characteristics are used to decode the continuous clusters by channel clustering, and the cluster channel position and time amplitude information are cached; this step eliminates the influence of isolated noise and greatly compresses the amount of data.

[0049] Spatial point clustering: Each valid hit signal contains three dimensions of information: two-dimensional planar coordinates (X, Y) determined by the readout channel, and longitudinal depth coordinates Z obtained from electron drift time conversion. A density-based clustering algorithm (such as DBSCAN) is used to group spatially adjacent signal points into a single electron cloud. Physical constraints (such as estimated sizes calculated based on the lateral and longitudinal diffusion of the electron cloud) are introduced during the clustering process for optimization. Finally, the coordinates and charges of all signal points within the electron cloud are cached to provide a basis for subsequent calculations of the centroid.

[0050] Charge-weighted calculation: The peak amplitude of the signal (or the integral charge of the waveform) is proportional to the number of primary ionized electrons; that is, the larger the amplitude, the higher the ionization density at that point, and the closer it is to the true center of the electron cloud. Three-dimensional barycentric coordinates of the electron cloud. Calculate using the following formula: .

[0051] charge As a weight for each signal point location, points with higher ionization density (larger charge) contribute more to the final centroid location, thus making the calculated centroid closer to the true centroid of electron distribution in the electron cloud.

[0052] 3D Track Fitting: In the 3D track fitting and reconstruction stage, a two-stage strategy is employed, utilizing screening algorithms (such as RANSAC (Random Sample Consensus Algorithm) screening) and optimal fitting (PCA (Principal Component Analysis) optimal fitting) to achieve efficient and high-precision 3D track reconstruction. For example, firstly, the RANSAC algorithm is used to screen inliers conforming to a straight line model from the charge-weighted 3D centroid point cloud through iterative random sampling and consensus set evaluation, while excluding noise outliers. This process can be optimized by setting parameters such as consensus set threshold distance, minimum inlier ratio, and minimum track penetration distance. Subsequently, PCA is applied to the screened inlier set. By calculating the eigenvectors of the point cloud covariance matrix, the direction vector corresponding to the largest eigenvalue is determined as the track direction, and the optimal spatial straight line equation is obtained by combining it with the point cloud mean center. This method fully leverages the noise screening capability of RANSAC and the statistical optimality of PCA, significantly improving the robustness and accuracy of track reconstruction while fully utilizing 3D geometric information. It is particularly suitable for complex reconstruction scenarios with background interference or small-angle tracks.

[0053] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection.

[0054] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0059] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for three-dimensional track reconstruction using muon imaging, characterized in that, include: The original waveform signal is acquired; the original waveform signal is acquired through a muon imaging system; the muon imaging system is a muon imaging system based on a time projection chamber detector; The original waveform signal is preprocessed to obtain the preprocessed original waveform signal; Waveform recognition technology is used to perform channel waveform analysis on the preprocessed original waveform signal to obtain the channel waveform analysis results. Based on the channel waveform analysis results and the detector-electronics channel mapping table, continuous clusters are decoded by channel clustering using the hit continuity characteristics, and continuous channel information is cached. Based on the continuous channel information, a spatial point clustering algorithm is used to classify spatially adjacent electronic signals in a continuous cluster as the same electronic cloud; the three-dimensional coordinates of the electronic signal are determined by the continuous channel information. The three-dimensional centroid coordinates of each electron cloud are obtained by charge weighting; Based on the three-dimensional centroid coordinates of multiple electron cloud clusters, a screening and reconstruction algorithm is used to complete the reconstruction of three-dimensional tracks.

2. The three-dimensional track reconstruction method for muon imaging according to claim 1, characterized in that, The original waveform signal is preprocessed to obtain a preprocessed original waveform signal, specifically including: The original waveform signal is subjected to baseline subtraction to obtain an intermediate processed signal; Digital filtering techniques are used to remove high-frequency noise from the intermediate processed signal to obtain the preprocessed original waveform signal.

3. The three-dimensional track reconstruction method for muon imaging according to claim 1, characterized in that, Waveform recognition technology is used to perform channel waveform analysis on the preprocessed raw waveform signal to obtain the channel waveform analysis results, which specifically include: The local maximum method is used to detect multiple local peaks in the preprocessed original waveform signal; Based on the peak values ​​of the multiple local peaks, the peak-valley minimum point segmentation method is used to determine multiple independent sub-peak fitting windows; Extract the sub-peak signal corresponding to each independent sub-peak fitting window from the preprocessed original waveform signal; Each sub-peak signal is fitted using a preset function to obtain waveform information for multiple channels; the channels and sub-peak signals correspond one-to-one; the waveform information includes: the leading edge time and peak amplitude of the sub-peak signal; A multi-index joint screening method was used to screen the waveform information of multiple channels to obtain the channel waveform analysis results.

4. The three-dimensional track reconstruction method for muon imaging according to claim 3, characterized in that, The local maximum method is used to detect multiple local peaks in the preprocessed original waveform signal, specifically including: The local maximum method is used to detect peaks in the preprocessed original waveform signal. When a plateau peak feature is detected, the peak point is determined based on the center position. When a non-plateau peak feature is detected, parabolic interpolation and parabolic fitting are used to determine the peak value.

5. The three-dimensional track reconstruction method for muon imaging according to claim 3, characterized in that, Based on the channel waveform analysis results and the detector-electronics channel mapping table, continuous clusters are decoded using the hit continuity characteristics of channel clustering, and continuous channel information is cached, specifically including: Based on the peak amplitude information of the channel, the channel is filtered for over-thresholding; Import the detector-electronics channel mapping table; Based on the detector-electronics channel mapping table, continuous clusters are decoded by channel clustering using the hit continuity characteristics, and continuous channel information is cached; the continuous channel information includes the channel position and time amplitude information of the continuous clusters.

6. The three-dimensional track reconstruction method for muon imaging according to claim 3, characterized in that, Based on the continuous channel information, a spatial point clustering algorithm is used to classify spatially adjacent electron signals in continuous clusters as the same electron cloud, specifically including: The three-dimensional coordinates of the electron signal in the continuous cluster are determined using the continuous channel information; the two-dimensional planar coordinates in the three-dimensional coordinates are determined based on the channel position; the longitudinal depth coordinates in the three-dimensional coordinates are obtained based on electron drift time conversion. Based on the three-dimensional coordinates of electronic signals in continuous clusters, a spatial point clustering algorithm is used to classify spatially adjacent electronic signals in continuous clusters as the same electronic cloud. Physical constraints are introduced during the clustering process to obtain multiple electronic clouds. The coordinates and charge of all signal points in the cached electron cloud are stored.

7. The three-dimensional track reconstruction method for muon imaging according to claim 6, characterized in that, The three-dimensional centroid coordinates of the electron cloud are: ； in, Let be the three-dimensional centroid coordinates of the i-th electron cloud. Let be the charge at the j-th signal point in the i-th electron cloud; Let J be the coordinates of the j-th signal point within the i-th electron cloud. The number of signal points in the i-th electron cloud.

8. The three-dimensional track reconstruction method for muon imaging according to claim 6, characterized in that, Based on the three-dimensional centroid coordinates of multiple electron cloud clusters, a filtering and reconstruction algorithm is used to reconstruct three-dimensional tracks, specifically including: The random sample consensus algorithm is used for iterative random sampling and consensus set evaluation. The set of interior points that conform to the linear model is selected from the 3D centroid point cloud, and noisy outliers in the interior point set are removed. The eigenvectors of the covariance matrix of the point cloud of the interior point set are calculated using the principal component analysis algorithm. The direction vector corresponding to the largest eigenvalue is determined as the trajectory direction; Based on the track direction, the mean center of the point cloud in the track direction is fitted to obtain the optimal spatial straight line equation; The optimal spatial straight line equation is determined as the result of three-dimensional track reconstruction.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the three-dimensional track reconstruction method for muon imaging according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the three-dimensional track reconstruction method for muon imaging as described in any one of claims 1-8.

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