Particle trajectory tracking method, device and equipment based on Retina algorithm and medium

Abstract

The invention relates to a particle trajectory tracking method based on a Retina (retina) algorithm, and the method comprises the steps: building a retina parameter space, dividing the built retina parameter space into # imgabs0 # discrete subspaces, taking each discrete subspace as a retina calculation unit, forming a retina calculation array by # imgabs1 # retina calculation units, and carrying out the retina calculation array; inputting the sample data into a retina calculation unit to obtain a similarity weight and a weight value of each element in the sample data in the retina calculation unit, and obtaining weight value distribution of input samples in a retina parameter space according to the weight values of all the retina calculation units, establishing a tracking trajectory according to the weight value distribution and the discrete subspace of the retina calculation unit corresponding to the maximum weight value; rapid positioning and reconstruction of a high-energy particle trajectory in a specified space range are realized, and a solution is provided for radiation positioning in a space and particle trajectory imaging in a high-energy physical experiment.

Description

Technical Field

[0001] The present invention relates to the field of tracing, and in particular to a particle trajectory tracking method based on a Retina algorithm. Background Art

[0002] As the collision energy in high-energy physics experiments becomes higher and higher, the data traffic generated by the experiments increases exponentially. The demand for real-time online trajectory tracking in high-energy physics experiments is increasing. It can reduce the data transmission bandwidth of DAQ (Data Acquisition System) while extracting the characteristic track information of particles of interest in a targeted and rapid manner.

[0003] The research on real-time tracking algorithms started very early. In the LHCb physics detection experiment, Günther et al. solved the key parts of "forward tracking", such as using SIMD instructions in Hough transform and cluster search, and realized the reconstruction of partial events, track reconstruction, tracking efficiency, false track rate and high throughput. Hennequin, A. et al. proposed a parallel reconstruction algorithm for the vertex detector in the LHCb experiment. The efficiency and accuracy of particle straight line trajectory tracking need to be improved. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a particle trajectory tracking method based on the Retina algorithm, which can realize the rapid positioning and reconstruction of high-energy particle trajectories within a specified spatial range, and provide a solution for radiation positioning in space and particle trajectory imaging in high-energy physics experiments.

[0005] According to a first aspect of the present invention, a particle trajectory tracking method based on a Retina algorithm is provided, comprising: Step 1, divide the two-dimensional space into various spatial units according to the set granularity, and use the spatial units as cell units in the retinal algorithm to establish a cell array; Step 2, using detector detection to obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; Step 3, based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each cell unit in turn, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

[0006] According to a first aspect of the present invention, a particle trajectory tracking method based on a Retina algorithm is provided, comprising: Step 1', dividing the two-dimensional space into various spatial units according to the set granularity, and using the spatial units as super retinal units in the retinal algorithm to establish a cell array; Step 2', using detectors to detect and obtain the coordinate position information of the pixel points when multiple particles pass through each layer of detector plane; Step 3', based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each super retinal unit in turn, sum the similarity weights of a super retinal unit and each coordinate position information of the particle to obtain the similarity weight value of the super retinal unit, and screen out the super retinal unit whose similarity weight value exceeds the set threshold sum_th1; Step 4', divide each super retinal unit obtained by screening into multiple cell units, calculate the similarity weight value of the cell unit, and screen the cell units whose similarity weight value exceeds the set threshold sum_th2; determine the linear trajectory of the particle based on the position and output it.

[0007] According to a third aspect of the present invention, there is provided a particle trajectory tracking device based on a Retina algorithm, comprising: Cell array building unit, detector and trajectory output unit; The cell array establishment unit is used to divide the two-dimensional space into various space units according to the set granularity, and establish the cell array using the space units as the cell units in the retina algorithm; The detector is used to detect and obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; The trajectory output unit is used to calculate the similarity weights of each coordinate position information of the particle and each cell unit in sequence based on the longitudinal distance, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

[0008] According to a fourth aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the processor is used to implement the steps of a particle trajectory tracking method based on a Retina algorithm when executing a computer management program stored in the memory.

[0009] According to a fifth aspect of the present invention, there is provided a computer-readable storage medium on which a computer management program is stored, and when the computer management program is executed by a processor, the steps of a particle trajectory tracking method based on the Retina algorithm are implemented.

[0010] The present invention provides a particle trajectory tracking method, device, electronic device and storage medium based on the Retina algorithm, proposes a retina algorithm and an upgraded iterative retina algorithm, tracks single-track and multi-track feature information in a single-case sample, and conducts position resolution, efficiency and purity test experiments. Finally, the resolution of single-track tracking reaches within 40um, which is less than 50% of the single pixel size (83um). The position resolution of multi-track tracking is equivalent to that of single-track tracking, and the tracking efficiency and purity are higher than 97%. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of a particle trajectory tracking method based on the Retina algorithm provided by the present invention; Figure 2 A schematic diagram of an embodiment of tracing a straight line in a two-dimensional plane in the Retina algorithm provided by the present invention; Figure 3 A schematic diagram of retinal weight calculation and weight value distribution in the Retina algorithm provided by the present invention; Figure 4 A flowchart of the iterative retinal algorithm for multi-particle tracking provided by an embodiment of the present invention; Figure 5 A two-dimensional graph formed by multiple tracks provided by an embodiment of the present invention; Figure 6 Schematic diagram of iterative retinal algorithm provided by an embodiment of the present invention; Figure 7 A flowchart of the number statistics of perfectly matched tracks, over-matched tracks and unmatched tracks in the iterative retinal algorithm provided in an embodiment of the present invention; Figure 8 A flowchart of the number of false tracks in the iterative retinal algorithm provided by an embodiment of the present invention; Fig. 9 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Fig.10 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0012] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0013] Example 1 Embodiment 1 provided by the present invention is an embodiment of a particle trajectory tracking method based on the Retina algorithm provided by the present invention. In the embodiment of the present invention, single particle trajectory tracking is performed on a particle single trajectory event sample. Figure 1A flow chart of a particle trajectory tracking method based on the Retina algorithm provided in an embodiment of the present invention is as follows: Figure 1 As shown, the embodiment of the invention includes: Step 1: Divide the two-dimensional space into various spatial units according to the set granularity, and use the spatial units as cell units in the retinal algorithm to establish a cell array.

[0014] The retina algorithm is inspired by the recognition characteristics of biological retina and is a pattern recognition algorithm invented by imitating the biological visual system. The present invention uses the retina algorithm to recognize straight line patterns on a two-dimensional plane. First, the two-dimensional space is divided into several small space units according to a pre-set granularity. Each small space unit is called a cell unit (Retina cell) of the algorithm, and the corresponding two-dimensional parameters are (i, j).

[0015] The specific division process includes: setting the boundary conditions of the cell array in the horizontal and vertical directions in turn, that is, the value range of the cell array in the horizontal and vertical axes, dividing it into i*j small square Retina cells along the horizontal and vertical axes, that is, dividing the array into i rows and j columns of cell computing units, and the total number of cell units is the granularity set by the algorithm. The horizontal dimension can be expressed as :{ , ,... }, the vertical dimension can be expressed as :{ , ,..., }.

[0016] Step 2: Use detector detection to obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane.

[0017] When performing single-particle tracking, high-energy heavy ion Kr (average energy 25MeV / µ) rays can be used in simulation experiments to pass through a gas chamber composed of Ar and CO2 (Ar: CO2=70:30). When charged particles pass through the gas detection, they interact electromagnetically with the electrons in the gas atoms, causing the gas medium to be ionized and excited. The energy lost in the interaction is converted into low-energy photons and electron-ion pairs. These electron-ion pairs form a straight trajectory along the direction of particle flight, and fall vertically on the surface of the pixel detector chip Topmetal under the condition of an external electric field. After collecting electrons, the Topmetal chip generates a straight trajectory sample in the effective two-dimensional area.

[0018] In one possible implementation, an electric field (electric field strength of 300 V / cm) perpendicular to the plane of the pixel detector is applied in the gas chamber to allow electrons to drift to the pixel detector. Finally, the Topmetal chip collects these charges to form a linear particle trajectory in the detection area as an input sample for the algorithm.

[0019] Step 3, based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each cell unit in turn, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

[0020] In the specific implementation process, the case sample information is input into the Retina computing array in sequence, and the similarity weight calculation is performed on a single case sample and each Retina cell. From the calculation process of the Retina algorithm, it can be seen that the calculation of the similarity weight of each computing unit in the Retina computing array can be performed simultaneously, which makes the algorithm calculation process have ultra-high parallelism and short calculation delay, and has the potential to be used as a real-time tracking algorithm.

[0021] In a possible embodiment, the formula (1) for obtaining the similarity weight value of the cell unit based on the longitudinal distance is:

[0022] Where i and j represent the horizontal and vertical serial numbers of the cell unit / super retinal unit, respectively; m and k represent the total number and serial number of the layers of the parallel plane of the detector, respectively; is the similarity weight value of the coordinate position information of the particle detected by the cell unit / super retinal unit in the i-th row and j-th column and the k-th layer detector; The coordinate position information of the particle detected by the k-th layer detector and the distance between the cell unit / super retinal unit in the i-th row and j-th column in the longitudinal direction; It is the adjustment factor of the operation, and its size is determined by the granularity of the retina.

[0023] In a possible embodiment, the process of determining the straight trajectory of the particle based on the position and outputting it includes: Determine the horizontal and vertical serial numbers i and j of the cell unit / super retinal unit, and use the slope and intercept of the cell unit / super retinal unit in the i-th row and j-th column as the slope and intercept of the particle linear track.

[0024] like Figure 2 and Figure 3The following are schematic diagrams of the tracing of a straight line in a two-dimensional plane and the calculation of retinal weights and the distribution of weight values ​​in the Retina algorithm provided by the embodiments of the present invention. A straight line in a two-dimensional space is represented as y=kx+b in a rectangular coordinate system. The two-dimensional parameter space of the Retina algorithm is defined as (k, b), where the horizontal axis represents the slope k of the Retina prediction line, and the value range is limited to -1 to 1; the vertical axis represents the vertical intercept b of the prediction line, and the value range is set to -1 to 1.

[0025] Figure 2 In the given embodiment, the scanning granularity of the Retina algorithm is set to i*j=4*4=16. The granularity of the Retina cell calculation unit in the entire parameter space is set to ~ ,in Represents the Retina cell computing unit at row i and column j.

[0026] The corresponding straight line Lij is defined as the Retina pre-matching straight line, and its corresponding straight line expression is y=jx+i. Figure 2 The left side shows the "slope-intercept" two-dimensional parameter space. The lines in the "xy" two-dimensional physical space corresponding to cell23 and cell41 in the two-dimensional parameter space are Figure 2 L23 and L41 shown on the right.

[0027] The actual sample straight line trajectory is Figure 3 The left side shows the coordinates of the straight line track information composed of gray points. The vertical distance between the coordinate point and the Retina prediction line L23 is , Indicates. Formula (1) can be used to calculate the similarity weight sum23 of the sample calculated in cell23. In the same way, the similarity weight sum of the sample in each cell unit in the cell array can be calculated, thereby obtaining the weight and distribution of the sample in the Retina cell array. ~ like Figure 3 Shown right. Compare the sizes and select the maximum value The position coordinates (i, j) in the parameter space are the slope and intercept parameter results of the particle straight line trajectory identified and reconstructed by the algorithm. So far, the Retina algorithm has completed the tracking and trajectory reconstruction of a straight line trajectory.

[0028] In the simulation verification, the number of straight line samples generated each time is set to 2000, and the range of parameters k and b is set to 0 to 10. The granularity of the Retina algorithm is set to 4 cases from high to low: 10*10, 20*20, 49*49 and 99*99. Under the four different algorithm granularities, the results of the slope resolution and longitudinal intercept resolution of the straight line trajectory identified by the algorithm are shown in Table 1.

[0029] Table 1

[0030] The resolution of the slope and intercept reconstructed by the retina algorithm in the experimental results is given in the form of standard deviation. The data in Table 1 show that the accuracy of the algorithm tracking increases with the granularity of the Retina array. When the algorithm granularity increases to 99*99, the slope resolution and intercept resolution reach 0.03 and 0.14 respectively, which are quite high, verifying the feasibility of the Retina algorithm for tracking straight line feature information.

[0031] The present invention provides a particle trajectory tracking method based on the Retina algorithm, which realizes the rapid positioning and reconstruction of high-energy particle trajectories within a specified spatial range through algorithm research combined with conventional pixel detector chips, and provides a solution for radiation positioning in space and particle trajectory imaging in high-energy physics experiments.

[0032] Example 2 Embodiment 2 provided by the present invention is an embodiment of a particle trajectory tracking method based on the Retina algorithm provided by the present invention. In the embodiment of the present invention, the IR (Iterative-Retina) algorithm is used to perform multi-particle trajectory tracking for particle multi-path event samples, such as Figure 4 The flowchart of the iterative retinal algorithm for multi-particle tracking provided by the embodiment of the present invention is shown in FIG. Figure 1 and Figure 4 It can be seen that the embodiment of the trajectory tracking method includes: Step 1', divide the two-dimensional space into various spatial units according to the set granularity, and use the spatial units as super retinal units in the retinal algorithm to establish a cell array.

[0033] Step 2', using detectors to detect and obtain the coordinate position information of the pixel points when multiple particles pass through each layer of the detector plane.

[0034] When performing multi-particle track tracing, multi-track samples are used as input to the IR algorithm. Under the same conditions and environment as the single-particle sample generation process, multiple kr heavy ion rays are input during the generation of a single sample event. In this way, Topmetal can obtain samples formed by multiple straight track formations each time. Figure 5As shown, the sample information is used as a multi-particle track sample for algorithm tracking input. During the simulation process, the number of incident Kr heavy ions is set to 3 to 7.

[0035] Step 3', based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each super retinal unit in turn, sum the similarity weights of a super retinal unit and each coordinate position information of the particle to obtain the similarity weight value of the super retinal unit, and screen out the super retinal units whose similarity weight values ​​exceed the set threshold sum_th1.

[0036] Step 4', divide each super retinal unit obtained by screening into multiple cell units, calculate the similarity weight value of the cell unit, and screen the cell units whose similarity weight value exceeds the set threshold sum_th2; determine the linear trajectory of the particle based on the position and output it.

[0037] The working principle of IR algorithm is as follows Figure 6 As shown, combined Figure 6 It can be seen that the IR algorithm calculates the retinal algorithm through multiple iterations of the target parameter space, thereby finding the maximum value in multiple regions as the target trajectory to be identified.

[0038] In the embodiment of the present invention, the IR algorithm of the second iteration is taken as an example. In the first retinal calculation, the granularity is set to N*N, and the entire parameter space is divided into i*j super retinal units (super cells). In the second retinal calculation, the granularity is set to M*M, and the parameter space contained in each selected super cell is further divided into N*M cell units (cells). The setting rules of the pixel threshold and the distance threshold in the two retinal calculations are the same as those of the retina algorithm in Example 1. The screening thresholds sum_th1 and sum_th2 that need to be set twice more are used to screen out the Retinacell units where the maximum values ​​are located to complete the tracking task.

[0039] Through the first retinal algorithm scan calculation, the sum value of the sample in each super cell can be obtained. The super cell unit whose sum value exceeds the threshold sum_th1 is selected (such as Figure 6 The yellow square in the figure represents the super cell) and enters the second retina algorithm for iterative calculation.

[0040] In the second iteration, find the cell with the maximum sum in each Super cell and determine whether the sum value of the cell exceeds the set threshold sum_th2. If it exceeds the threshold sum_th2, a pattern is successfully identified in the cell space, such as Figure 6 The cell area where the green square is located. If it does not exceed the threshold sum_th2, no valid pattern is matched in the super cell.

[0041] Compared with the retina algorithm, the IR algorithm can reduce the amount of calculation of the entire algorithm under the same scanning granularity. Assuming that the IR algorithm selects H super cells in the first retinal calculation, the number of retinal calculation units that need to be traversed by the entire algorithm to complete the calculation is N*N+H*M*M. The number of retinal calculation units that need to be traversed by the retina algorithm to achieve the same scanning granularity is N. 2 *M 2 The computational complexity of the IR algorithm is (1 / M 2 +H / N 2 ) times, a significant decrease.

[0042] In a possible embodiment, the formula for obtaining the similarity weight value of the cell unit / super retinal unit based on the longitudinal distance is:

[0043] Where i and j represent the horizontal and vertical serial numbers of the cell unit / super retinal unit, respectively; m and k represent the total number and serial number of the layers of the parallel plane of the detector, respectively; is the similarity weight value of the coordinate position information of the particle detected by the cell unit / super retinal unit in the i-th row and j-th column and the k-th layer detector; The coordinate position information of the particle detected by the k-th layer detector and the distance between the cell unit / super retinal unit in the i-th row and j-th column in the longitudinal direction; It is the adjustment factor of the operation, and its size is determined by the granularity of the retina.

[0044] In a possible embodiment, the process of determining the straight trajectory of the particle based on the position and outputting it includes: Determine the horizontal and vertical serial numbers i and j of the cell unit / super retinal unit, and use i and j as the slope and intercept of the particle linear trajectory.

[0045] For a multi-path tracking algorithm, algorithm efficiency and purity are two important indicators to measure the quality of the algorithm, which reflect whether the algorithm can efficiently identify as many straight line tracks as possible in all samples. In a possible implementation manner, the track tracking method also includes: The detector is used to obtain multiple trajectory sample information, where the trajectory sample information is the coordinate position information of the pixel points when multiple particles pass through each layer of the detector plane.

[0046] When the IR algorithm is used to track N multi-trajectory samples and M straight line trajectories are found, in the result analysis, the M trajectories found need to be classified one by one according to the defined matching mode. The classification of matching mode includes: When there is only one straight line trajectory that is the same as a trajectory in the standard library, it is determined to be a perfect matching trajectory; When there are at least two straight line trajectories that are identical to a trajectory in the standard library, it is considered an over-perfect matching trajectory; When any track in the standard track library is different from any straight track, it is determined to be an unmatched track; If the straight track is different from any track in the standard track library, it is determined to be a false track.

[0047] like Figure 7 FIG. 1 is a flowchart of the number of perfectly matched tracks, over-matched tracks and unmatched tracks in the iterative retinal algorithm provided by an embodiment of the present invention. Figure 7 It can be seen that the original existing trajectory in the sample is used as input, and the similarity matching is performed in turn with all the trajectories found by the IR algorithm. Each time a match is successful, the matching count F increases by one. The judgment rule for similarity matching is to count the number of identical coordinate points Num_s in the original trajectory and the trajectory identified by the IR algorithm. When this number exceeds a threshold Num_th, the match is successful, otherwise the match fails. Finally, after the statistics of the original trajectories in all samples are input, the straight line trajectory with a matching number F equal to 1 is a perfectly matched trajectory. The straight line trajectory with a matching number F greater than 1 is an over-matched trajectory, and the straight line trajectory with a matching number F equal to 0 is an unmatched trajectory.

[0048] In a possible embodiment, Figure 8 FIG. 1 is a flowchart showing the number of false tracks in the iterative retinal algorithm provided by an embodiment of the present invention. Figure 8 It can be seen that in the process of counting the number of false tracks, the track found by the IR algorithm is used as input, and the similarity is matched with all the tracks in the sample in turn. If there is no match, the number of false tracks N_fake is counted plus 1. Finally, after inputting all the tracks reconstructed by the IR algorithm, the number of false track patterns can be obtained.

[0049] The efficiency of the trajectory tracking method is calculated based on the number of four matching trajectories. and purity , and its calculation formula is: ; .

[0050] Among them, N_repeat, N_miss, N_true and N_fake represent the number of perfectly matched tracks, over-matched tracks, unmatched tracks and false tracks respectively. Represents the total number of straight line trajectories that originally existed in the sample.

[0051] The embodiment of the present invention designs two forms of algorithms around the retinal algorithm to track single-track samples and multi-track samples respectively, namely the original retinal algorithm and the iterative retinal algorithm. Through repeated experiments, the appropriate algorithm granularity and other algorithm parameters are set to evaluate the algorithm parameter reconstruction accuracy and tracking efficiency of the experimental samples, so as to evaluate the performance of the algorithm.

[0052] The original retina algorithm position resolution is within 40μm, which is less than 50% of the size of a single pixel (83um*83um). The iterative retina algorithm can achieve tracking efficiency and purity of more than 97% when the position resolution and slope resolution are equivalent to the Retina algorithm, achieving efficient and accurate particle straight line trajectory tracking. The final results confirm that the retina and iterative retina algorithms can complete the task of tracking the straight line particle trajectory of the Topmetal pixel detector chip.

[0053] The embodiment of the present invention proposes a particle trajectory tracking method based on the Retina algorithm, which performs fast and real-time particle trajectory tracking on the straight particle trajectory detected by the pixel detector in a two-dimensional plane space. In the specific implementation process: 1. Design a particle trajectory tracking algorithm based on the retina algorithm, and use the algorithm to realize the tracking of a single kr ion straight track in a two-dimensional space covered by a Topmetal chip with a size of 72*72 pixel array, and the position resolution is within 40um, which is less than 50% of the size of a single pixel (83um*83um); 2. Continue to expand on the basis of the retina algorithm and upgrade it to an iterative retina algorithm. Through iterative retina calculation, it realizes the tracking of multiple particle trajectories in a single event at the same time. When the position resolution result is equivalent to the single particle trajectory tracking, the tracking efficiency and purity of the algorithm are better than 97%. In addition, compared with the previous algorithm, this algorithm has very high parallelism and occupies less resources, and is easier to implement real-time, online trajectory tracking applications on board-level field programmable gate arrays FPGA.

[0054] Example 3 Embodiment 3 provided by the present invention is an embodiment of determining the parameters of the retina algorithm in a particle trajectory tracking method based on the Retina algorithm provided by the present invention, and the two-dimensional parameter space of the retina algorithm is (k, b). Wherein k represents the slope of the straight line, and the value range is set to -1~1. And b represents the vertical intercept of the straight line, and the value range is set to 0~72 pixel size.

[0055] In order to improve the calculation accuracy of the retinal algorithm in this experimental environment, three new influencing factors are added to the link of calculating the weight value W in the retinal algorithm calculation unit: pixel value, pixel threshold and distance threshold.

[0056] In a possible embodiment, the pixel value An of each input sample coordinate point n is added as an influencing factor to the similarity weight calculation formula (1). The calculation formula becomes as shown in formula (2):

[0057] Using formula (2) can make the coordinate point pair samples with high brightness (pixel value) in the trajectory sample have a higher matching weight in the predicted line L represented by the corresponding visual cell. This can improve the tracking accuracy of the retinal algorithm.

[0058] In a possible embodiment, based on the similarity weight calculation formula (2), a distance threshold d_th and a pixel threshold A_th are further added. The calculation formula becomes as shown in formula (3):

[0059] Since a straight line track sample contains a large number of valid coordinate points, formula (3) sets two thresholds d_th and A_th to allow the algorithm to ignore the contribution of coordinate positions that are far away from the predicted straight line L (dij_n>d_th) or have low pixel values ​​An to the weight value W, thereby reducing the amount of calculation of the algorithm while improving the algorithm's calculation efficiency and tracking accuracy.

[0060] According to the generated heavy ion trajectory samples, the pixel threshold value A_th is set to 10 brightness, and the distance threshold value d_th is set to 1.5 times the size of a single retinal computing unit on the vertical intercept parameter axis, that is, 1.5*83*72 / J um.

[0061] Example 4 Embodiment 4 provided by the present invention is an embodiment of a particle trajectory tracking device based on the Retina algorithm provided by the present invention. The embodiment of the trajectory tracking device includes: a cell array establishment unit, a detector, and a trajectory output unit.

[0062] The cell array establishment unit is used to divide the two-dimensional space into various space units according to the set granularity, and establish the cell array using the space units as the cell units in the retina algorithm.

[0063] The detector is used to detect and obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane.

[0064] The trajectory output unit is used to calculate the similarity weights of each coordinate position information of the particle and each cell unit in sequence based on the longitudinal distance, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

[0065] It can be understood that the particle trajectory tracking device based on the Retina algorithm provided by the present invention corresponds to the particle trajectory tracking method based on the Retina algorithm provided in the aforementioned embodiments. The relevant technical features of the particle trajectory tracking device based on the Retina algorithm can refer to the relevant technical features of the particle trajectory tracking method based on the Retina algorithm, which will not be repeated here.

[0066] See also Fig. 9 , Fig. 9 Schematic diagram of an electronic device provided by an embodiment of the present invention. Fig. 9 As shown, an embodiment of the present invention provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1311, the following steps are implemented: dividing the two-dimensional space into various spatial units according to the set granularity, and establishing a cell array with the spatial units as the cell units in the retinal algorithm; using a detector to detect and obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; calculating the similarity weights of each coordinate position information of the particle and each cell unit in turn based on the longitudinal distance, summing the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traversing all the cell units in the cell array, determining the position of the cell unit corresponding to the largest similarity weight value, and determining the straight line trajectory of the particle based on the position and outputting it.

[0067] See also Fig.10 , Fig.10 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Fig.10As shown, this embodiment provides a computer-readable storage medium 1400, on which a computer program 1411 is stored. When the computer program 1411 is executed by a processor, the following steps are implemented: dividing the two-dimensional space into various spatial units according to a set granularity, and establishing a cell array with the spatial units as the cell units in the retinal algorithm; using a detector to detect and obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; calculating the similarity weights of each coordinate position information of the particle and each cell unit in turn based on the longitudinal distance, summing the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traversing all the cell units in the cell array, determining the position of the cell unit corresponding to the largest similarity weight value, and determining the straight line trajectory of the particle based on the position and outputting it.

[0068] The embodiments of the present invention provide a particle trajectory tracking method, device, electronic device and storage medium based on the Retina algorithm, propose a retina algorithm and an upgraded iterative retina algorithm, track single-track and multi-track feature information in a single-case sample, and conduct position resolution, efficiency and purity test experiments. Finally, the resolution of single-track tracking reaches within 40um, which is less than 50% of the size of a single pixel (83um). The position resolution of multi-track tracking is equivalent to that of single-track tracking, and the tracking efficiency and purity are higher than 97%.

[0069] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0070] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0072] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0074] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A particle trajectory tracking method based on Retina algorithm, characterized in that: The trajectory tracking method comprises: Step 1, divide the two-dimensional space into various spatial units according to the set granularity, and use the spatial units as cell units in the retina algorithm to establish a cell array; Step 2, using detector detection to obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; Step 3, based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each cell unit in turn, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

2. A particle trajectory tracking method based on Retina algorithm, characterized in that: The tracing method comprises: Step 1', dividing the two-dimensional space into various spatial units according to the set granularity, and using the spatial units as super retinal units in the retinal algorithm to establish a cell array; Step 2', using detectors to detect and obtain the coordinate position information of the pixel points when multiple particles pass through each layer of the detector plane; Step 3', based on the longitudinal distance, calculate the similarity weights of each coordinate position information of the particle and each super retinal unit in turn, sum the similarity weights of a super retinal unit and each coordinate position information of the particle to obtain the similarity weight value of the super retinal unit, and screen out the super retinal unit whose similarity weight value exceeds the set threshold sum_th1; Step 4', divide each super retinal unit obtained by screening into multiple cell units, calculate the similarity weight value of the cell unit, and screen the cell units whose similarity weight value exceeds the set threshold sum_th2; determine the linear trajectory of the particle based on the position and output it.

3. The trajectory tracking method according to claim 1 or 2, characterized in that: The formula for obtaining the similarity weight value of the cell unit / super retinal unit based on the longitudinal distance is: Where i and j represent the horizontal and vertical serial numbers of the cell unit / super retinal unit, respectively; m and k represent the total number and serial number of the layers of the parallel plane of the detector, respectively; is the similarity weight value of the coordinate position information of the particle detected by the cell unit / super retinal unit in the i-th row and j-th column and the k-th layer detector; The coordinate position information of the particle detected by the k-th layer detector and the distance between the cell unit / super retinal unit in the i-th row and j-th column in the longitudinal direction; is the adjustment factor for the operation.

4. The method according to claim 1 or 2, characterized in that: The formula for obtaining the similarity weight value of the cell unit / super retinal unit based on the longitudinal distance is: Where i and j represent the horizontal and vertical serial numbers of the cell unit / super retinal unit, respectively; m and n represent the total number and serial number of the layers of the parallel plane of the detector, respectively; is the similarity weight value of the coordinate position information of the particle detected by the cell unit / super retinal unit in the i-th row and j-th column and the n-th layer detector; The coordinate position information of the particle detected by the n-th layer detector and the distance between the cell unit / super retinal unit in the i-th row and j-th column in the longitudinal direction; A is the adjustment factor of the operation; n Represents the pixel value of the particle detected by the n-th layer detector.

5. The trajectory tracking method according to claim 1 or 2, characterized in that: The formula for obtaining the similarity weight value of the cell unit / super retinal unit based on the longitudinal distance is: Where i and j represent the horizontal and vertical serial numbers of the cell unit / super retinal unit, respectively; m and n represent the total number and serial number of the layers of the parallel plane of the detector, respectively; is the similarity weight value of the coordinate position information of the cell in the i-th row and j-th column and the particle detected by the detector in the n-th layer; The coordinate position information of the particle detected by the n-th layer detector and the distance between the cell unit / super retinal unit in the i-th row and j-th column in the longitudinal direction; A is the adjustment factor of the operation; n It represents the pixel value of the pixel point of the particle detected by the detector at the nth layer; d_th is the set distance threshold, and A_th is the set pixel threshold.

6. The trajectory tracking method according to claim 1 or 2, characterized in that: The process of determining the straight trajectory of the particle based on the position and outputting it includes: Determine the horizontal and vertical serial numbers i and j of the cell unit / super retinal unit, and use the slope and intercept of the cell unit / super retinal unit in the i-th row and j-th column as the slope and intercept of the particle linear track.

7. The trajectory tracking method according to claim 2, characterized in that: The trajectory tracking method further comprises: Using a detector to detect and obtain multiple track sample information, the track sample information is the coordinate position information of the pixel points when multiple particles pass through each layer of the detector plane; Tracing multiple trajectory samples to find multiple straight trajectories and match them with the trajectories in the standard library respectively: when there is only one straight trajectory that is the same as a trajectory in the standard library, it is determined to be a perfect matching trajectory; when at least two straight trajectories are the same as a trajectory in the standard library, it is determined to be an over-perfect matching trajectory; when any trajectory in the standard trajectory library is different from all straight trajectories, it is determined to be an unmatched trajectory; when the straight trajectory is different from any trajectory in the standard trajectory library, it is determined to be a false trajectory; The efficiency and purity of the trajectory tracking method were calculated based on the number of four matching trajectories.

8. A particle trajectory tracking device based on Retina algorithm, characterized in that: The trajectory tracking device comprises: a cell array establishment unit, a detector and a trajectory output unit; The cell array establishment unit is used to divide the two-dimensional space into various space units according to the set granularity, and establish the cell array using the space units as the cell units in the retina algorithm; The detector is used to detect and obtain the coordinate position information of the pixel point when a particle passes through each layer of the detector plane; The trajectory output unit is used to calculate the similarity weights of each coordinate position information of the particle and each cell unit in sequence based on the longitudinal distance, sum the similarity weights of a cell unit and each coordinate position information of the particle to obtain the similarity weight value of the cell unit, traverse all the cell units in the cell array, determine the position of the cell unit corresponding to the largest similarity weight value, determine the straight line trajectory of the particle based on the position and output it.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the processor is used to implement the steps of the particle trajectory tracking method based on the Retina algorithm as described in any one of claims 1 to 7 when executing a computer management program stored in the memory.

10. A computer-readable storage medium, characterized in that: A computer management program is stored thereon, and when the computer management program is executed by the processor, the steps of the particle trajectory tracking method based on the Retina algorithm as described in any one of claims 1 to 7 are implemented.

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