Material discrimination method and device based on muon transmission imaging, electronic equipment and storage medium

Abstract

The invention relates to a material screening method and device based on muon transmission imaging, electronic equipment and a storage medium. The method comprises the steps that actually-measured muon track flux data and a cosmic ray muon flux model are acquired; according to the actual measurement muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining an actual measurement muon blocking capability matrix; simulating penetration processes of cosmic ray muons in different track directions for multiple times according to the cosmic ray muon flux model to obtain multiple groups of simulated muon track flux data; according to the simulation muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining a simulation muon prevention capability matrix; determining the coincidence degree of each prior material combination according to the actually measured muon prevention capability matrix and the plurality of simulated muon prevention capability matrixes of each prior material combination; and determining a material screening result according to the coincidence degree of each prior material combination. Therefore, the actual material composition in the to-be-detected object can be effectively discriminated.

Description

Technical Field

[0001] The present disclosure relates to the field of nuclear technology applications, and in particular to a material identification method, device, electronic device and storage medium based on muon transmission imaging. Background Art

[0002] Natural cosmic ray muons are secondary particles produced by the interaction of high-energy primary cosmic rays from outer space with the Earth's atmosphere. The flux at sea level is about 1μ / cm 2 / min, energy range width (10 -1 GeV to 10 3 Cosmic ray muon transmission imaging uses the high energy and strong penetration characteristics of cosmic ray muons to measure the flux attenuation of penetrating matter, so as to achieve imaging of large objects such as mountains and buildings and study their internal structure and properties.

[0003] For example, in 2015, the ScanPyramids project team used a variety of detection technologies such as nuclear emulsion, scintillator and gas detectors to perform transmission imaging of the Egyptian Pyramid of Khufu, identifying several undiscovered hollow chambers. In 2023, the relevant team used muon transmission imaging technology to image the Zaozigou underground mining area and the ancient city wall of Xi'an, and obtained the density distribution of underground space and building structures. In addition to pyramids, muon transmission imaging has also been applied to structural imaging of volcanoes, caves, buildings, etc.

[0004] The current muon transmission imaging research is mainly aimed at reconstructing material density, and there is no analytical method for material type identification. The muon transmission imaging method for material identification is conducive to the exploration of the morphology and structure of large buildings, and is of great significance in archaeological protection and architectural protection. On the other hand, large-volume buildings such as mountains are mainly composed of materials with similar densities such as bricks, wood, soil, and stone, and the muon flux of penetrating cosmic rays is low, so the material identification method is relatively difficult and very critical. Summary of the invention

[0005] In view of this, the present disclosure proposes a material identification method, device, electronic device and storage medium based on muon transmission imaging, which can construct a probability model between a priori material combinations and the stopping power of transmitted muons, analyze the most probable a priori material combinations, and achieve material identification.

[0006] According to one aspect of the present disclosure, a material identification method is provided, comprising: obtaining measured muon track flux data of an object to be tested whose internal material is to be identified and a cosmic ray muon flux model, wherein the measured muon track flux data include measured muon fluxes of cosmic ray muons in different track directions after penetrating the object to be tested, which are actually measured at different points by using a muon detector, and the cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before they are injected into the object to be tested; according to the measured muon track flux data, the cosmic ray muon flux model and the object to be tested, a voxel discrete grid corresponding to the object to be tested, determining a measured muon stopping power matrix corresponding to the object to be tested, wherein the voxel discrete grid includes a plurality of voxels discretized from the object to be tested, and the measured muon stopping power matrix includes actual stopping powers of different voxels in the object to be tested for cosmic ray muons; obtaining a plurality of a priori material combinations of the object to be tested, and simulating the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination for multiple times according to the cosmic ray muon flux model, obtaining a plurality of groups of simulated muon track flux data corresponding to each a priori material combination, and each a priori material combination The method comprises the following steps: the simulated muon track flux data comprises the simulated muon flux in different track directions; and the simulated muon track flux data comprises the simulated muon flux in different track directions. The simulated muon track flux data comprises the simulated muon flux in different track directions. The simulated muon stopping power matrices corresponding to each a priori material combination are determined according to the multiple groups of simulated muon track flux data corresponding to each a priori material combination, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured. The simulated muon stopping power matrices corresponding to each a priori material combination comprise the simulated stopping power of different voxels in the object to be measured of each a priori material combination for cosmic ray muons. ; According to the measured muon stopping power matrix and the multiple simulated muon stopping power matrices corresponding to each of the multiple a priori material combinations, determine the degree of conformity of each a priori material combination with the actual material combination in the object to be tested, wherein the actual material combination includes the multiple material types actually contained in the object to be tested and the distribution areas of different material types; according to the degree of conformity of each of the multiple a priori material combinations with the actual material combination in the object to be tested, determine the material identification result of the object to be tested, and the material identification result represents the actual material combination in the object to be tested.

[0007] In a possible implementation, determining the measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured includes: determining the measured muon minimum energy matrix according to the measured muon track flux data and the cosmic ray muon flux model, the measured muon minimum energy matrix including the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be measured and be detected by the muon detector; determining the measured muon track length matrix according to the measured muon track flux data and the voxel discrete grid corresponding to the object to be measured, the measured muon track length matrix including the track lengths of cosmic ray muons in different track directions in different voxels in the object to be measured; determining the measured muon stopping power matrix of the object to be measured according to the measured muon minimum energy matrix and the measured muon minimum energy matrix.

[0008] In a possible implementation, the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination is simulated multiple times according to the cosmic ray muon flux model to obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, including: for any a priori material combination, a geometric model of the object to be tested is established according to the a priori material combination, and the geometric model represents the object to be tested when the a priori material combination is used; and Monte Carlo simulation technology is used to simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using the a priori material combination on the geometric model multiple times according to the cosmic ray muon flux model to obtain multiple groups of simulated muon track flux data corresponding to the a priori material combination.

[0009] In a possible implementation, the method of determining a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to each a priori material combination of the plurality of a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured comprises: for any a priori material combination, determining a plurality of simulated muon minimum energy matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and the cosmic ray muon flux model; determining a plurality of simulated muon track length matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be measured; and determining a plurality of simulated muon stopping power matrices corresponding to the a priori material combination according to the plurality of simulated muon minimum energy matrices and the plurality of simulated muon minimum energy matrices.

[0010] In a possible implementation, the method of determining the degree of conformity of each a priori material combination with the actual material combination in the object to be tested based on the measured muon stopping power matrix and multiple simulated muon stopping power matrices corresponding to each a priori material combination in the multiple a priori material combinations includes: for any a priori material combination, determining the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used based on the simulated muon stopping power matrix corresponding to the a priori material combination and the voxel discrete grid corresponding to the object to be tested, the likelihood function corresponding to any voxel representing the probability that the voxel has different stopping powers under the material type contained in a given voxel; determining the posterior probability corresponding to the a priori material combination based on the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used and the measured muon stopping power matrix, the posterior probability representing the degree of conformity of the a priori material combination with the actual material combination in the object to be tested.

[0011] In a possible implementation, the determining the posterior probability corresponding to the a priori material combination according to the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used and the measured muon stopping power matrix includes: determining a material discrimination probability model based on Bayesian inference according to the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used; solving the material discrimination probability model according to the measured muon stopping power matrix to obtain the posterior probability corresponding to the a priori material combination; wherein the material discrimination probability model is expressed as:

[0012]

[0013] Among them, m represents the prior material combination, m j represents the material type contained in the jth voxel, m j ∈m={m1,…,m J}, j∈{1,…,J}, J represents the total number of voxels in the object to be tested; f j represents the likelihood function corresponding to the jth voxel in the object to be tested when the prior material combination m is used, and p represents the likelihood function f j The stopping power variable, p j represents the measured stopping power corresponding to the jth voxel in the measured muon stopping power matrix, f j (p=p j |m j ) represents the value of j The next blocking power variable p is p j π(m) represents the prior probability preset for the prior material combination m; k represents the normalization factor.

[0014] In one possible implementation, the material identification result of the object to be tested is determined based on the degree of conformity between each prior material combination among the multiple prior material combinations and the actual material combination in the object to be tested, including: determining the prior material combination with the greatest degree of conformity as the material identification result of the object to be tested; or, optimizing the prior material combination with the greatest degree of conformity to obtain an optimized prior material combination, and re-determining the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested, until the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested reaches a preset requirement, and determining the prior material combination whose degree of conformity reaches the preset requirement as the material identification result of the object to be tested.

[0015] According to another aspect of the present disclosure, a material identification device is provided, comprising: an acquisition module, for acquiring measured muon track flux data of an object to be detected of an internal material to be identified and a cosmic ray muon flux model, wherein the measured muon track flux data comprises measured muon fluxes of cosmic ray muons in different track directions after penetrating the object to be detected, which are actually measured at different points by using a muon detector, and the cosmic ray muon flux model comprises initial muon fluxes of cosmic ray muons in different track directions before they are injected into the object to be detected; a first determination module, for determining a material identification device according to the measured muon track flux data, the cosmic ray muon flux model and the A voxel discrete grid corresponding to the object to be tested is used to determine the measured muon stopping power matrix corresponding to the object to be tested, wherein the voxel discrete grid includes a plurality of voxels discretized from the object to be tested, and the measured muon stopping power matrix includes the actual stopping power of different voxels in the object to be tested for cosmic ray muons; a simulation module is used to obtain a plurality of a priori material combinations of the object to be tested, and according to the cosmic ray muon flux model, simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination for multiple times, and obtain a plurality of groups of simulated muon track flux data corresponding to each a priori material combination, wherein each a priori material combination The simulated muon track flux data includes the estimated multiple material types and distribution areas of different material types, and the simulated muon track flux data includes the simulated muon flux in different track directions; a second determination module is used to determine a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to the multiple groups of simulated muon track flux data corresponding to each a priori material combination in the multiple a priori material combinations, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured, wherein each simulated muon stopping power matrix corresponding to each a priori material combination includes the simulated stopping power of different voxels in the object to be measured of each a priori material combination for cosmic ray muons; A third determination module is used to determine the degree of conformity of each a priori material combination with the actual material combination in the object to be tested according to the measured muon stopping power matrix and multiple simulated muon stopping power matrices corresponding to each of the multiple a priori material combinations, wherein the actual material combination includes multiple material types actually contained in the object to be tested and distribution areas of different material types; a screening module is used to determine the material screening result of the object to be tested according to the degree of conformity of each a priori material combination in the multiple a priori material combinations with the actual material combination in the object to be tested, wherein the material screening result represents the actual material combination in the object to be tested.

[0016] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0017] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0018] According to another aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0019] According to various aspects of the present disclosure, the measured muon track flux data of the object to be measured are measured by a muon detector to determine the measured muon stopping power matrix, and the simulated muon track flux data under different prior material combinations are obtained by simulating the penetration process of cosmic ray muons in the object to be measured with different prior material combinations. Then, based on the simulated muon track flux data, the simulated muon stopping power matrix corresponding to each prior material combination is determined. Then, by determining the degree of conformity between the simulated muon stopping power matrix corresponding to each prior material data and the measured muon stopping power matrix, the actual material combination inside the object to be measured can be effectively identified based on the degree of conformity, that is, the material types actually contained in the object to be measured and the distribution areas of different material types can be identified.

[0020] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0022] Figure 1 A flow chart of a material identification method according to an embodiment of the present disclosure is shown.

[0023] Figure 2 A schematic diagram of muon transmission imaging according to an embodiment of the present disclosure is shown.

[0024] Figure 3 A schematic diagram showing a geometric model of a tunnel-type building with a width of 7 m and a height of 7 m according to an embodiment of the present disclosure is shown.

[0025] Figure 4(a) to Figure 4(d) Schematic diagrams showing four geometric models under four priori material combinations estimated for tunnel-type buildings according to an embodiment of the present disclosure.

[0026] Figure 5 A schematic diagram showing a material screening process according to an embodiment of the present disclosure is shown.

[0027] Figure 6 The embodiment of the present disclosure is shown Figure 3 Schematic diagram of the reconstruction results of the measured muon stopping power matrix of the tunnel-type building.

[0028] Figure 7(a) to Figure 7(d) A schematic diagram showing the distribution of likelihood functions of each voxel under four prior material combinations according to an embodiment of the present disclosure.

[0029] Figure 8 A block diagram of a material identification device according to an embodiment of the present disclosure is shown.

[0030] Fig. 9 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0031] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0032] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0033] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0034] It should be understood that the terms "first", "second", etc. in the claims, specifications and drawings of the present disclosure are used to distinguish different objects rather than to describe a specific order. The terms "include" and "comprise" used in the specifications and claims of the present disclosure indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0035] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0036] In practical applications, the material identification method proposed in the embodiment of the present disclosure can be deployed on various terminal devices through software or hardware modification. The terminal device involved in the embodiment of the present disclosure may refer to a device with a wireless connection function and / or a wired connection function. The wireless connection function refers to the ability to connect to other devices through wireless connection methods such as wifi and Bluetooth. The terminal device involved in the embodiment of the present disclosure may also communicate with other devices through a wired connection function. The terminal device involved in the embodiment of the present disclosure may be a touch screen, a non-touch screen, or a screenless device. The touch screen device can be controlled by clicking and sliding on the display screen with a finger or a stylus. The non-touch screen device can be connected to an input device such as a mouse, a keyboard, a touch panel, and the terminal device is controlled through the input device. For example, the device without a screen may be a Bluetooth speaker without a screen. For example, the terminal device of the present application may include but is not limited to a user equipment (UE), a mobile device, a user terminal, a terminal, a handheld device, a tablet computer, a laptop, a PDA, a computing device, etc.

[0037] The material identification method of the embodiment of the present disclosure can also be deployed on a server, which can be located in the cloud or locally, and can be a physical device or a virtual device, such as a virtual machine, a container, etc., and has a wireless communication function, wherein the wireless communication function can be set in the chip (system) or other parts or components of the server. It can refer to a device with a wireless connection function, and the wireless connection function refers to the ability to connect to other servers or terminal devices through wireless connection methods such as Wi-Fi and Bluetooth. The server involved in the embodiment of the present disclosure may also have the function of communicating through a wired connection. For example, the server can receive the measured muon track flux data of the object to be tested sent by the terminal device, and the server executes the material identification method of the embodiment of the present disclosure based on the measured muon track flux data to obtain a material identification result, and returns the material identification result to the terminal device, so as to display the material identification result of the object to be tested to the user in the terminal device.

[0038] Figure 1 FIG. 1 is a flow chart showing a material identification method according to an embodiment of the present disclosure. The method can be applied to electronic devices such as the above-mentioned terminal device or server. Figure 1 As shown, the method includes: step S11 to step S16.

[0039] In step S11, the measured muon track flux data of the object to be tested whose internal material is to be identified and the cosmic ray muon flux model are obtained, wherein the measured muon track flux data include the measured muon flux of cosmic ray muons in different track directions after penetrating the object to be tested, which is actually measured at different points by using a muon detector, and the cosmic ray muon flux model includes the initial muon flux of cosmic ray muons in different track directions before they are injected into the object to be tested.

[0040] Among them, the object to be tested can be any physical object such as a building, object, natural landscape, etc. whose internal materials are to be identified, or it can be a partial area of ​​a physical object such as a building, object, natural landscape, etc., which is not limited in the embodiments of the present disclosure.

[0041] As described above, cosmic ray muons are secondary particles generated by the interaction of high-energy primary cosmic rays from outer space with the Earth's atmosphere. Therefore, those skilled in the art can use any known initial muon flux testing technology in the art to obtain the initial muon flux of cosmic ray muons in different track directions (i.e., different azimuths) before they enter the object to be tested. The initial muon flux can be understood as the muon flux before it is blocked by the object to be tested. The disclosed embodiments do not limit the method for obtaining the initial muon flux.

[0042] In practical applications, any muon detector known in the art can be used to detect the measured muon flux after the cosmic ray muons in different track directions penetrate the object to be measured, wherein those skilled in the art can manipulate the muon detector at multiple points around the object to be measured according to the actual structure of the object to be measured to detect the measured muon flux in different track directions (i.e., different azimuths), and the embodiments of the present disclosure are not limited to this. It should be understood that the muon penetration of the object to be measured will produce flux attenuation, and the measured muon flux is the muon flux after the muons are blocked by the object to be measured and attenuated.

[0043] In step S12, the measured muon stopping power matrix corresponding to the object to be measured is determined based on the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured. The voxel discrete grid includes multiple voxels discretized from the object to be measured, and the measured muon stopping power matrix includes the actual stopping powers of different voxels in the object to be measured for cosmic ray muons.

[0044] In practical applications, those skilled in the art can design the size of a unit voxel according to information such as the shape and size of the object to be measured, and then discretize the object to be measured into a voxel discrete grid according to the size of the unit voxel, that is, discretize it into a number of voxels, for example, Figure 2 A schematic diagram of muon transmission imaging is shown in FIG. Figure 2As shown, a partial area within the triangular building can be discretized to obtain a voxel discrete grid, wherein each grid represents a voxel. It should be understood that those skilled in the art can use any voxel discretization method known in the art to construct a voxel discrete grid of the object to be tested, and the embodiments of the present disclosure are not limited to this.

[0045] In a possible implementation, determining the measured muon stopping power matrix corresponding to the object to be measured based on the measured muon track flux data, the cosmic ray muon flux model, and the voxel discrete grid corresponding to the object to be measured includes:

[0046] According to the measured muon track flux data and the cosmic ray muon flux model, the measured muon minimum energy matrix is ​​determined. The measured muon minimum energy matrix includes the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be measured and be detected by the muon detector.

[0047] Determine a measured muon track length matrix according to the measured muon track flux data and the voxel discrete grid corresponding to the object to be measured, wherein the measured muon track length matrix includes the track lengths of cosmic ray muons in different voxels in the object to be measured in different track directions;

[0048] According to the measured muon minimum energy matrix and the measured muon minimum energy matrix, the measured muon stopping power matrix of the object to be measured is determined.

[0049] It can be seen that the essence of muon transmission imaging is to use the flux attenuation of cosmic ray muons in matter for imaging. The measured muon flux measured by the muon detector (i.e., muon track detector) can be expressed as formula (1):

[0050]

[0051] Where E represents the energy of the muon; Ω represents the azimuth angle of the muon incident (i.e., the direction of the track); ε represents the detection efficiency of the muon detector; φ(Ω,E) represents the initial muon flux of the cosmic ray muon differential; φ m (Ω) represents the measured muon flux of the cosmic ray muon integral after penetrating the object to be measured, which can be actually detected by the muon detector; E min (Ω) represents the minimum energy required for a muon to pass through the object to be detected within the azimuth angle Ω and be detected by the muon detector, which is related to the material structure of the object to be detected.

[0052] Therefore, the minimum energy E of cosmic ray muons in each track direction can be calculated using formula (1) based on the measured muon flux in different track directions measured by the muon detector and the initial muon flux of the incident cosmic ray muons in different track directions. min (Ω), thus obtaining the measured muon minimum energy matrix E min.

[0053] The track length of a muon in any voxel in any track direction is the length of the muon track in that voxel, as Figure 2 As shown, M i,j is the length of the muon track of the i-th track direction Ω in the j-th voxel, φ m (i) = φ m (Ω). Wherein, the track direction Ω of the muon track, the size of the unit voxel and the voxel discrete grid are known. Those skilled in the art may adopt any geometric algorithm known in the art. For example, they may refer to the fast calculation method of the radiological path for a three-dimensional computed tomography (CT) array proposed by Robert L. Siddon in "Fast calculation of the exact radiological path for a three-dimensional CT array" to realize the calculation of the track lengths of cosmic ray muons in different voxels in the object to be measured in different track directions according to the voxel discrete grid corresponding to the object to be measured, and the embodiments of the present disclosure are not limited to this.

[0054] Then, the stopping power matrix p of each voxel for muons and the muon minimum energy matrix E can be established using formula (2): min Relationships:

[0055] M×p=E min (2)

[0056] Where M represents the measured muon track length matrix, and the elements M in M ​​are i,j is the length of the i-th muon track in the j-th voxel; p represents the measured muon stopping power matrix, and the element p in p j is the stopping power of the jth voxel for muons, in MeV / cm; E min represents the measured minimum muon energy matrix, E min The element E in min,i is the minimum energy required for a muon in the i-th track direction to pass through the object to be detected and be detected by the muon detector.

[0057] In practical applications, any numerical method known in the art, such as the classic algebraic reconstruction technique (ART), can be used to solve formula (2) to obtain the measured muon stopping power matrix p corresponding to the object to be measured, and the embodiments of the present disclosure are not limited to this. Among them, the definition of stopping power is clearly known in the art, and the following brief description is given in the embodiments of the present disclosure: the stopping power of each voxel in the object to be measured for cosmic ray muons can be understood as the ability of each voxel to prevent cosmic ray muons from passing through. The greater the stopping power, the stronger the ability to prevent cosmic ray muons from passing through, or the more cosmic ray muons are prevented from passing through, the more flux attenuation.

[0058] In step S13, a plurality of a priori material combinations of the object to be measured are obtained, and according to the cosmic ray muon flux model, the penetration process of cosmic ray muons in different track directions in the object to be measured using each a priori material combination is simulated multiple times to obtain a plurality of groups of simulated muon track flux data corresponding to each a priori material combination, each a priori material combination includes a plurality of estimated material types and distribution areas of different material types, and the simulated muon track flux data includes simulated muon fluxes in different track directions.

[0059] In practical applications, those skilled in the art can construct a variety of prior material combinations of the object to be tested based on prior knowledge of the object to be tested or existing detection technology, which is equivalent to estimating the possible situations of the material combinations inside some objects to be tested. For example, Figure 3 The geometric model of a tunnel-type building with a width of 7m and a height of 7m is shown. There are five layers of structure inside, namely 1m thick softwood, 0.5m thick cavity, 1m thick hardwood, 0.5m thick cavity and 1m thick rock. Figure 4(a) to Figure 4(d) The display may be for Figure 3 The geometric models of the four prior material combinations predicted for the tunnel-type building are shown in FIG4(a), FIG4(b), FIG4(c) and FIG4(d). The prior material combinations from the outside to the inside are: cork-air-hardwood-air-rock, hardwood-air-cork-air-rock, cork-air-rock-air-hardwood, rock-air-hardwood-air-cork, where different colors represent different material types, and the color areas covered by different colors represent the distribution areas of different material types. It should be understood that the embodiments of the present disclosure do not limit the number and content of the prior material combinations predicted for the object to be tested.

[0060] In a possible implementation, a Monte Carlo simulation technique known in the art can be used to simulate the penetration process (i.e., the transport process) of cosmic ray muons in different track directions in the object to be tested using each a priori material combination. Specifically, according to the cosmic ray muon flux model, the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination is simulated multiple times to obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, including:

[0061] For any a priori material combination, a geometric model of the object to be tested is established according to the a priori material combination, and the geometric model represents the object to be tested when the a priori material combination is used;

[0062] Monte Carlo simulation technology is used to simulate the penetration process of cosmic ray muons in different track directions in the object to be tested with a priori material combination on the geometric model according to the cosmic ray muon flux model, and multiple groups of simulated muon track flux data corresponding to the a priori material combination are obtained.

[0063] Among them, those skilled in the art can use any known geometric modeling technology in the art to establish a geometric model of the object to be tested based on the prior material combination, wherein the established geometric model can characterize the object to be tested when the prior material combination is used and the shape, size and other shape information of the object to be tested, which is equivalent to using the geometric model to simulate the object to be tested when the prior material combination is used. It should be understood that the embodiments of the present disclosure do not limit the modeling method of the geometric model of the object to be tested. For example, the above Figure 4(a) to Figure 4(d) The geometric model of the tunnel-type building is shown for four a priori material combinations.

[0064] Among them, the Monte Carlo simulation technology is used to simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using the priori material combination on the geometric model according to the cosmic ray muon flux model. It can be understood that the Monte Carlo simulation technology is used to generate sampled muons in different track directions according to the cosmic ray muon flux model for many times, and then the geometric model of the sampled muons and the object to be tested is used each time to simulate the penetration process (that is, the transport process) of cosmic ray muons in different track directions in the object to be tested using the priori material combination, and multiple groups of simulated muon track flux data under the priori material combination are obtained, that is, the simulated muon flux of cosmic ray muons in different track directions after penetrating the object to be tested using the priori material combination is obtained, which is equivalent to simulating the flux attenuation when cosmic ray muons in different track directions penetrate the object to be tested with the priori material combination, so as to obtain the simulated muon flux in different track directions.

[0065] It should be understood that the above-mentioned use of Monte Carlo simulation technology to simulate the penetration process of cosmic ray muons is a possible implementation method proposed in the embodiment of the present disclosure. Those skilled in the art can also use other simulation technologies known in the art to simulate the above-mentioned penetration process of cosmic ray muons, and the embodiment of the present disclosure does not limit this.

[0066] In step S14, multiple simulated muon stopping power matrices corresponding to each a priori material combination are determined based on multiple groups of simulated muon track flux data corresponding to each a priori material combination, a cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be tested. Each simulated muon stopping power matrix corresponding to each a priori material combination includes the simulated stopping power of different voxels in the object to be tested using each a priori material combination for cosmic ray muons.

[0067] Among them, the calculation process of the measured muon stopping power matrix can be referred to to determine the multiple simulated muon stopping power matrices corresponding to each a priori material combination. Specifically, the multiple simulated muon track flux data corresponding to each a priori material combination in the multiple a priori material combinations, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured are determined to determine the multiple simulated muon stopping power matrices corresponding to each a priori material combination, which may include:

[0068] For any a priori material combination, multiple groups of simulated muon track flux data corresponding to the a priori material combination and the cosmic ray muon flux model are used to determine multiple groups of simulated muon minimum energy matrices corresponding to the a priori material combination; wherein the simulated muon minimum energy matrix represents the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be tested using the a priori material combination and be detected by the muon detector;

[0069] Determine a plurality of simulated muon track length matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be measured; wherein the simulated muon track length matrix represents the track lengths of cosmic ray muons in different track directions in different voxels of the object to be measured using the a priori material combination;

[0070] According to multiple simulated muon minimum energy matrices and multiple simulated muon minimum energy matrices, multiple simulated muon stopping power matrices corresponding to the priori material combination are determined; each simulated muon stopping power matrix corresponding to the priori material combination includes the simulated stopping power of different voxels in the object to be tested using the priori material combination for cosmic ray muons.

[0071] Among them, the calculation method of the measured muon minimum energy matrix in the above formula (1) can be referred to to realize the multiple sets of simulated muon track flux data corresponding to the priori material combination and the cosmic ray muon flux model, and determine the multiple simulated muon minimum energy matrices corresponding to the priori material combination, wherein a set of simulated muon track flux data and the cosmic ray muon flux model can determine a simulated muon minimum energy matrix; and the calculation method of the actual muon track length matrix can be referred to to realize the multiple sets of simulated muon track flux data corresponding to the priori material combination and the voxel discrete grid corresponding to the object to be measured, and determine the priori A simulated muon track length matrix corresponding to a material combination, wherein a set of simulated muon track flux data and a voxel discrete grid can determine a simulated muon track length matrix; and, referring to the calculation method of the measured muon stopping power matrix in the above formula (2), multiple simulated muon minimum energy matrices and multiple simulated muon minimum energy matrices can be used to determine multiple simulated muon stopping power matrices corresponding to a priori material combinations, wherein a simulated muon stopping power matrix is ​​determined based on a simulated muon minimum energy matrix and a simulated muon minimum energy matrix calculated based on the same set of simulated muon track flux data. It should be understood that there are several sets of simulated muon track flux data that can calculate several simulated muon stopping power matrices, which will not be elaborated here.

[0072] In step S15, the degree of conformity of each a priori material combination with the actual material combination in the object to be tested is determined based on the measured muon stopping power matrix and multiple simulated muon stopping power matrices corresponding to each of the multiple a priori material combinations, where the actual material combination includes the multiple material types actually contained in the object to be tested and the distribution areas of different material types.

[0073] In a possible implementation, determining the degree of conformity between each a priori material combination and the actual material combination in the object to be tested based on the measured muon stopping power matrix and the multiple simulated muon stopping power matrices corresponding to each a priori material combination in the multiple a priori material combinations may include:

[0074] For any a priori material combination, according to a plurality of simulated muon stopping power matrices corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be tested, a likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used is determined, and the likelihood function corresponding to any voxel represents the probability that the voxel has different stopping powers under the material type contained in the given voxel;

[0075] According to the likelihood function corresponding to each voxel in the object to be tested when the priori material combination is used and the measured muon stopping power matrix, the posterior probability corresponding to the priori material combination is determined, and the posterior probability represents the degree of conformity between the priori material combination and the actual material combination in the object to be tested.

[0076] In practical applications, for any a priori material combination, multiple simulation experiments can be performed and simulated stopping powers can be calculated through the above steps S13 to S14, that is, each voxel can simulate and calculate multiple simulated stopping powers (or each a priori material combination can simulate and calculate multiple simulated muon stopping power matrices), and then based on the multiple simulated stopping powers corresponding to each voxel and the material type contained in each voxel, the likelihood function f(p|m) corresponding to each voxel in the object to be tested under the a priori material combination can be constructed. It should be understood that those skilled in the art can use any known likelihood function construction method in the art to realize the construction of the likelihood function corresponding to each voxel, and the embodiments of the present disclosure are not limited to this.

[0077] Among them, Bayesian inference can be used to realize the posterior probability of the prior material based on the likelihood function and the measured muon stopping power matrix. Specifically, the above-mentioned determination of the posterior probability corresponding to the prior material combination based on the likelihood function corresponding to each voxel in the object to be tested when the prior material combination is used and the measured muon stopping power matrix may include:

[0078] According to the likelihood function corresponding to each voxel in the object to be tested when the prior material combination is adopted, a material discrimination probability model based on Bayesian inference is determined;

[0079] According to the measured muon stopping power matrix, the material identification probability model is solved to obtain the posterior probability corresponding to the prior material combination;

[0080] Among them, the material identification probability model is expressed as formula (3):

[0081]

[0082] Among them, m represents the prior material combination, m j represents the material type contained in the jth voxel, m j ∈m={m1,…,m J}, j∈{1,…,J}, J represents the total number of voxels in the object to be tested; f j represents the likelihood function corresponding to the jth voxel in the object to be tested when the prior material combination m is used, and p represents the likelihood function f j The stopping power variable, p j represents the measured stopping power corresponding to the jth voxel in the measured muon stopping power matrix p, p j ∈p={p1,…,p J}, f j (p=p j |m j ) represents the value of j The next blocking power variable p is p j; π(m) represents the prior probability preset for the prior material combination m, π(m) is usually determined according to empirical rules or measurement information, and in the absence of other additional information, π(m) = 1 can be taken; k represents the normalization factor, which can also be understood as the normalization factor of the posterior probability P(m). Those skilled in the art can customize the specific value of the normalization factor, which is not limited to the embodiments of the present disclosure.

[0083] The above-mentioned method of determining the posterior probability can be understood as first establishing the material discrimination probability model based on Bayesian inference shown in formula (3) based on the likelihood function corresponding to each voxel in the object to be tested when a certain prior material combination is used, and then bringing the measured stopping power corresponding to each voxel in the measured muon stopping power matrix into the above-mentioned material discrimination probability model to obtain the posterior probability corresponding to the prior material combination; the posterior probability represents the degree of conformity, and the larger the posterior probability, the higher the degree of conformity. It should be understood that for each prior material combination, the above-mentioned method of determining the posterior probability can be referred to to obtain the posterior probability corresponding to each prior material combination.

[0084] It should be noted that the above-mentioned calculation of the degree of compliance corresponding to each prior material combination by establishing a likelihood function and a material discrimination probability model is a possible implementation method provided by the embodiments of the present disclosure. In fact, under the guidance of the embodiments of the present disclosure, those skilled in the art may also adopt other related algorithms known in the art to indicate the above-mentioned degree of compliance. For example, the similarity between the measured muon stopping power matrix and the simulated muon stopping power matrix corresponding to each prior material combination may be calculated, and the similarity may be used as the degree of compliance. The higher the similarity, the higher the degree of compliance.

[0085] In step S16, a material identification result of the object to be tested is determined according to the degree of conformity between each of the multiple priori material combinations and the actual material combination in the object to be tested, and the material identification result represents the actual material combination in the object to be tested.

[0086] Optionally, determining the material identification result of the object to be tested according to the degree of conformity between each prior material combination in the plurality of prior material combinations and the actual material combination in the object to be tested may include: determining the prior material combination with the greatest degree of conformity as the material identification result of the object to be tested. This method can be understood as excluding the prior material combination that does not conform, and directly determining the prior material combination with the greatest degree of conformity (such as the greatest posterior probability, i.e., the most probable) as the actual material combination of the object to be tested.

[0087] Taking into account that the screened a priori material combination with the greatest degree of compliance may still not be accurate enough, optionally, the above-mentioned determination of the material identification result of the object to be tested based on the degree of compliance corresponding to each priori material combination in the multiple prior material combinations may include: optimizing the priori material combination with the greatest degree of compliance to obtain an optimized priori material combination, and re-determining the degree of compliance between the optimized priori material combination and the actual material combination in the object to be tested, until the degree of compliance between the optimized priori material combination and the actual material combination in the object to be tested reaches a preset requirement, and determining the priori material combination whose degree of compliance reaches the preset requirement as the material identification result of the object to be tested.

[0088] Among them, optimizing the prior material combination can be, for example, optimizing the distribution area of ​​each material type in the prior material combination. The embodiments of the present disclosure do not limit the optimization method. For example, the prior material combination can be optimized by using a search algorithm known in the art, and the embodiments of the present disclosure do not limit this.

[0089] Among them, the implementation method of the above-mentioned steps S12 to S15 can be referred to to re-determine the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested; then, it is judged whether the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested meets the preset requirements. If the preset requirements are not met, the optimized prior material combination can be optimized again until the degree of conformity between the iteratively optimized prior material combination and the actual material combination in the object to be tested meets the preset requirements, and the prior material combination whose degree of conformity meets the preset requirements is determined as the material identification result of the object to be tested.

[0090] In actual applications, technicians in this field can customize the specific content of the preset requirements according to actual needs. For example, if the degree of compliance is characterized by a posterior probability, the preset requirement can be a preset probability threshold, and the degree of compliance reaching the preset requirement can include the posterior probability being greater than or equal to the preset probability threshold, and then the priori material combination with the posterior probability greater than or equal to the probability threshold can be determined as the actual material combination of the object to be tested; if the degree of compliance is characterized by similarity, the preset requirement can be a preset similarity threshold, and the degree of compliance reaching the preset requirement can include the similarity being greater than or equal to the preset similarity threshold, etc., and then the priori material combination with the similarity greater than or equal to the similarity threshold can be determined as the actual material combination of the object to be tested, and the embodiments of the present disclosure are not limited to this.

[0091] According to the method of the embodiment of the present disclosure, the measured muon track flux data of the object to be measured are measured by a muon detector to determine the measured muon stopping power matrix, and the simulated muon track flux data under different prior material combinations are obtained by simulating the penetration process of cosmic ray muons in the object to be measured with different prior material combinations. Then, based on the simulated muon track flux data, the simulated muon stopping power matrix corresponding to each prior material combination is determined. Then, by determining the degree of conformity between the simulated muon stopping power matrix corresponding to each prior material data and the measured muon stopping power matrix, the actual material combination inside the object to be measured can be effectively identified based on the degree of conformity, that is, the material types actually contained in the object to be measured and the distribution areas of different material types can be identified.

[0092] The disclosed embodiment proposes a material identification method based on muon transmission imaging. The statistical distribution of the muon stopping power of each voxel is obtained in the image reconstruction through Monte Carlo simulation calculation. The imaging results under different prior material combinations are optimized and iterated in combination with the Bayesian statistical method. Finally, the most probable material composition is given, which effectively realizes the identification of the actual material composition inside the object to be tested.

[0093] Based on the material identification method provided by the above disclosed example, Figure 5 A schematic diagram showing a material identification process according to an embodiment of the present disclosure is shown as follows: Figure 5 As shown in the figure, the material identification process includes three parts: actual muon imaging experiment, Monte Carlo simulation experiment and Bayesian inference; among them, the actual muon imaging experiment mainly includes: measuring muon track and flux (i.e. measuring the measured muon track flux data), then calculating the track length matrix based on the muon track and flux (i.e. calculating the measured muon track length matrix), and calculating the muon minimum energy based on the muon track and flux and the incident muon angular distribution and flux (i.e. cosmic ray muon flux model) (i.e. calculating the measured muon minimum energy matrix), and then numerically solving the equation M×p=E based on the track length matrix and the muon minimum energy. min , and the measured muon stopping power matrix is ​​obtained. Among them, the Monte Carlo simulation experiment part includes: calculating the simulated muon track and flux (i.e. calculating the simulated muon track flux data) based on the prior building structure and materials (i.e. the prior material combination) and the incident muon angular distribution and flux (i.e. the cosmic ray muon flux model), and then calculating the muon minimum energy (i.e. calculating the simulated muon minimum energy matrix) based on the simulated muon track and flux and the incident muon angular distribution and flux, and calculating the track length matrix (i.e. calculating the simulated muon track length matrix) based on the simulated muon track and flux, and then numerically solving the equation M×p=E based on the simulated track length matrix and the muon minimum energy. min, and obtain the simulated muon stopping power matrix. The Bayesian inference part includes: for the prior building structure and materials (i.e., the prior material combination), multiple simulation experiments can be carried out through Monte Carlo simulation experiments to obtain multiple simulated muon stopping power matrices (i.e., p1, p2, ..., p obtained by simulation experiments #1, #2, ..., #N respectively). N ), and then the distribution f(p|m) of the simulated muon stopping power matrix can be constructed, that is, the likelihood function corresponding to each voxel under the prior building structure and material (that is, the prior material combination) can be obtained. Then, based on the measured muon stopping power matrix and f(p|m), the posterior probability corresponding to the prior building structure and material (that is, the prior material combination) can be obtained. Based on the posterior probability, the prior building structure and material can be optimized and iterated to obtain the most probable material combination (that is, the prior material combination with the highest posterior probability).

[0094] According to the embodiments of the present disclosure, by taking the measured muon tracks, the angular distribution and flux of cosmic ray muons, and the prior building structure materials as inputs, the posterior probabilities corresponding to different prior material combinations are given in combination with Monte Carlo simulation and Bayesian inference, thereby realizing the identification of the composition of the building materials. In addition, by iteratively comparing the posterior probabilities under different prior material combinations, the prior material combinations that are inconsistent with the actual material composition can be excluded, and the most probable prior material combination can be given, thereby realizing material identification.

[0095] Based on the material identification method of the above-mentioned embodiment of the present disclosure, the feasibility of the material identification method proposed in the embodiment of the present disclosure can also be verified through ideal experiments. Figure 3 The tunnel-shaped geometric building shown is 7m wide and 7m high, and Figure 4(a) to Figure 4(d) The geometric model of the tunnel-type building for four a priori material combinations is shown. Assuming that cosmic ray muons are emitted from above the building, the direction of the muon zenith angle is extracted according to the generalized Gassier formula. The total number of muons emitted in the simulation experiment is equivalent to the accumulated muon counts at sea level for 4 days, which can be obtained. Figure 6 as well as Figure 7(a) to Figure 7(d) ideal experimental results.

[0096] in, Figure 6 Shows the Figure 3 The reconstruction results of the measured muon stopping power matrix of the tunnel-type building are as follows: Figure 6 As shown, the structure of the two-layer cavity is clearly visible, with a certain ability to distinguish between rock and wood. Figure 7(a) to Figure 7(d)The distribution of the likelihood function f(p|m) of each voxel under the four prior material combinations is shown, where the darker the color, the higher the likelihood function value. It can be seen from Figure 7(a), Figure 7(b), Figure 7(c) and Figure 7(d) that when the material type contained in the voxel under the prior material combination is inconsistent with the actual material type, its likelihood function value will be significantly lower than the expected result.

[0097] According to the material identification method proposed in the embodiment of the present disclosure, it can be calculated Figure 4(a) to Figure 4(d) The posterior probabilities P under the four prior material combinations shown are -18539, -23161, -147720 and -239716, respectively. Among them, the prior material combination "softwood-air-hardwood-air-rock" corresponding to Figure 4(a), which is consistent with the actual material combination, has the largest posterior probability and the best degree of compliance.

[0098] In summary, it can be seen that the material identification method based on Monte Carlo simulation and Bayesian inference proposed in the embodiment of the present disclosure can effectively identify three materials: softwood, hardwood and rock, and has good feasibility.

[0099] Figure 8 A block diagram of a material identification device according to an embodiment of the present disclosure is shown as follows: Figure 8 As shown, the device comprises:

[0100] An acquisition module 801 is used to acquire measured muon track flux data of an object to be tested whose internal material is to be identified and a cosmic ray muon flux model, wherein the measured muon track flux data includes measured muon fluxes of cosmic ray muons in different track directions after penetrating the object to be tested, measured at different points by using a muon detector, and the cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before they are injected into the object to be tested;

[0101] A first determination module 802 is used to determine a measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured, wherein the voxel discrete grid includes a plurality of voxels discretized from the object to be measured, and the measured muon stopping power matrix includes actual stopping powers of different voxels in the object to be measured for cosmic ray muons;

[0102] The simulation module 803 is used to obtain multiple a priori material combinations of the object to be tested, and according to the cosmic ray muon flux model, simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination for multiple times, and obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, each a priori material combination includes multiple estimated material types and distribution areas of different material types, and the simulated muon track flux data includes simulated muon fluxes in different track directions;

[0103] A second determination module 804 is used to determine a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to each a priori material combination in the plurality of a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured, wherein each simulated muon stopping power matrix corresponding to each a priori material combination includes simulated stopping powers of different voxels in the object to be measured using each a priori material combination for cosmic ray muons;

[0104] A third determination module 805 is used to determine the degree of conformity of each a priori material combination with the actual material combination in the object to be tested according to the measured muon stopping power matrix and the simulated muon stopping power matrix corresponding to each of the multiple a priori material combinations, wherein the actual material combination includes multiple material types actually contained in the object to be tested and distribution areas of different material types;

[0105] The identification module 806 is used to determine the material identification result of the object to be tested according to the degree of conformity between each of the multiple priori material combinations and the actual material combination in the object to be tested, and the material identification result represents the actual material combination in the object to be tested.

[0106] In a possible implementation, determining the measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured includes: determining the measured muon minimum energy matrix according to the measured muon track flux data and the cosmic ray muon flux model, the measured muon minimum energy matrix including the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be measured and be detected by the muon detector; determining the measured muon track length matrix according to the measured muon track flux data and the voxel discrete grid corresponding to the object to be measured, the measured muon track length matrix including the track lengths of cosmic ray muons in different track directions in different voxels in the object to be measured; determining the measured muon stopping power matrix of the object to be measured according to the measured muon minimum energy matrix and the measured muon minimum energy matrix.

[0107] In a possible implementation, according to the cosmic ray muon flux model, the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination is simulated multiple times to obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, including: for any a priori material combination, according to the a priori material combination, a geometric model of the object to be tested is established, and the geometric model represents the object to be tested when the a priori material combination is used; according to the cosmic ray muon flux model, the penetration process of cosmic ray muons in different track directions in the object to be tested using the a priori material combination is simulated multiple times on the geometric model using the Monte Carlo simulation technology, to obtain multiple groups of simulated muon track flux data corresponding to the a priori material combination. .

[0108] In a possible implementation, the method of determining a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to each a priori material combination of the plurality of a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured comprises: for any a priori material combination, determining a plurality of simulated muon minimum energy matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and the cosmic ray muon flux model; determining a plurality of simulated muon track length matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be measured; and determining a plurality of simulated muon stopping power matrices corresponding to the a priori material combination according to the plurality of simulated muon minimum energy matrices and the plurality of simulated muon minimum energy matrices.

[0109] In a possible implementation, the method of determining the degree of conformity of each a priori material combination with the actual material combination in the object to be tested based on the measured muon stopping power matrix and multiple simulated muon stopping power matrices corresponding to each a priori material combination in the multiple a priori material combinations includes: for any a priori material combination, determining the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used based on the simulated muon stopping power matrix corresponding to the a priori material combination and the voxel discrete grid corresponding to the object to be tested, the likelihood function corresponding to any voxel representing the probability that the voxel has different stopping powers under the material type contained in a given voxel; determining the posterior probability corresponding to the a priori material combination based on the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used and the measured muon stopping power matrix, the posterior probability representing the degree of conformity of the a priori material combination with the actual material combination in the object to be tested.

[0110] In a possible implementation, determining the posterior probability corresponding to the a priori material combination according to the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used and the measured muon stopping power matrix includes: establishing a material discrimination probability model based on Bayesian inference according to the likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used; solving the material discrimination probability model according to the measured muon stopping power matrix to obtain the posterior probability corresponding to the a priori material combination; wherein the material discrimination probability model is expressed as:

[0111]

[0112] Among them, m represents the prior material combination, m j represents the material type contained in the jth voxel, m j ∈m={m1,…,m J}, j∈{1,…,J}, J represents the total number of voxels in the object to be tested; f j represents the likelihood function corresponding to the jth voxel in the object to be tested when the prior material combination m is used, and p represents the likelihood function f j The stopping power variable, p j represents the measured stopping power corresponding to the jth voxel in the measured muon stopping power matrix, f j (p=p j |m j ) represents the value of j The next blocking power variable p is p j π(m) represents the prior probability preset for the prior material combination m; k represents the normalization factor.

[0113] In one possible implementation, the material identification result of the object to be tested is determined based on the degree of conformity between each prior material combination among the multiple prior material combinations and the actual material combination in the object to be tested, including: determining the prior material combination with the greatest degree of conformity as the material identification result of the object to be tested; or, optimizing the prior material combination with the greatest degree of conformity to obtain an optimized prior material combination, and re-determining the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested, until the degree of conformity between the optimized prior material combination and the actual material combination in the object to be tested reaches a preset requirement, and determining the prior material combination whose degree of conformity reaches the preset requirement as the material identification result of the object to be tested.

[0114] According to the device of the embodiment of the present disclosure, the measured muon track flux data of the object to be tested is measured by a muon detector to determine the measured muon stopping power matrix, and the simulated muon track flux data is obtained by simulating the penetration process of cosmic ray muons in the object to be tested with different priori material combinations. Then, based on the simulated muon track flux data, the simulated muon stopping power matrix corresponding to each priori material combination is determined. Then, by determining the degree of conformity between the simulated muon stopping power matrix corresponding to each priori material data and the measured muon stopping power matrix, the actual material combination inside the object to be tested is effectively identified based on the degree of conformity, that is, the material types actually contained in the object to be tested and the distribution areas of different material types are identified.

[0115] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0116] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0117] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0118] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0119] Fig. 9 1 is a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Fig. 9 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0120] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , Mac OS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0121] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0122] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0123] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0124] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0126] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0127] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0128] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0129] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0130] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A material identification method, characterized in that: include: Obtaining measured muon track flux data and a cosmic ray muon flux model of the object to be tested whose internal material is to be identified, wherein the measured muon track flux data include measured muon fluxes of cosmic ray muons in different track directions after penetrating the object to be tested, which are actually measured at different points by using a muon detector, and the cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before they are injected into the object to be tested; Determine a measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured, wherein the voxel discrete grid includes a plurality of voxels discretized from the object to be measured, and the measured muon stopping power matrix includes actual stopping powers of different voxels in the object to be measured for cosmic ray muons; Acquire multiple a priori material combinations of the object to be measured, and simulate the penetration process of cosmic ray muons in different track directions in the object to be measured using each a priori material combination for multiple times according to the cosmic ray muon flux model, and obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, each a priori material combination includes multiple estimated material types and distribution areas of different material types, and the simulated muon track flux data includes simulated muon fluxes in different track directions; Determine, according to multiple groups of simulated muon track flux data corresponding to each of the multiple a priori material combinations, the cosmic ray muon flux model, and the voxel discrete grid corresponding to the object to be measured, multiple simulated muon stopping power matrices corresponding to each a priori material combination, wherein each simulated muon stopping power matrix corresponding to each a priori material combination includes simulated stopping powers of different voxels in the object to be measured using each a priori material combination for cosmic ray muons; Determining the degree of conformity of each a priori material combination with the actual material combination in the object to be tested according to the measured muon stopping power matrix and a plurality of simulated muon stopping power matrices corresponding to each of the plurality of a priori material combinations, wherein the actual material combination includes a plurality of material types actually contained in the object to be tested and distribution areas of different material types; The material identification result of the object to be tested is determined according to the degree of conformity between each of the multiple priori material combinations and the actual material combination in the object to be tested, and the material identification result represents the actual material combination in the object to be tested.

2. The method according to claim 1, characterized in that Determining a measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model, and the voxel discrete grid corresponding to the object to be measured includes: Determine a measured muon minimum energy matrix according to the measured muon track flux data and the cosmic ray muon flux model, wherein the measured muon minimum energy matrix includes the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be measured and be detected by the muon detector; Determine a measured muon track length matrix according to the measured muon track flux data and the voxel discrete grid corresponding to the object to be measured, wherein the measured muon track length matrix includes track lengths of cosmic ray muons in different voxels in the object to be measured in different track directions; The measured muon stopping power matrix of the object to be measured is determined according to the measured muon minimum energy matrix and the measured muon minimum energy matrix.

3. The method according to claim 1, characterized in that According to the cosmic ray muon flux model, the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination is simulated multiple times to obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, including: For any a priori material combination, a geometric model of the object to be measured is established according to the a priori material combination, wherein the geometric model represents the object to be measured when the a priori material combination is used; The Monte Carlo simulation technology is used to simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using the a priori material combination on the geometric model according to the cosmic ray muon flux model, so as to obtain multiple groups of simulated muon track flux data corresponding to the a priori material combination.

4. The method according to claim 1 or 3, characterized in that: The method further comprises: determining a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to each a priori material combination in the plurality of a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured, comprising: For any a priori material combination, according to multiple groups of simulated muon track flux data corresponding to the a priori material combination and the cosmic ray muon flux model, determine multiple simulated muon minimum energy matrices corresponding to the a priori material combination; Determine a plurality of simulated muon track length matrices corresponding to the a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be measured; According to the multiple simulated muon minimum energy matrices and the multiple simulated muon minimum energy matrices, multiple simulated muon stopping power matrices corresponding to the a priori material combination are determined.

5. The method according to claim 1, characterized in that Determining the conformity of each a priori material combination with the actual material combination in the object to be tested according to the measured muon stopping power matrix and a plurality of simulated muon stopping power matrices corresponding to each a priori material combination in the plurality of a priori material combinations includes: For any a priori material combination, according to a plurality of simulated muon stopping power matrices corresponding to the a priori material combination and a voxel discrete grid corresponding to the object to be tested, a likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used is determined, wherein the likelihood function corresponding to any voxel represents the probability that the voxel has different stopping powers under the material type contained in the given voxel; According to the likelihood function corresponding to each voxel in the object to be tested when the priori material combination is used and the measured muon stopping power matrix, the posterior probability corresponding to the priori material combination is determined, and the posterior probability represents the degree of conformity between the priori material combination and the actual material combination in the object to be tested.

6. The method according to claim 5, characterized in that The determining, based on the likelihood function corresponding to each voxel in the object to be tested when the priori material combination is used and the measured muon stopping power matrix, the posterior probability corresponding to the priori material combination comprises: Determining a material discrimination probability model based on Bayesian inference according to the likelihood function corresponding to each voxel in the object to be tested when the prior material combination is adopted; Solving the material discrimination probability model according to the measured muon stopping power matrix to obtain the posterior probability corresponding to the priori material combination; Wherein, the material identification probability model is expressed as: Among them, m represents the prior material combination, m j represents the material type contained in the jth voxel, m j ∈m={m1,…,m J }, j∈{1,…,J}, J represents the total number of voxels in the object to be tested; f j represents the likelihood function corresponding to the jth voxel in the object to be tested when the prior material combination m is used, and p represents the likelihood function f j The stopping power variable, p j represents the measured stopping power corresponding to the jth voxel in the measured muon stopping power matrix, f j (p=p j |m j ) represents the value of j The next blocking power variable p is p j π(m) represents the prior probability preset for the prior material combination m; k represents the normalization factor.

7. The method according to claim 1, characterized in that The determining of the material identification result of the object to be tested according to the degree of conformity between each of the plurality of priori material combinations and the actual material combination in the object to be tested comprises: Determine the a priori material combination with the highest degree of compliance as the material identification result of the object to be tested; or, The prior material combination with the highest degree of compliance is optimized to obtain an optimized prior material combination, and the degree of compliance between the optimized prior material combination and the actual material combination in the object to be tested is re-determined until the degree of compliance between the optimized prior material combination and the actual material combination in the object to be tested reaches a preset requirement, and the prior material combination whose degree of compliance reaches the preset requirement is determined as the material identification result of the object to be tested.

8. A material screening device, characterized in that: include: An acquisition module is used to acquire the measured muon track flux data of the object to be tested whose internal material is to be identified and a cosmic ray muon flux model, wherein the measured muon track flux data include the measured muon flux of cosmic ray muons in different track directions after penetrating the object to be tested, which is actually measured at different points by using a muon detector, and the cosmic ray muon flux model includes the initial muon flux of cosmic ray muons in different track directions before they are injected into the object to be tested; A first determination module is used to determine a measured muon stopping power matrix corresponding to the object to be measured according to the measured muon track flux data, the cosmic ray muon flux model and the voxel discrete grid corresponding to the object to be measured, wherein the voxel discrete grid includes a plurality of voxels discretized from the object to be measured, and the measured muon stopping power matrix includes actual stopping powers of different voxels in the object to be measured for cosmic ray muons; A simulation module, used to obtain multiple a priori material combinations of the object to be tested, and according to the cosmic ray muon flux model, simulate the penetration process of cosmic ray muons in different track directions in the object to be tested using each a priori material combination for multiple times, to obtain multiple groups of simulated muon track flux data corresponding to each a priori material combination, each a priori material combination includes multiple estimated material types and distribution areas of different material types, and the simulated muon track flux data includes simulated muon fluxes in different track directions; A second determination module is used to determine a plurality of simulated muon stopping power matrices corresponding to each a priori material combination according to a plurality of groups of simulated muon track flux data corresponding to each a priori material combination in the plurality of a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured, wherein each simulated muon stopping power matrix corresponding to each a priori material combination includes simulated stopping powers of different voxels in the object to be measured using each a priori material combination for cosmic ray muons; A third determination module is used to determine the conformity of each a priori material combination with the actual material combination in the object to be tested according to the measured muon stopping power matrix and a plurality of simulated muon stopping power matrices corresponding to each of the plurality of a priori material combinations, wherein the actual material combination includes a plurality of material types actually contained in the object to be tested and distribution areas of different material types; The identification module is used to determine the material identification result of the object to be tested according to the degree of conformity between each of the multiple priori material combinations and the actual material combination in the object to be tested, wherein the material identification result represents the actual material combination in the object to be tested.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 7 when executing the instructions stored in the memory.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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