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

Abstract

The present disclosure relates to a material identification method, device, electronic device, and storage medium based on muon transmission imaging. The method comprises: obtaining measured muon track flux data and a cosmic ray muon flux model; determining a measured muon stopping power matrix based on the measured muon track flux data, the cosmic ray muon flux model, and a voxel discrete grid; repeatedly simulating the penetration process of cosmic ray muons in different track directions based on the cosmic ray muon flux model to obtain multiple sets of simulated muon track flux data; determining a simulated muon stopping power matrix based on the simulated muon track flux data, the cosmic ray muon flux model, and the voxel discrete grid; determining the degree of conformity of each a priori material combination based on the measured muon stopping power matrix and multiple simulated muon stopping power matrices for each a priori material combination; and determining a material identification result based on the degree of conformity of each a priori material combination. Thus, the actual material composition of the object to be tested can be effectively identified.

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 utilizes the high energy and strong penetration of cosmic ray muons to measure the flux attenuation of penetrating matter, enabling imaging of large objects such as mountains and buildings to study their internal structure and properties.

[0003] For example, in 2015, the ScanPyramids project team used a variety of detection technologies, including nuclear emulsion, scintillator, and gas detectors, to perform transmission imaging of the Great Pyramid of Khufu in Egypt, identifying multiple previously undiscovered void chambers. In 2023, the team used muon transmission imaging to image the Zaozigou underground mining area and the ancient city wall of Xi'an, revealing the density distribution of underground spaces and architectural structures. In addition to pyramids, muon transmission imaging has also been applied to structural imaging of volcanoes, caves, and buildings.

[0004] Current muon transmission imaging research primarily focuses on reconstructing material density, and analytical methods for identifying material types are lacking. Muon transmission imaging methods for material identification could facilitate the exploration of the morphology and structure of large buildings, and hold significant significance in archaeological and architectural conservation. On the other hand, large structures, such as mountains, are primarily constructed of materials with similar densities, such as brick, wood, earth, and stone. These materials, through which the muon flux of cosmic rays penetrates, are relatively low, making material identification more difficult and crucial. 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 probabilistic 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, the internal material of which 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, measured at different points by a muon detector, and the cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before entering the object to be tested; and obtaining a material identification method according to the measured muon track flux data, the cosmic ray muon flux model, and the object to be tested. The measured muon stopping power matrix corresponding to the object to be measured is determined by using a corresponding voxel discrete grid, 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 the actual stopping power of different voxels in the object to be measured for cosmic ray muons; 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, and each a priori material combination has a plurality of simulated muon track flux data corresponding to the a priori material combination. The method comprises the following steps: 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 and distribution areas of different material types, wherein the simulated muon track flux data comprises simulated muon fluxes in different track directions; 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, 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 comprises simulated stopping powers of different voxels in the object to be measured for cosmic ray muons using each a priori material combination; ; According to the measured muon stopping power matrix and the multiple simulated muon stopping power matrices corresponding to each of the multiple prior material combinations, the degree of conformity of each prior material combination with the actual material combination in the object to be tested is determined, and 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 prior material combinations with the actual material combination in the object to be tested, the material identification result of the object to be tested is determined, 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 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: determining a measured muon minimum energy matrix based on 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 a measured muon track length matrix based on 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; and determining the measured muon stopping power matrix of the object to be measured based on the measured muon minimum energy matrix and the measured muon minimum energy matrix.

[0008] In one possible implementation, the method comprises: for any one of the a priori material combinations, establishing a geometric model of the object to be measured based on the a priori material combination, wherein the geometric model represents the object to be measured when the a priori material combination is used; and simulating the penetration process of cosmic ray muons in different track directions in the object to be measured using the a priori material combination multiple times on the geometric model based on the cosmic ray muon flux model, thereby obtaining multiple sets 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 based on a plurality of sets of simulated muon track flux data corresponding to each a priori material combination, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured includes: for any a priori material combination, determining a plurality of simulated muon minimum energy matrices corresponding to the a priori material combination based on the plurality of sets 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 based on the plurality of sets of simulated muon track flux data corresponding to the a priori material combination and the 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 based on the plurality of simulated muon minimum energy matrices and the plurality of simulated muon minimum energy matrices.

[0010] In one 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, 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, a likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used, the likelihood function corresponding to each voxel representing the probability that the voxel has different stopping powers under the material type contained in a given voxel; and determining, 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, a posterior probability corresponding to the a priori material combination, 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 one possible implementation, 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 includes: determining a material discrimination probability model based on Bayesian inference based on 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 based on 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 j-th 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 ability 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, measured at different points by a muon detector, and the cosmic ray muon flux model comprises initial muon fluxes of cosmic ray muons in different track directions before entering the object to be detected; a first determination module for determining a material identification device based on the measured muon track flux data, the cosmic ray muon flux model, and the cosmic ray muon flux model; A voxel discrete grid corresponding to the object to be measured is used to determine the measured muon stopping power matrix 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 the actual stopping power of different voxels in the object to be measured for cosmic ray muons; a simulation module is used to obtain multiple a priori material combinations of the object to be measured, 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 measured 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, and each a priori material combination The method comprises the following steps: the simulated muon track flux data includes the simulated muon flux in different track directions; the simulated muon track flux data includes the simulated muon flux in different track directions; the second determining module is used to determine, based on the multiple sets 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, a plurality of simulated muon stopping power matrices corresponding to each a priori material combination, 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 for cosmic ray muons using each a priori material combination; 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 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, 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; a screening module is used to determine the material screening result of the object to be tested based on 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, where 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, a measured muon stopping power matrix is ​​determined by measuring the measured muon track flux data of the object to be measured by a muon detector, and 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, a 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 type 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 showing 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 meters and a height of 7 meters 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 a tunnel-type building 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 screening device according to an embodiment of the present disclosure is shown.

[0030] Figure 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 indicated.

[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 simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent 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," and the like in the claims, specification, and drawings of the present disclosure are used to distinguish between different objects, rather than to describe a specific order. The terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0035] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points 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 means that it can be connected to other devices through wireless connection methods such as wifi and Bluetooth. The terminal device involved in the embodiment of the present disclosure can also communicate with other devices through a wired connection function. The terminal device involved in the embodiment of the present disclosure can be a touch screen, a non-touch screen, or a screenless device. The touch screen device can be controlled by clicking, sliding, etc. 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, keyboard, touch panel, etc., and the terminal device can be controlled by the input device. For example, a device without a screen can be a Bluetooth speaker without a screen. For example, the terminal device of the present application can include but is not limited to user equipment (UE), mobile device, user terminal, terminal, handheld device, tablet computer, laptop computer, PDA, 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., with 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 function of wireless connection means that it can be connected 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 can 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 to display the material identification result of the object to be tested to the user in the terminal device.

[0038] Figure 1 The flow chart of the material identification method according to the embodiment of the present disclosure is shown. The method can be applied to the above-mentioned terminal device or server and other electronic devices. Figure 1 As shown, the method includes: steps S11 to S16.

[0039] In step S11, the measured muon track flux data of the object to be tested and the cosmic ray muon flux model of the internal material to be identified are obtained, wherein the measured muon track flux data include the 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 using a muon detector; the cosmic ray muon flux model includes the initial muon fluxes 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 produced by the interaction of high-energy primary cosmic rays from outer space with the Earth's atmosphere. Therefore, those skilled in the art can employ any known initial muon flux measurement technique to obtain the initial muon flux of cosmic ray muons in different track directions (i.e., different azimuth angles) before they enter the object under test. This initial muon flux can be understood as the muon flux before being blocked by the object under test. 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 cosmic ray muons in different track directions penetrate the object to be measured. Persons skilled in the art can manipulate the muon detector at multiple points around the object to be measured based on the actual structure of the object to be measured to detect the measured muon flux in different track directions (i.e., at different azimuth angles), which is not limited to this embodiment of the present disclosure. It should be understood that muons penetrating 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 power 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 based on 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, as Figure 2As shown, a portion of the area within the triangular building can be discretized to obtain a voxel discrete grid, where 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 measured, and this embodiment of the present disclosure is not limited to this.

[0045] In one 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] Based on 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 based on 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 to perform 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 track direction); 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 measured and be detected by the muon detector within the azimuth angle Ω, which is related to the material structure of the object to be measured.

[0052] Therefore, the minimum energy E of cosmic ray muons in each track direction can be calculated using formula (1) based on the actual 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 also the length of the muon track in that voxel, as Figure 2 As shown, M i,j is the length of the muon track in the jth voxel in the direction of the i-th track Ω, 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 can use any geometric algorithm known in the art. For example, they can 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 calculate the track lengths of cosmic ray muons in different voxels in the object to be measured in different track directions based on the measured muon track flux data and the voxel discrete grid corresponding to the object to be measured. This is not limited to the embodiments of the present disclosure.

[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 Relationship:

[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 is j is the stopping power of the j-th 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 direction of the i-th track 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 this is not limited in the embodiments of the present disclosure. The definition of stopping power is clearly known in the art, and the following brief explanation 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 in other words, the more cosmic ray muons are prevented from passing through, the greater the flux attenuation.

[0058] In step S13, multiple 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 multiple sets 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.

[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 the object 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. The internal structure has five layers: 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 shown may be for Figure 3 The geometric models of the tunnel-type building under the four a priori material combinations predicted are shown in Figures 4(a), 4(b), 4(c), and 4(d). From the outside in, the a priori material combinations are: cork-air-hardwood-air-rock, hardwood-air-cork-air-rock, cork-air-rock-air-hardwood, and rock-air-hardwood-air-cork. 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 a priori material combinations predicted for the object to be tested.

[0060] In one 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 measured using each a priori material combination. Specifically, based on 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 multiple sets 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 measured is established according to the a priori material combination, and the geometric model represents the object to be measured 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 using a priori material combinations 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 combinations 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 measured based on the prior material combination, wherein the established geometric model can characterize the object to be measured when the prior material combination is used, as well as the shape, size and other shape information of the object to be measured, which is equivalent to using the geometric model to simulate the object to be measured when the prior material combination is used. It should be understood that the embodiment of the present disclosure does not limit the modeling method of the geometric model of the object to be measured. For example, the above Figure 4(a) to Figure 4(d) The geometric model of a 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 based on the cosmic ray muon flux model multiple times. It can be understood that the Monte Carlo simulation technology is used to generate sampled muons in different track directions multiple times based on the cosmic ray muon flux model, and then each time the geometric model of the sampled muons and the object to be tested is 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 the priori material combination, so as to obtain multiple groups of simulated muon track flux data under the priori material combination, that is, to obtain the simulated muon flux of cosmic ray muons in different track directions after penetrating the object to be tested using the priori material combination, which is equivalent to simulating the flux attenuation of cosmic ray muons in different track directions when penetrating 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, based on multiple groups of simulated muon track flux data corresponding to each of the multiple 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 priori material combination are determined. Each simulated muon stopping power matrix corresponding to each priori material combination includes the simulated stopping power of different voxels in the object to be measured using each priori material combination for cosmic ray muons.

[0067] 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, the cosmic ray muon flux model, and the voxel discrete grid corresponding to the object to be measured can be used to determine the multiple simulated muon stopping power matrices corresponding to each a priori material combination. The method may include:

[0068] For any a priori material combination, multiple sets 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 sets of simulated muon minimum energy matrices corresponding to the a priori material combination. The simulated muon minimum energy matrices represent the minimum energy required for cosmic ray muons in different track directions to penetrate the object to be detected using the a priori material combination and be detected by the muon detector.

[0069] Determining multiple simulated muon track length matrices corresponding to the a priori material combination based on multiple sets of simulated muon track flux data corresponding to the a priori material combination and the voxel discrete grid corresponding to the object to be measured; wherein the simulated muon track length matrices represent the track lengths of cosmic ray muons in different voxels in the object to be measured using the a priori material combination in different track directions;

[0070] Based on 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 The simulated muon track length matrix corresponding to the 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 the a priori material combination, wherein a simulated muon stopping power matrix is ​​determined based on the simulated muon minimum energy matrix and the simulated muon minimum energy matrix calculated based on the same set of simulated muon track flux data. It should be understood that several simulated muon stopping power matrices can be calculated based on several sets of simulated muon track flux data, which will not be described in detail here.

[0072] In step S15, based on the measured muon stopping power matrix and multiple simulated muon stopping power matrices corresponding to each of the multiple prior material combinations, the degree of conformity of each prior material combination with the actual material combination in the object to be tested is determined. 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 one 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 of the multiple a priori material combinations may include:

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

[0075] 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 is determined. 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 calculated in steps S13 to S14 above. That is, multiple simulated stopping powers can be simulated and calculated for each voxel (or multiple simulated muon stopping power matrices can be simulated and calculated for each a priori material combination). Based on the multiple simulated stopping powers corresponding to each voxel and the material type contained in each voxel, a likelihood function f(p|m) corresponding to each voxel in the object under test for 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 construct the likelihood function corresponding to each voxel, and this is not limited to the embodiments of the present disclosure.

[0077] Bayesian inference can be used to obtain 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 can include:

[0078] Determine 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 a priori material combinations are used;

[0079] According to the measured muon stopping power matrix, the material discrimination 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 j-th 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 ability variable p is p j; π(m) represents the prior probability preset for the prior material combination m, π(m) is usually determined based on 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 in the embodiments of the present disclosure.

[0083] The above-mentioned method for determining the posterior probability can be understood as first establishing the material discrimination probability model based on Bayesian inference, 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 for 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 can also use 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 can be calculated, and the similarity can 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 based on the degree of conformity between each of the multiple 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 approach can be understood as excluding inconsistent prior material combinations and directly determining the prior material combination with the greatest degree of conformity (e.g., 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 prior material combination with the highest degree of compliance screened out 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 prior material combination in the multiple prior material combinations may include: optimizing the prior material combination with the highest degree of compliance to obtain an optimized prior material combination, and re-determining the degree of compliance between the optimized prior material combination and the actual material combination in the object to be tested, 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 determining the prior 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 embodiment of the present disclosure does not limit the optimization method. For example, the prior material combination can be optimized using a search algorithm known in the art, and the embodiment of the present disclosure does 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 priori 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 priori 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 priori material combination can be optimized again until the degree of conformity between the iteratively optimized priori material combination and the actual material combination in the object to be tested meets the preset requirements, and the priori 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, those skilled in the art can customize the specific content of the preset requirements according to actual needs. For example, if the degree of compliance is represented 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 being 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 represented 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 being 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 do not limit 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 is 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 type 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 in the image reconstruction is obtained through Monte Carlo simulation calculation. Combined with the Bayesian statistical method, the imaging results under different prior material combinations are optimized and iterated, and the most probable material composition is finally 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, 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 tracks and fluxes (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 obtain the measured muon stopping power matrix. The Monte Carlo simulation experiment 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), 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. Among them, 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 from 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 using measured muon tracks, cosmic ray muon angular distribution and flux, and a priori building structural materials as inputs, Monte Carlo simulation and Bayesian inference are combined to generate posterior probabilities corresponding to different a priori material combinations, thereby enabling the identification of the building material composition. Furthermore, by iteratively comparing the posterior probabilities under different a priori material combinations, a priori material combinations that do not match the actual material composition can be eliminated and the most probable a priori material combination can be determined, thereby achieving material identification.

[0095] Based on the material identification method of the 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 structure 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 count of 4 days at sea level, 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 Figures 7(a), 7(b), 7(c), and 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 consistency.

[0098] In summary, 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 includes:

[0100] An acquisition module 801 is configured to acquire measured muon track flux data and a cosmic ray muon flux model of an object to be tested whose internal material is to be identified. 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, as measured at different points using a muon detector. The cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before entering the object to be tested.

[0101] A first determining module 802 is configured to determine a 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 a 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 within the object to be measured for cosmic ray muons;

[0102] A simulation module 803 is configured to obtain multiple a priori material combinations of the object to be measured, and based on the cosmic ray muon flux model, simulate multiple times the penetration process of cosmic ray muons in different track directions through the object to be measured using each a priori material combination, thereby obtaining multiple sets of simulated muon track flux data corresponding to each a priori material combination, wherein 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 determining module 804 is configured to determine, based on the multiple sets 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 for cosmic ray muons using each a priori material combination;

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

[0105] The identification module 806 is used to determine the material identification result of the object to be tested based on the degree of conformity between each of the multiple prior 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.

[0106] 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: determining a measured muon minimum energy matrix based on 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 a measured muon track length matrix based on 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; and determining the measured muon stopping power matrix of the object to be measured based on the measured muon minimum energy matrix and the measured muon minimum energy matrix.

[0107] In one possible implementation, based on the cosmic ray muon flux model, multiple simulations are performed on the penetration process of cosmic ray muons in different track directions through a test object using each a priori material combination to obtain multiple sets of simulated muon track flux data corresponding to each a priori material combination. This includes: for each a priori material combination, establishing a geometric model of the test object based on the a priori material combination, wherein the geometric model represents the test object when the a priori material combination is used; and using Monte Carlo simulation techniques, based on the cosmic ray muon flux model, multiple simulations are performed on the geometric model on the penetration process of cosmic ray muons in different track directions through the test object using the a priori material combination to obtain multiple sets 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 based on a plurality of sets of simulated muon track flux data corresponding to each a priori material combination, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured includes: for any a priori material combination, determining a plurality of simulated muon minimum energy matrices corresponding to the a priori material combination based on the plurality of sets 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 based on the plurality of sets of simulated muon track flux data corresponding to the a priori material combination and the 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 based on the plurality of simulated muon minimum energy matrices and the plurality of simulated muon minimum energy matrices.

[0109] In one 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, 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, a likelihood function corresponding to each voxel in the object to be tested when the a priori material combination is used, the likelihood function corresponding to each voxel representing the probability that the voxel has different stopping powers under the material type contained in a given voxel; and determining, 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, a posterior probability corresponding to the a priori material combination, 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 one possible implementation, 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 includes: establishing a material discrimination probability model based on Bayesian inference based on 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 based on 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 j-th 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 ability 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 measured 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 measured 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 measured is effectively identified based on the degree of conformity, that is, the material type actually contained in the object to be measured 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. For the sake of brevity, it will not be repeated here.

[0116] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium may 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] An embodiment of the present disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying 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] Figure 9 FIG1 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server or a terminal device. Figure 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 the instructions to perform the above-described method.

[0120] The electronic device 1900 may further 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 that 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, method and / or 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 can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can 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 thereof. More specific examples (a non-exhaustive list) of computer-readable storage media 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 disc 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 thereof. As used herein, a computer-readable storage medium is not to be construed 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 an electrical 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, fiber optic transmission, wireless transmission, 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 to be stored in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely 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 entirely 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., utilizing 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 personalized by utilizing the state information of the computer-readable program instructions. 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 flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, 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 is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks 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 operational 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 blocks in the flowchart and / or block diagram.

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

[0130] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A material screening method, characterized in that: include: Obtaining measured muon track flux data and a cosmic ray muon flux model of an object to be tested whose internal material is to be identified, 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, as actually measured at different points using a muon detector, and the cosmic ray muon flux model includes initial muon fluxes of cosmic ray muons in different track directions before entering the object to be tested; determining a 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 a 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; Acquiring multiple a priori material combinations of the object to be measured, and simulating the penetration process of cosmic ray muons in different track directions in the object to be measured using each a priori material combination multiple times based on the cosmic ray muon flux model, to obtain multiple sets of simulated muon track flux data corresponding to each a priori material combination, wherein 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; Determining, based on multiple sets of simulated muon track flux data corresponding to each of the multiple a priori material combinations, the cosmic ray muon flux model, and a voxel discrete grid corresponding to the object to be measured, multiple simulated muon stopping power matrices corresponding to each a priori material combination, each simulated muon stopping power matrix corresponding to each a priori material combination including simulated stopping powers of different voxels in the object to be measured for cosmic ray muons using each a priori material combination; determining, based on 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, a degree of conformity between each a priori material combination and an actual material combination within the object to be tested, the actual material combination including the plurality of material types actually contained in the object to be tested and the distribution areas of the different material types; 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.

2. The method according to claim 1, characterized in that Determining a 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 a voxel discrete grid corresponding to the object to be measured includes: Determining a measured muon minimum energy matrix based on 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; Determining a measured muon track length matrix based on the measured muon track flux data and a 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 sets 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 technique 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, and multiple groups of simulated muon track flux data corresponding to the a priori material combination are obtained.

4. The method according to claim 1 or 3, characterized in that The step of determining, based on the multiple sets 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 includes: For any a priori material combination, determining a plurality of simulated muon minimum energy matrices corresponding to the a priori material combination based on a plurality of sets 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 based on the plurality of groups of simulated muon track flux data corresponding to the a priori material combination and the voxel discrete grid corresponding to the object to be measured; A plurality of simulated muon stopping power matrices corresponding to the a priori material combination are determined based on the plurality of simulated muon minimum energy matrices and the plurality of simulated muon minimum energy matrices.

5. The method according to claim 1, characterized in that Determining, based on 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, the degree of conformity of each a priori material combination with the actual material combination in the object to be tested includes: For any a priori material combination, determining, based on multiple simulated muon stopping power matrices corresponding to the a priori material combination and a discrete voxel 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, wherein the likelihood function corresponding to each voxel represents the probability that the voxel has different stopping powers under the material type contained in the given voxel; 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 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 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 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 prior material combination is adopted; Solving the material discrimination probability model based on the measured muon stopping power matrix to obtain the posterior probability corresponding to the prior 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 j-th 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 ability 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 Determining the material identification result of the object to be tested based on the degree of conformity between each of the multiple priori material combinations and the actual material combination in the object to be tested includes: Determine the a priori material combination with the greatest degree of compliance as the material screening result of the object to be tested; or, The priori material combination with the highest degree of compliance is optimized to obtain an optimized priori material combination, and the degree of compliance of the optimized priori material combination with the actual material combination in the object to be tested is re-determined until the degree of compliance of the optimized priori material combination with the actual material combination in the object to be tested reaches the preset requirements, and the priori material combination whose degree of compliance reaches the preset requirements is determined as the material screening result of the object to be tested.

8. A material screening device, characterized in that: include: an acquisition module, configured to acquire measured muon track flux data and a cosmic ray muon flux model of an object to be tested whose internal material is to be identified, wherein the measured muon track flux data includes the measured muon fluxes of cosmic ray muons in different track directions after penetrating the object to be tested, as actually measured at different points using a muon detector; and the cosmic ray muon flux model includes the initial muon fluxes of cosmic ray muons in different track directions before entering the object to be tested; a first determining module, configured to determine a 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 a 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 within the object to be measured for cosmic ray muons; a simulation module for obtaining multiple a priori material combinations of the object to be measured, and, based on the cosmic ray muon flux model, repeatedly simulating the penetration process of cosmic ray muons in different track directions in the object to be measured using each a priori material combination, to obtain multiple sets of simulated muon track flux data corresponding to each a priori material combination, wherein 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 determining module for determining, based on multiple sets 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 for cosmic ray muons using each a priori material combination; a third determination module, configured to determine, based on 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, a degree of conformity between each a priori material combination and an actual material combination within the object to be tested, wherein the actual material combination includes the plurality of material types actually contained in the object to be tested and the distribution areas of the different material types; The identification module is used to determine the material identification result of the object to be tested based on the degree of conformity between each of the multiple prior 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 according to 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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