Radiation source positioning method and device, equipment, storage medium and program product

Abstract

The invention provides a radiation source positioning method, and relates to the technical field of radiation source detection. The method comprises the following steps: acquiring a geometric space model of a target space; collecting sampling data of a target space, wherein the sampling data comprises geometric space coordinate information and radiation counting rate information; constructing a system transmission matrix, wherein the system transmission matrix is used for representing radiation propagation characteristics of the target space; performing image reconstruction on the radiation field of the target space by using a maximum posteriori estimation method; and positioning the radiation source according to the reconstructed image. The invention further provides a radiation source positioning device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of radiation source detection technology, specifically to the field of radiation hotspot positioning technology, and more specifically to a radiation source positioning method, device, equipment, storage medium and program product. Background Art

[0002] The detection and positioning of radiation hotspots based on detectors without directional resolution can be mainly divided into: non-model detection and model-based detection. Among them, the non-model solution has poor adaptability to the environment, is only applicable to open scenes, and has low positioning efficiency; there are also many limitations for model-based detection solutions. For example, the model solution using a fixed network has requirements for the performance and position of each detector; the model solution using mobile detection MCMC requires the number of radiation sources to be predicted, the sampling acceptance rate is low, and the migration process is uncontrollable; the model solution using mobile detection particle filtering and the model solution using mobile detection Gaussian process regression, the efficiency is significantly reduced as the complexity of the environment increases. Therefore, how to propose a method for locating the coordinates of unknown point sources in an area has become a technical problem that needs to be solved urgently.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a radiation source positioning method, device, equipment, storage medium and program product that are simple to implement, highly efficient and environmentally adaptable.

[0005] According to a first aspect of the present disclosure, a radiation source positioning method is provided, the method comprising:

[0006] Obtaining a geometric space model of the target space;

[0007] Collecting sampling data of the target space, wherein the sampling data includes geometric space coordinate information and radiation count rate information;

[0008] Constructing a system transmission matrix, wherein the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space;

[0009] Reconstructing the radiation field of the target space using a maximum a posteriori estimation method; and

[0010] The radiation source is located according to the reconstructed image.

[0011] According to an embodiment of the present disclosure, the image reconstruction of the radiation field of the target space using the maximum a posteriori estimation method includes:

[0012] Using information entropy as prior information, define the prior probability distribution; and

[0013] The radiation field distribution is iteratively optimized using the maximum a posteriori estimation method to complete image reconstruction.

[0014] According to an embodiment of the present disclosure, the iterative optimization of the radiation field distribution using the maximum a posteriori estimation method to complete the image reconstruction includes:

[0015] Setting a priori information weight parameter and a cutoff parameter, wherein the cutoff parameter includes the number of iterations or a termination condition; and

[0016] The iterative calculation is performed according to the formula of the maximum a posteriori estimation method until the cutoff parameter is met, where the iterative formula is:

[0017]

[0018] in, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient.

[0019] According to an embodiment of the present disclosure, the construction of a system transmission matrix includes:

[0020] According to the geometric space model, the geometric calculation method is used to calculate the system transmission matrix elements under ideal open conditions. , ,in Voxel Center point and sampling point the square of the distance; and

[0021] Construct a system transfer matrix based on the system transfer matrix elements.

[0022] According to an embodiment of the present disclosure, the said constructing a system transmission matrix further comprises:

[0023] Identify obstacle types and raycast thicknesses in the geometric space model; and

[0024] The system transmission matrix is ​​modified according to the obstacle type and ray casting thickness.

[0025] According to an embodiment of the present disclosure, the geometric space model is created based on SLAM technology, and the SLAM technology includes one or more of laser SLAM, visual SLAM and multi-sensor fusion SLAM.

[0026] A second aspect of the present disclosure provides a radiation source positioning device, the device comprising:

[0027] An acquisition module, used to acquire a geometric space model of a target space;

[0028] A sampling module, used to collect sampling data of the target space, wherein the sampling data includes geometric space coordinate information and radiation count rate information;

[0029] A construction module, used to construct a system transmission matrix, wherein the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space;

[0030] an image reconstruction module, configured to reconstruct an image of the radiation field of the target space using a maximum a posteriori estimation method; and

[0031] The positioning module is used to locate the radiation source according to the reconstructed image.

[0032] According to an embodiment of the present disclosure, the image reconstruction module includes: a first determination submodule and an iterative optimization submodule.

[0033] A first determination submodule is used to define a priori probability distribution using information entropy as prior information; and

[0034] The iterative optimization submodule is used to iteratively optimize the radiation field distribution using the maximum a posteriori estimation method to complete image reconstruction.

[0035] According to an embodiment of the present disclosure, the iterative optimization submodule includes a setting unit and an iterative calculation unit.

[0036] A setting unit, configured to set a priori information weight parameter and a cutoff parameter, wherein the cutoff parameter includes the number of iterations or a termination condition; and

[0037] The iterative calculation unit is used to perform iterative calculation according to the formula of the maximum a posteriori estimation method until the cutoff parameter is satisfied, wherein the single-step iteration formula is:

[0038]

[0039] in, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient.

[0040] According to an embodiment of the present disclosure, the construction module includes a calculation submodule and a construction submodule.

[0041] The calculation submodule is used to calculate the system transmission matrix elements under ideal open conditions using a geometric calculation method according to the geometric space model. , ,in Voxel Center point and sampling point the square of the distance; and

[0042] The construction submodule is used to construct the system transmission matrix according to the system transmission matrix elements.

[0043] According to an embodiment of the present disclosure, the construction module further includes an identification submodule and a correction submodule.

[0044] an identification submodule for identifying obstacle types and ray casting thicknesses in a geometric space model; and

[0045] The correction submodule corrects the system transmission matrix according to the obstacle type and the ray projection thickness.

[0046] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned radiation source positioning method.

[0047] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned radiation source positioning method.

[0048] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned radiation source positioning method when executed by a processor.

[0049] A radiation source positioning method provided by an embodiment of the present disclosure obtains a geometric space model of the target space; collects sampling data of the target space, the sampling data including geometric space coordinate information and radiation count rate information; constructs a system transmission matrix, the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space; uses the maximum a posteriori estimation method to reconstruct an image of the radiation field of the target space; and positions the radiation source according to the reconstructed image. Compared with related technologies, the radiation source positioning method provided by the embodiment of the present disclosure does not require the number of point sources to be known in advance, can cope with emergency conditions, has higher efficiency, and better environmental adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0051] Figure 1 A schematic diagram showing an application scenario of a radiation source positioning method, apparatus, device, storage medium, and program product according to an embodiment of the present disclosure;

[0052] Figure 2 A flowchart of a radiation source positioning method provided according to an embodiment of the present disclosure is schematically shown;

[0053] Figure 3 One of the flow charts of a method for image reconstruction of the radiation field of the target space using the maximum a posteriori estimation method provided according to another embodiment of the present disclosure is schematically shown;

[0054] Figure 4 A second flowchart of a method for image reconstruction of the radiation field of the target space using a maximum a posteriori estimation method provided according to another embodiment of the present disclosure is schematically shown;

[0055] Figure 5 A structural block diagram of a radiation source positioning device according to an embodiment of the present disclosure is schematically shown; and

[0056] Figure 6 A block diagram of an electronic device suitable for implementing a radiation source positioning method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0057] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0058] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0059] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0060] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0061] Based on the above technical problems, an embodiment of the present disclosure provides a radiation source positioning method, which includes: acquiring a geometric space model of the target space; collecting sampling data of the target space, the sampling data including geometric space coordinate information and radiation counting rate information; constructing a system transmission matrix, which is used to characterize the radiation propagation characteristics of the target space; using the maximum a posteriori estimation method to reconstruct an image of the radiation field of the target space; and positioning the radiation source according to the reconstructed image.

[0062] Figure 1 The application scenario diagram of the radiation source positioning method, apparatus, device, storage medium and program product according to the embodiments of the present disclosure is schematically shown.

[0063] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a radiation source positioning scenario. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0064] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0065] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0066] Server 105 may be a back-end server, which may execute the radiation source positioning method provided in the embodiment of the present disclosure, and obtain a geometric space model of the target space in response to a network request from a client; collect sampling data of the target space, wherein the sampling data includes geometric space coordinate information and radiation count rate information; construct a system transmission matrix, wherein the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space; use a maximum a posteriori estimation method to perform image reconstruction of the radiation field of the target space; and position the radiation source according to the reconstructed image.

[0067] It should be noted that the radiation source positioning method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the radiation source positioning device provided in the embodiment of the present disclosure can generally be set in the server 105. The radiation source positioning method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the radiation source positioning device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0068] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0069] It should be noted that the radiation source positioning method and device determined in the embodiments of the present disclosure can be used in the field of radiation source detection technology, and can also be used in any field outside the field of radiation source detection technology. The application field of the radiation source positioning method and device determined in the embodiments of the present disclosure is not limited.

[0070] The following will be based on Figure 1 The application scenario described by Figure 2~Figure 6 The radiation source positioning method of the embodiment of the present disclosure is described in detail.

[0071] Figure 2 The flowchart of a radiation source positioning method provided according to an embodiment of the present disclosure is schematically shown. Figure 2 As shown, the radiation source positioning method of this embodiment includes operations S210 to S250, and the method can be executed by a server or other computing devices.

[0072] In operation S210, a geometric space model of a target space is acquired.

[0073] According to an embodiment of the present disclosure, the geometric space model is created based on SLAM technology, and the SLAM technology includes one or more of laser SLAM, visual SLAM and multi-sensor fusion SLAM.

[0074] In one example, the target space is the space where the radiation source to be located is located, that is, the working space of the embodiment of the present disclosure. First, the geometric space of the target space is mapped and modeled using a detector. The geometric space mapping and modeling can use SLAM (Simultaneous Localization and Mapping, SLAM) simultaneous positioning and map building methods, which can be compatible with laser SLAM, visual SLAM, and other SLAM methods. SLAM is a technology that estimates the position of a robot or unmanned system in real time through sensor data and builds an environmental map. In this embodiment, the method for obtaining the geometric space model is not limited, and the geometric space model built by other methods or devices can also be directly used.

[0075] In operation S220, sampling data of the target space is collected.

[0076] According to an embodiment of the present disclosure, the sampling data includes geometric space coordinate information and radiation count rate information.

[0077] In one example, the sampling data of each sampling point in the target space is collected. The number of sampling points can be adjusted according to the actual range of the target space. The sampling data includes the coordinates of each sampling point in the geometric space and the radiation count rate information of this point. Before all sampling is completed, as long as the environment and radiation source parameters do not change rapidly or significantly, the positioning method provided by the embodiment of the present disclosure can be used.

[0078] In operation S230, a system transmission matrix is ​​constructed.

[0079] According to an embodiment of the present disclosure, the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space.

[0080] According to an embodiment of the present disclosure, the geometric space model is used to calculate the system transmission matrix elements under ideal open conditions using a geometric calculation method. , ,in Voxel Center point and sampling point The square of the distance; construct the system transmission matrix based on the system transmission matrix elements.

[0081] According to an embodiment of the present disclosure, constructing the system transmission matrix further includes: identifying obstacle types and ray projection thicknesses in a geometric space model; and modifying the system transmission matrix according to the obstacle types and ray projection thicknesses.

[0082] In one example, the system transfer matrix is ​​obtained using a geometric calculation method, and the geometric calculation adopts the following formula (1):

[0083] (1)

[0084] In the formula, is the square of the distance between the center of voxel i and sampling point j.

[0085] In an example, since there are usually various obstacles in the target space, which will affect the accuracy of the sampled data, when there are obstacles, in order to make the constructed system transmission matrix more accurate, it is necessary to use the exponential decay law to Specifically, the obstacle type and ray projection thickness in the geometric space model are identified; and the corresponding exponential attenuation coefficient is determined according to the obstacle type and ray projection thickness, and then the system transmission matrix is ​​corrected. Through the system transmission matrix, the position of the radiation source can be more accurately located. Since the system transmission matrix in the embodiment of the present disclosure takes into account the influence of obstacles on radiation propagation, the accuracy of radiation measurement can be improved by correcting the propagation path and attenuation coefficient.

[0086] In operation S240, an image reconstruction is performed on the radiation field of the target space using a maximum a posteriori estimation method.

[0087] In operation S250, the radiation source is located according to the reconstructed image.

[0088] In one example, after constructing the system transmission matrix, the maximum a posteriori estimation method using information entropy as prior information is used to reconstruct the image of the geometric space model. The specific calculation process can be found in Figure 3 Operations S231 to S232 are not described in detail here.

[0089] A radiation source positioning method provided by an embodiment of the present disclosure obtains a geometric space model of the target space; collects sampling data of the target space, the sampling data including geometric space coordinate information and radiation count rate information; constructs a system transmission matrix, the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space; uses the maximum a posteriori estimation method to reconstruct an image of the radiation field of the target space; and positions the radiation source based on the reconstructed image. Compared with related point source positioning technologies, the radiation source positioning method provided by the embodiment of the present disclosure does not require the number of point sources to be known in advance, can cope with emergency conditions, is more efficient, and has better environmental adaptability.

[0090] Combine the following Figures 3-5 The process of reconstructing an image of the radiation field of the target space using the maximum a posteriori estimation method in the embodiment of the present disclosure is introduced. Figure 3One of the flowcharts of a method for image reconstruction of the radiation field of the target space using the maximum a posteriori estimation method provided according to another embodiment of the present disclosure is schematically shown. Figure 4 The second flowchart of the method for reconstructing the radiation field of the target space using the maximum a posteriori estimation method according to another embodiment of the present disclosure is schematically shown. Figure 3 As shown, operation S240 includes operation S241 to operation S242.

[0091] In operation S231, a priori probability distribution is defined using information entropy as priori information.

[0092] In operation S232, the radiation field distribution is iteratively optimized using a maximum a posteriori estimation method to complete image reconstruction.

[0093] like Figure 4 As shown, operation S232 includes operation S2321 to operation S2322.

[0094] In operation S2321, a priori information weight parameter and a cutoff parameter are set, where the cutoff parameter includes the number of iterations or a termination condition.

[0095] In operation S2322, iterative calculation is performed according to the formula of the maximum a posteriori estimation method until a cutoff parameter is satisfied.

[0096] According to an embodiment of the present disclosure, the iterative formula is:

[0097]

[0098] in, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient.

[0099] In an example, setting the prior information weight coefficient and cutoff parameter, prior information weight coefficient is a hyperparameter that controls the influence of prior information in the posterior estimate. It reflects the strength of our belief in the parameter before observing new data. The larger the weight coefficient, the stronger the role of prior information in the posterior estimate, and vice versa, the greater the influence of new data. By adjusting the weight coefficient, a balance can be achieved between prior knowledge and new data. The prior information weight coefficient can help the model generalize better to unseen data. By incorporating prior knowledge, the model can converge faster and reduce the risk of overfitting when faced with limited data.

[0100] In one example, the cutoff parameter includes the number of iterations or the termination condition, and the cutoff parameter is used to determine the condition when the algorithm stops iterating. The number of iterations refers to the maximum number of iterative steps allowed to be executed before the algorithm stops. Setting the number of iterations can prevent the algorithm from falling into an infinite loop in the process of finding the optimal solution, especially when the algorithm may not converge or converge slowly. The selection of the number of iterations is usually based on the scale, complexity and computing resource limitations of the problem. No specific restrictions are made in this embodiment.

[0101] In an example, the single-step iteration formula (2) of the maximum a posteriori method using information entropy as prior information is:

[0102] (2)

[0104] In the formula, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient, which controls the intensity of the effect.

[0105] The calculation method is as follows:

[0106]

[0107]

[0108]

[0109] In one example, the image reconstruction can be started by setting parameters such as β, the number of iterations or the termination condition. Finally, the coordinates of the unknown point sources in the space are located. In this embodiment, the mapping and modeling, sampling and image reconstruction of the geometric space are independent of each other, and the image reconstruction is offline.

[0110] The method provided by the embodiment of the present disclosure is simpler and more direct, more efficient, and more environmentally adaptable than non-model methods. Compared with the model solution of a fixed network, this method does not require the prediction of the number of point sources, and can cope with nuclear emergency, nuclear decommissioning and other working conditions. Compared with the model solution of mobile detection MCMC, this method does not require the prediction of the number of point sources, and the iteration progress and trend are easier to control, and clear termination conditions can be proposed. Compared with the model solutions of mobile detection particle filtering and Gaussian process regression, this method has higher efficiency and can be compatible with more complex scenarios. For complex environments, the calculation time will not be significantly longer, and positioning will not fail due to complex environments.

[0111] It should be noted that this method can be used not only in a plane, but also in a three-dimensional space. It has strong adaptability to the environment and can be used both outdoors and indoors. It can be used not only in open conditions, but also in scenes with obstacles, and the essence remains unchanged. This method has no restrictions on the type of detectors. It can be used on robots, as well as on drones, unmanned ships, surface ships, underwater submersibles and other equipment. This method can not only support sampling on mobile terminals, but also fixed detectors, as long as the accuracy of the sampling data is met. This method can be used not only on fully automated equipment, but also on handheld devices, remote control devices, and manually driven equipment, as long as the sampling conditions are met. This method is not limited to gamma radiation, but is also effective for neutron radiation.

[0112] Based on the above radiation source positioning method, the present disclosure also provides a radiation source positioning device. Figure 5 The device is described in detail.

[0113] Figure 5 The structure block diagram of a radiation source positioning device according to an embodiment of the present disclosure is schematically shown. Figure 5 As shown, the radiation source positioning device 500 of this embodiment includes an acquisition module 510 , a sampling module 520 , a construction module 530 , an image reconstruction module 540 and a positioning module 550 .

[0114] The acquisition module 510 is used to acquire the geometric space model of the target space. In one embodiment, the acquisition module 510 can be used to perform the operation S210 described above, which will not be described in detail here.

[0115] The sampling module 520 is used to collect sampling data of the target space, and the sampling data includes geometric space coordinate information and radiation count rate information. In one embodiment, the sampling module 520 can be used to perform the operation S220 described above, which will not be described in detail here.

[0116] The construction module 530 is used to construct a system transmission matrix, and the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space. In one embodiment, the construction module 530 can be used to perform the operation S230 described above, which will not be described in detail here.

[0117] The image reconstruction module 540 is used to reconstruct the radiation field of the target space using the maximum a posteriori estimation method. In one embodiment, the image reconstruction module 540 can be used to perform the operation S240 described above, which will not be described in detail here.

[0118] The positioning module 550 is used to position the radiation source according to the reconstructed image. In one embodiment, the positioning module 550 can be used to perform the operation S250 described above, which will not be described in detail here.

[0119] According to an embodiment of the present disclosure, the image reconstruction module includes: a first determination submodule and an iterative optimization submodule.

[0120] A first determination submodule is used to define a priori probability distribution using information entropy as prior information; and

[0121] The iterative optimization submodule is used to iteratively optimize the radiation field distribution using the maximum a posteriori estimation method to complete image reconstruction.

[0122] According to an embodiment of the present disclosure, the iterative optimization submodule includes a setting unit and an iterative calculation unit.

[0123] A setting unit, configured to set a priori information weight parameter and a cutoff parameter, wherein the cutoff parameter includes the number of iterations or a termination condition; and

[0124] The iterative calculation unit is used to perform iterative calculation according to the formula of the maximum a posteriori estimation method until the cutoff parameter is met, wherein the iterative formula is:

[0125]

[0126] in, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient.

[0127] According to an embodiment of the present disclosure, the construction module includes a calculation submodule and a construction submodule.

[0128] The calculation submodule is used to calculate the system transmission matrix elements under ideal open conditions using a geometric calculation method according to the geometric space model. , ,in Voxel Center point and sampling point the square of the distance; and

[0129] The construction submodule is used to construct the system transmission matrix according to the system transmission matrix elements.

[0130] According to an embodiment of the present disclosure, the construction module further includes an identification submodule and a correction submodule.

[0131] an identification submodule for identifying obstacle types and ray casting thicknesses in a geometric space model; and

[0132] The correction submodule corrects the system transmission matrix according to the obstacle type and the ray projection thickness.

[0133] According to an embodiment of the present disclosure, any multiple modules of the acquisition module 510, the sampling module 520, the construction module 530, the image reconstruction module 540 and the positioning module 550 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 510, the sampling module 520, the construction module 530, the image reconstruction module 540 and the positioning module 550 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the acquisition module 510, the sampling module 520, the construction module 530, the image reconstruction module 540 and the positioning module 550 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0134] Figure 6 A block diagram of an electronic device suitable for implementing a radiation source positioning method according to an embodiment of the present disclosure is schematically shown.

[0135] like Figure 6 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0136] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0137] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the I / O interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. The drive 909 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 909 as needed, so that the computer program read therefrom is installed into the storage portion 908 as needed.

[0138] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the radiation source positioning method according to the embodiment of the present disclosure is implemented.

[0139] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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 portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0140] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the radiation source positioning method provided by the embodiment of the present disclosure.

[0141] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0142] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0143] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0144] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0145] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession 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 or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0147] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A radiation source positioning method, characterized in that: The method comprises: Obtaining a geometric space model of the target space; Collecting sampling data of the target space, wherein the sampling data includes geometric space coordinate information and radiation count rate information; Constructing a system transmission matrix, wherein the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space; Reconstructing the radiation field of the target space using a maximum a posteriori estimation method; and The radiation source is located according to the reconstructed image.

2. The method according to claim 1, characterized in that The image reconstruction of the radiation field of the target space using the maximum a posteriori estimation method includes: Using information entropy as prior information, define the prior probability distribution; and The radiation field distribution is iteratively optimized using the maximum a posteriori estimation method to complete image reconstruction.

3. The method according to claim 2, characterized in that The method of iteratively optimizing the radiation field distribution using the maximum a posteriori estimation method to complete image reconstruction includes: Setting a priori information weight parameter and a cutoff parameter, wherein the cutoff parameter includes the number of iterations or a termination condition; and The iterative calculation is performed according to the formula of the maximum a posteriori estimation method until the cutoff parameter is met, where the single-step iteration formula is: in, For the The estimated value of the radiation hotspot image after iterations, is the sample data column vector, is the system transmission matrix, is the prior information based on information entropy, is the prior information weight coefficient.

4. The method according to claim 1, characterized in that The construction system transmission matrix includes: According to the geometric space model, the geometric calculation method is used to calculate the system transmission matrix elements under ideal open conditions. , , Voxel Center point and sampling point the square of the distance; and Construct a system transfer matrix based on the system transfer matrix elements.

5. The method according to claim 4, characterized in that The construction system transmission matrix also includes: Identify obstacle types and raycast thicknesses in the geometric space model; and The system transmission matrix is ​​modified according to the obstacle type and ray casting thickness.

6. The method according to any one of claims 1 to 5, characterized in that The geometric space model is created based on SLAM technology, and the SLAM technology includes one or more of laser SLAM, visual SLAM and multi-sensor fusion SLAM.

7. A radiation source positioning device, characterized in that: The device comprises: An acquisition module, used to acquire a geometric space model of a target space; A sampling module, used to collect sampling data of the target space, wherein the sampling data includes geometric space coordinate information and radiation count rate information; A construction module, used to construct a system transmission matrix, wherein the system transmission matrix is ​​used to characterize the radiation propagation characteristics of the target space; an image reconstruction module, configured to reconstruct an image of the radiation field of the target space using a maximum a posteriori estimation method; and The positioning module is used to locate the radiation source according to the reconstructed image.

8. An electronic device comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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