A method of equivalent scaling of a probe energy deposition profile and related apparatus

Abstract

The application discloses an equivalent conversion method for energy deposition distribution of a detector and a related device, belongs to the technical field of radiation detection and data processing, and comprises the following steps: obtaining an energy deposition data set of a detector and calculating a first moment parameter; determining a conversion coefficient; converting the first moment parameter into a second moment parameter by using the conversion coefficient; inputting the data set into a sinh-arcsinh conversion function containing an optimization parameter after scaling and standardizing the data set; constructing a loss function for quantifying the difference between the first high-order moment parameter and the second high-order moment parameter in the second moment parameter and iteratively optimizing; and performing linear scaling on the optimization result to obtain a final conversion result data set. The application can realize equivalent conversion of the energy deposition distribution to a target medium or a target size without replacing the detector material or reducing the size of the detector, and effectively avoids measurement deviation caused by material difference and size limitation.

Description

Technical Field

[0001] This invention relates to the field of radiation detection and data processing technology, and in particular to an equivalent conversion method and related apparatus for energy deposition distribution of a detector. Background Technology

[0002] The distribution of energy deposition in a medium is an important physical quantity in the field of radiation measurement. It characterizes the spatial distribution of the radiation field and the response properties of the measured medium, playing a crucial role in particle physics, radiation protection, and radiotherapy. Currently, conventional measurement methods mainly fall into two categories: (1) Direct measurement method: Measurement is performed in the relevant physical field using detectors such as ionization chambers and semiconductors. However, due to the differences in the materials and sizes of the detectors themselves, detectors of different sizes or materials are often required under different conditions in order to achieve accurate measurement and conversion of energy deposition distribution.

[0003] (2) Monte Carlo simulation method: The deposition energy distribution in the medium is directly calculated by simulating particle transport by computer. Theoretically, the results in any medium material can be obtained, and the calculation results have a high degree of agreement with the experimental results. It is considered the "gold standard".

[0004] In summary, the methods described above have certain limitations in some application scenarios. For example, when the detector size is much larger than the area of ​​interest, and manufacturing processes prevent further reduction in detector size, the energy deposition distribution measured by the large-size detector cannot accurately reflect the energy deposition situation in a small-scale region. Furthermore, when there is a significant difference between the detector material and the target medium, the energy deposition distribution in the detector cannot be directly equated to the actual deposition situation in the target medium, thus making it impossible to accurately determine the actual energy deposition characteristics in the target medium based on the measurement results. This is common in radiation protection and radiotherapy. In radiotherapy plan validation, it is often necessary to obtain the energy deposition distribution in biological tissues (usually approximated by water). However, direct measurement is often limited by detector size and materials, leading to biased results and affecting the assessment of radiotherapy dose. Monte Carlo simulation methods are computationally intensive and cannot meet the requirements for real-time or rapid feedback. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of the prior art and to provide an equivalent conversion method and related apparatus for energy deposition distribution of detectors.

[0006] The objective of this invention is achieved through the following technical solution: an equivalent conversion method for detector energy deposition distribution, comprising the following steps: Deposition energy is measured using a detector in the target environment to obtain an energy deposition dataset; Calculate the first moment parameters of the energy deposition dataset; The conversion factor is determined based on the physical scale or physical material relationship between the detector and the target object; The second moment parameter corresponding to the energy deposition distribution of the target object is calculated by converting the first moment parameter using a conversion factor. The energy deposition dataset is scaled proportionally according to the conversion factor to obtain an intermediate dataset; The intermediate dataset is standardized to obtain a standardized intermediate dataset. The standardized intermediate dataset is input into the sinh-arcsinh transformation function, which includes the optimization parameters, to obtain the transformed dataset; For any combination of optimization parameters, calculate the first higher-order moment parameters of the transformed dataset and construct a loss function, which is used to quantify the difference between the first higher-order moment parameters and the second higher-order moment parameters in the second moment parameters; With the goal of minimizing the loss function, the optimization parameters are iteratively adjusted, and the transformed dataset corresponding to the minimum value of the loss function is used as the optimized transformation result dataset. Calculate the first low-order moment parameter of the optimized transformation result dataset, and use the first low-order moment parameter and the second low-order moment parameter of the second moment parameter to linearly scale the optimized transformation result dataset to obtain the final transformation result dataset.

[0007] In one example, the first moment parameter and the second moment parameter both include mean, standard deviation, skewness, and excess kurtosis; the first higher-order moment parameter and the second higher-order moment parameter both include skewness and excess kurtosis; and the first lower-order moment parameter and the second lower-order moment parameter both include mean and standard deviation.

[0008] In one example, the conversion factor is determined based on the physical scale or material relationship between the detector and the target object, including: When the conversion between the detector and the target object is across dimensions, the conversion factor is the ratio of the target object size to the detector size; when the conversion between the detector and the target object is across media, the conversion factor is the ratio of the mass resistance of the target medium to the mass resistance of the detector material.

[0009] In one example, the expression for calculating the second moment parameter is: , , , ; in, , , and These are the mean, standard deviation, skewness, and excess kurtosis in the first moment parameters, respectively. These are conversion factors; , , and These are the mean, standard deviation, skewness, and excess kurtosis in the second moment parameters, respectively.

[0010] In one example, the transformed dataset for: ; in, It is a hyperbolic sine function; It is an inverse hyperbolic sine function; , To find the optimal parameters; To standardize the intermediate dataset.

[0011] In one example, the loss function for: ; in, , To find the optimal parameters; , These are the skewness and excess kurtosis in the first higher-order moment parameters, respectively. , These are the skewness and excess kurtosis in the second higher-order moment parameters, respectively.

[0012] In one example, the final transformed dataset The calculation expression is: ; in, , These are the mean and standard deviation of the first lower-order moment parameters; , These are the mean and standard deviation of the second lowest-order moment parameters; To optimize the transformed dataset.

[0013] It should be further noted that the technical features corresponding to the above examples can be combined or replaced to form new technical solutions.

[0014] The present invention also includes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the equivalent conversion method for a detector energy deposition distribution formed by any or a combination of the above examples.

[0015] The present invention also includes a storage medium storing computer instructions that, when executed, perform the steps of the equivalent conversion method for detector energy deposition distribution formed by any or more of the above examples.

[0016] The present invention also includes a terminal comprising a memory and a processor, the memory storing computer instructions executable on the processor, wherein the processor, when executing the computer instructions, performs the steps of the equivalent conversion method for detector energy deposition distribution formed by any or more of the above examples.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. Achieving accurate conversion of energy deposition distribution across size and medium. Based on the variation of energy deposition distribution across different detector sizes, this invention establishes a mapping relationship between the detector's deposited energy distribution and the target distribution. This enables the conversion of measurement results from large-size detectors to equivalent results from smaller-size detectors. Even when existing detectors are difficult to further reduce in size, it can still obtain energy deposition distributions closer to actual small-scale regions. Simultaneously, this invention can equivalently convert the energy deposition distribution in detectors made of specific materials to the energy deposition in the target medium (e.g., water or other tissue-equivalent materials), avoiding measurement biases caused by material differences. This invention is applicable to scenarios such as radiation protection and medical irradiation verification.

[0018] 2. It can maintain the spatial shape and detailed features of the energy distribution. This invention calculates moment parameters and uses higher-order distribution feature parameters as optimization targets to adjust the optimization parameters so that the final transformed dataset has the same mean and standard deviation as the target result. Moreover, the skewness and excess kurtosis of the final transformed dataset approximate the corresponding parameters of the target distribution. This can largely maintain the spatial variation trend and detailed structure of the energy deposition distribution, ensuring that the converted energy deposition distribution strictly conforms to the physical non-negativity and statistical laws of radiation energy deposition, avoiding distortion of the distribution shape, and guaranteeing the accuracy and reliability of the conversion results.

[0019] 3. Reduced experimental constraints and improved measurement flexibility. The cross-size and cross-medium equivalent conversion method proposed in this invention can obtain the equivalent energy distribution in the target size or target medium through mathematical calculation without changing the detector material or reducing the detector size. This significantly reduces the experimental constraints caused by limited equipment selection and improves measurement flexibility.

[0020] 4. Wide applicability, covering various detection scenarios. This invention's method is applicable to dose conversion from ionization chambers or semiconductor detectors to water-equivalent tissues in radiotherapy, and also to energy deposition assessment from monitoring detectors to critical human organs in radiation protection, demonstrating its broad applicability. Equivalent conversion can be performed simply by setting the equivalent conversion coefficients and moment parameters of the target result according to the measurement scenario, avoiding the repetitive work of developing separate correction algorithms for different detectors and media. Attached Figure Description

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The accompanying drawings are provided to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to denote the same or similar parts. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application.

[0022] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention; Figure 2 [100 μm] provided as an embodiment of the present invention 3 The deposition energy distribution in large and small diamond detectors is oriented towards [75 μm]. 3 A schematic diagram showing the equivalent conversion results of large and small diamond detectors; Figure 3 [100 μm] provided as an embodiment of the present invention 3 The energy distribution of deposited particles in large and small diamond detectors is oriented towards [25 μm]. 3 A schematic diagram showing the equivalent conversion results of large and small diamond detectors; Figure 4 [100 μm] provided as an embodiment of the present invention 3 The energy distribution of deposited particles in large and small diamond detectors is directed towards [100 μm]. 3 A schematic diagram showing the equivalent conversion results of large and small biological tissues; Figure 5 [25 μm] provided for one embodiment of the present invention 3 The energy distribution of deposited particles in large and small diamond detectors is oriented towards [25 μm]. 3 A schematic diagram showing the equivalent conversion results of biological tissues of different sizes. Detailed Implementation

[0023] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, ordinal numbers (e.g., "first and second," etc.) are used to distinguish objects and are not limited to this order, nor should they be construed as indicating or implying relative importance. Furthermore, the technical features involved in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0025] This invention proposes an equivalent conversion method for detector energy deposition distribution based on sinh-arcsinh expansion. According to the correlation between target moments of deposition energy distribution in different sizes and media, a mapping relationship between detector results and target results is established. Then, the detector measurement results are equivalently converted according to the mapping relationship to obtain the deposition energy distribution in the target size or target medium.

[0026] In one embodiment, such as Figure 1 As shown, an equivalent conversion method for detector energy deposition distribution is provided, which includes the following steps: S10: Utilize the detector to measure the deposition energy in the target environment and obtain an energy deposition dataset.

[0027] In step S10, the detector is placed in the radiation field, and the energy value deposited each time a particle interacts with the detector is recorded. Multiple energy deposition values ​​form an energy deposition dataset. Y .

[0028] S20: Calculate the first moment parameter of the energy deposition dataset.

[0029] In step S20, the first moment parameter includes the mean. Standard deviation skewness and excess kurtosis The mean represents the average level of energy deposition; the standard deviation is calculated by taking the square root of the average of the squares of the differences between each energy deposition value and the mean, and is used to represent the dispersion of energy deposition relative to the mean; the skewness is calculated by dividing the average of the cubes of the differences between each data point and the mean by the cube of the standard deviation, and is used to represent the asymmetry of the energy deposition distribution; the excess kurtosis is calculated by dividing the average of the fourth power of the differences between each data point and the mean by the fourth power of the standard deviation and then subtracting 3, and is used to represent the tail thickness of the energy deposition distribution relative to the normal distribution.

[0030] S30: Determine the conversion factor based on the physical scale or material relationship between the detector and the target object. n .

[0031] In step S30, the conversion factor is used to quantify the proportional relationship between the energy deposition measurement results of the detector and the actual energy deposition results of the target object.

[0032] S40: Using conversion factors to convert the first moment parameter, calculate the second moment parameter corresponding to the energy deposition distribution X of the target object.

[0033] In step S40, the second moment parameter includes the mean. Standard deviation skewness and excess kurtosis The mean and standard deviation are lower-order moment parameters, while skewness and excess kurtosis are higher-order moment parameters.

[0034] S50: Scale the energy deposition dataset according to the conversion factor to obtain an intermediate dataset.

[0035] In step S50, based on the conversion factor... n Energy deposition dataset Y To perform proportional scaling, the calculation expression is: To obtain the intermediate dataset W .

[0036] S60: Standardize the intermediate dataset to obtain a standardized intermediate dataset.

[0037] In step S60, the intermediate dataset is calculated. W mean and standard deviation Then, on the intermediate dataset W Standardization is performed to obtain a standardized intermediate dataset. The calculation expression is: .

[0038] S70: Input the standardized intermediate dataset into the sinh-arcsinh transformation function, which includes the optimization parameters, to obtain the transformed dataset.

[0039] S80: For any combination of optimization parameters, calculate the first higher-order moment parameters of the transformed dataset and construct a loss function. The loss function is used to quantify the difference between the first higher-order moment parameters and the second higher-order moment parameters in the second set of moment parameters.

[0040] In step S80, the first higher-order moment parameter is the transformed dataset. Sample skewness and sample excess kurtosis For any set of optimization parameters, transform the dataset. Sample skewness and sample excess kurtosis It varies depending on the value of the optimization parameter.

[0041] S90: With the goal of minimizing the loss function, iteratively adjust the values ​​of the optimization parameters, and use the transformed dataset corresponding to the minimum value of the loss function as the optimized transformation result dataset.

[0042] In step S90, a numerical optimization algorithm is used to continuously try to find the optimal combination of parameters to transform the dataset. Sample skewness and sample excess kurtosis The algorithm gradually approaches the target value. In each iteration, the loss function value under the current optimization parameters is calculated, and the optimization parameters are adjusted based on the feedback from the loss function until the loss function reaches its minimum value. The corresponding transformed dataset at this point... Sample skewness and sample excess kurtosis It is closest to the target moment parameter.

[0043] S100: Calculate the first low-order moment parameter of the optimized transformation result dataset, and use the first low-order moment parameter and the second low-order moment parameter in the second moment parameter to linearly scale the optimized transformation result dataset to obtain the final transformation result dataset.

[0044] The first and second lower-order moment parameters are both the mean and standard deviation; the optimized transformation dataset is calculated. current mean and standard deviation Then, a linear transformation is performed on each data value in the optimized transformation result dataset to make the mean of the optimized transformation result dataset... Equal to the target mean in the second moment parameters Optimize the standard deviation of the transformed dataset Equal to the target standard deviation in the second moment parameters This yields the final transformed dataset.

[0045] In one embodiment, determining the conversion factor based on the physical scale relationship or physical material relationship between the detector and the target object includes: When the conversion between the detector and the target object is across dimensions, the conversion factor is the ratio of the target object size to the detector size; when the conversion between the detector and the target object is across media, the conversion factor is the ratio of the mass resistance of the target medium to the mass resistance of the detector material.

[0046] Typically, the deposition energy in a medium can be calculated by integrating the linear stopping power over the transmission distance. Since the transmission distance of heavily charged particles in a medium is approximately equal to the geometric dimensions of the medium, the conversion factor between deposition energies of different sizes of media can be expressed as the size ratio of the two. For different media of the same size, the ratio of their linear stopping powers can be used.

[0047] In one embodiment, the expression for calculating the second moment parameter is: , , , ; in, , , and These are the mean, standard deviation, skewness, and excess kurtosis in the first moment parameters, respectively. These are conversion factors; , , and These are the mean, standard deviation, skewness, and excess kurtosis in the second moment parameters, respectively.

[0048] In one embodiment, the transformed dataset for: ; in, It is a hyperbolic sine function; It is an inverse hyperbolic sine function; , To find the optimal parameters; To standardize the intermediate dataset.

[0049] In one embodiment, the loss function for: ; in, , These are the skewness and excess kurtosis in the first higher-order moment parameters, respectively. , These are the skewness and excess kurtosis in the second higher-order moment parameters, respectively. , To find the optimal parameters, for any given ( b , c ), calculate the transformed dataset Sample skewness and sample excess kurtosis And find the solution through numerical optimization algorithms. L Minimum parameter and The sinh-arcsinh transform result is obtained, which is the optimized transform result dataset. : .

[0050] In one embodiment, the final transformed result dataset The calculation expression is: ; in, , These are the mean and standard deviation of the first lower-order moment parameters; , These are the mean and standard deviation of the second lowest-order moment parameters; To optimize the transformed dataset, the variable Z now has the same mean and standard deviation as the target energy deposition result X, and the skewness and kurtosis of variable Z also highly approximate the skewness of the target energy deposition result X. and excess kurtosis .

[0051] Combining the above embodiments yields a preferred embodiment of the present invention. This preferred embodiment uses two application scenarios as examples: equivalent conversion of the deposition energy distribution in a large-size diamond detector to a small-size diamond detector under 30 MeV proton irradiation, and equivalent conversion of the deposition energy distribution in a diamond detector to biological tissue of the same size. These examples illustrate the application effect of the proposed method in cross-size and cross-medium equivalent conversion of diamond detectors. The specific implementation steps of the preferred embodiment of the present invention are as follows: S1: Place the diamond detector under 30 MeV proton irradiation to obtain the energy deposition dataset Y.

[0052] S2: Calculate the mean of the detector deposition energy dataset Y. Standard deviation skewness and excess kurtosis .

[0053] S3: Calculate the conversion factor between the detector and the target based on the correlation between them. n .

[0054] Specifically, an equivalent conversion is made between the deposition energy distribution in a large-size diamond detector and a small-size diamond detector, with the conversion factor being the size ratio of the two; an equivalent conversion is made between the deposition energy distribution in a diamond detector and biological tissue of the same size, with the conversion factor being the linear stopping power ratio of the two.

[0055] S4: Based on the conversion factor n Calculate the mean value of energy deposition X in the target detector or target medium. Standard deviation skewness and excess kurtosis : , , , ; S5: Transfer the energy deposition dataset Y Based on conversion factor n To perform proportional scaling, the calculation expression is: To obtain the intermediate dataset W .

[0056] S6: Calculate the intermediate dataset Wmean and standard deviation Then, on the intermediate dataset W Standardization is performed to obtain a standardized intermediate dataset. : .

[0057] S7: Construct the sinh-arcsinh transform based on the standardized intermediate dataset, and obtain the transformed dataset. The conversion formula is: ; In the formula, b and c are the parameters that need to be optimized.

[0058] S8: For any given ( b , c The transformed dataset can be obtained. skewness and kurtosis And construct the loss function; .

[0059] S9: Finding the optimal solution through numerical optimization L Minimum parameter and The sinh-arcsinh transform result is obtained, which is the optimized transform result dataset. : .

[0060] S10: Calculate the optimized transformation result dataset mean and standard deviation And perform linear scaling to obtain the final transformed dataset Z: .

[0061] Thus, the obtained variable Z has the same mean and standard deviation as the target result X, and its skewness and kurtosis are also highly approximating. , .

[0062] Furthermore, to verify the equivalent conversion effect of the method of the present invention, the deposition energy distribution of protons in detectors of different cubic shapes (side lengths of 100 μm, 75 μm, and 25 μm) was obtained, as well as the deposition energy distribution of protons in biological tissues of the same size (usually approximated by water, side lengths of 100 μm and 25 μm) under the same conditions. The method proposed in this invention will be used for equivalent energy calculation, and the comparison between the conversion results and the actual distributions will be performed. Figure 2-5 As shown. Figure 2-5 The horizontal axis represents the energy deposited in the medium, and the vertical axis represents the probability distribution of the deposited energy. According to... Figure 2-5It can be seen that the results obtained by performing equivalent conversions across size and medium using the method of the present invention have a high degree of agreement with the measured results under the same conditions. That is, the method of the present invention can achieve accurate equivalent conversions of the deposition energy distribution measured by the detector across size and medium, and can provide a conversion method with a wide range of applications and flexible measurement for accurate measurement of energy deposition distribution in the medium.

[0063] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the equivalent conversion method for detector energy deposition distribution formed by any or a combination of the above embodiments. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0064] The present invention also provides a storage medium having the same inventive concept as the equivalent conversion method for detector energy deposition distribution formed by any or more of the above embodiments, wherein computer instructions are stored thereon, and the computer instructions, when executed, perform the steps of the equivalent conversion method for detector energy deposition distribution formed by any or more of the above embodiments.

[0065] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] This invention also provides a terminal that shares the same inventive concept as any embodiment or combination of embodiments corresponding to the above-described equivalent conversion method for detector energy deposition distribution. The terminal includes a memory and a processor. The memory stores computer instructions executable on the processor. When the processor executes the computer instructions, it performs the steps of the above-described equivalent conversion method for detector energy deposition distribution. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement this invention.

[0067] In one embodiment, the terminal, i.e., the electronic device, is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0068] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section above, based on various exemplary embodiments of the present invention. For example, the processing unit can perform the equivalent conversion method for detector energy deposition distribution described above.

[0069] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0070] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these embodiments may include an implementation of a network environment.

[0071] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0072] Through the above description, those skilled in the art will readily understand that the embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to these exemplary embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods of the exemplary embodiments of this application.

[0073] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. An equivalent conversion method for detector energy deposition distribution, characterized in that, Includes the following steps: Deposition energy is measured using a detector in the target environment to obtain an energy deposition dataset; Calculate the first moment parameters of the energy deposition dataset; The conversion factor is determined based on the physical scale or physical material relationship between the detector and the target object; The second moment parameter corresponding to the energy deposition distribution of the target object is calculated by converting the first moment parameter using a conversion factor. The energy deposition dataset is scaled proportionally according to the conversion factor to obtain an intermediate dataset; The intermediate dataset is standardized to obtain a standardized intermediate dataset. The standardized intermediate dataset is input into the sinh-arcsinh transformation function, which includes the optimization parameters, to obtain the transformed dataset; For any combination of optimization parameters, calculate the first higher-order moment parameters of the transformed dataset and construct a loss function, which is used to quantify the difference between the first higher-order moment parameters and the second higher-order moment parameters in the second moment parameters; With the goal of minimizing the loss function, the optimization parameters are iteratively adjusted, and the transformed dataset corresponding to the minimum value of the loss function is used as the optimized transformation result dataset. Calculate the first low-order moment parameter of the optimized transformation result dataset, and use the first low-order moment parameter and the second low-order moment parameter of the second moment parameter to linearly scale the optimized transformation result dataset to obtain the final transformation result dataset.

2. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, The first moment parameter and the second moment parameter both include mean, standard deviation, skewness, and excess kurtosis; the first higher-order moment parameter and the second higher-order moment parameter both include skewness and excess kurtosis; and the first lower-order moment parameter and the second lower-order moment parameter both include mean and standard deviation.

3. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, Based on the physical scale or material relationship between the detector and the target object, the conversion factor is determined, including: When the conversion between the detector and the target object is across dimensions, the conversion factor is the ratio of the target object size to the detector size; when the conversion between the detector and the target object is across media, the conversion factor is the ratio of the mass resistance of the target medium to the mass resistance of the detector material.

4. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, The expression for calculating the second moment parameter is: ， ， ， ； in, , , and These are the mean, standard deviation, skewness, and excess kurtosis in the first moment parameters, respectively. These are conversion factors; , , and These are the mean, standard deviation, skewness, and excess kurtosis in the second moment parameters, respectively.

5. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, The transformation dataset for: ； in, It is a hyperbolic sine function; It is an inverse hyperbolic sine function; , To find the optimal parameters; To standardize the intermediate dataset.

6. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, The loss function for: ； in, , To find the optimal parameters; , These are the skewness and excess kurtosis in the first higher-order moment parameters, respectively. , These are the skewness and excess kurtosis in the second higher-order moment parameters, respectively.

7. The equivalent conversion method for detector energy deposition distribution according to claim 1, characterized in that, Final transformation result dataset The calculation expression is: ； in, , These are the mean and standard deviation of the first lower-order moment parameters; , These are the mean and standard deviation of the second lowest-order moment parameters; To optimize the transformed dataset.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the equivalent conversion method for detector energy deposition distribution as described in any one of claims 1-7.

9. A storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the steps of the equivalent conversion method for detector energy deposition distribution as described in any one of claims 1-7.

10. A terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that, When the processor executes the computer instructions, it performs the step of the equivalent conversion method for detector energy deposition distribution as described in any one of claims 1-7.

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