Gamma radionuclide matrix simulation efficiency scale key factor database construction method

Abstract

The invention discloses a gamma radionuclide matrix simulation efficiency scale key factor database construction method, which comprises the steps of selecting a multi-nuclide characteristic parameter group, and collecting nuclide radiation transmission original data in different matrix environments through a nuclide decay timestamp matching platform, constructing a nuclide matrix efficiency coupling calibration model input set and establishing a multi-dimensional parameter mapping relation; processing an input set by using the coupling calibration model, and generating a calibrated nuclide efficiency parameter matrix in combination with a timestamp matching result; a parameter matrix is corrected through a nuclide activity reconstruction error compensation model, and related correction factors are introduced to output nuclide activity characteristic parameters; splitting the analysis parameters by adopting a multi-nuclide component parallel solution algorithm to obtain a multi-nuclide component quantitative result; and scale key factors are extracted and classified and stored. According to the method, the multi-model and timestamp matching technology is integrated, the comprehensiveness and reliability of key factor data are ensured, and powerful data support is provided for nuclide matrix simulation efficiency scale.

Description

Technical Field

[0001] This invention relates to the field of radionuclide matrix simulation technology, and in particular to a method for constructing a database of key factors for calibrating the simulation efficiency of γ-radionuclide matrices. Background Technology

[0002] Calibration of the matrix simulation efficiency of gamma radionuclides is a core technical aspect in fields such as radionuclide detection and radiation protection, and its accuracy directly affects the reliability of radionuclide activity measurement, component analysis, and radiation risk assessment. With the increasing complexity of multi-nuclide coexistence scenarios, the interference of different matrix environments on the radiotransmission characteristics of radionuclides is becoming more significant. Traditional calibration methods relying on single parameters or simplified models are no longer sufficient to meet the multi-dimensional and high-precision application requirements. Constructing a database covering multiple key factors such as radionuclide decay characteristics, matrix physical parameters, and simulation calculation parameters has become a crucial support for achieving accurate calibration of the matrix simulation efficiency of radionuclides. The integrated application of technologies such as radionuclide decay timestamp matching, multi-model coupling calibration, and parallel computation provides the necessary technical foundation for the efficient construction of this database.

[0003] Existing technologies have two significant shortcomings in constructing a database of key factors for calibrating the efficiency of nuclide matrix simulations: First, they lack a systematic multi-model collaboration mechanism, failing to deeply integrate techniques such as nuclide matrix efficiency coupling calibration, activity reconstruction error compensation, and parallel calculation of multiple nuclide components. This results in weak parameter correlation during key factor extraction, making it difficult to fully reflect the interaction between nuclides and the matrix. Second, the database construction process lacks standardized step breakdown and unit collaborative design. The acquisition, calibration, calculation, and storage of key factors are independent of each other, failing to form a coherent technical chain. Furthermore, insufficient optimization of nuclide decay timestamp matching accuracy limits the consistency and accuracy of key factor data, making it impossible to provide comprehensive and reliable data support for calibrating the efficiency of nuclide matrix simulations. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration.

[0005] The technical solution adopted in this invention is a method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration, comprising the following steps: S1, selecting a multi-nucleus characteristic parameter group, covering nuclide decay constant, matrix component ratio, radiation energy response coefficient, and simulation geometric parameters, and collecting raw data of nuclide radiative transport under different matrix environments through a nuclide decay timestamp matching platform; S2, constructing an input set for a nuclide matrix efficiency coupling calibration model based on the collected data, integrating initial values ​​of nuclide activity, matrix physical property parameters, and simulation calculation grid density parameters, and establishing a multi-dimensional parameter mapping relationship; S3, using the nuclide matrix efficiency coupling calibration model to perform matrix processing on the input set, adjusting the model coupling coefficient in combination with the nuclide decay timestamp matching results, and generating a calibrated model. S4: The calibrated parameter matrix is ​​corrected using a nuclide activity reconstruction error compensation model. A matrix scattering correction factor and energy deposition deviation coefficient are introduced, and the error-compensated nuclide activity characteristic parameters are output. S5: A multi-nuclide component parallel solution algorithm is used to decompose and quantitatively analyze the error-compensated parameters. Decay chain parameters and matrix interaction parameters of different nuclides are processed simultaneously to obtain quantitative results for multi-nuclide components. S6: Key calibration factors are extracted from the analysis results, including nuclide efficiency calibration coefficients, activity reconstruction correction factors, component decomposition weight parameters, and timestamp matching deviation parameters. These are categorized and stored according to nuclide type, matrix category, and energy range to construct a database of key calibration factors for the simulated efficiency of γ-radioactive nuclides in the matrix.

[0006] Furthermore, the expression for the nuclide matrix efficiency coupling calibration model is as follows: middle, The efficiency of the calibrated nuclide matrix simulation. Let be the decay constant of the i-th nuclide. Let i be the percentage of the i-th nuclide in the matrix. Let be the radiation energy response coefficient of the i-th nuclide. Let be the simulated geometric parameters of the i-th nuclide. Let be the matching coefficient of the i-th nuclide obtained through the nuclide decay timestamp matching platform. The scattering correction factor for the j-th matrix is... Let be the physical property parameters of the j-th type of matrix. These are the model coupling coefficients. This is the offset correction factor. As a matrix type influencing factor, This is the energy response deviation coefficient.

[0007] Furthermore, the expression for the nuclide activity reconstruction error compensation model is as follows: ,in, The activity of the nuclide after error compensation. This represents the initial value of the nuclide activity. Let k be the systematic error component. For the k-th error compensation weight, for The characteristic function of nuclide decay at time t, for The output parameters of the platform are matched with the timestamps of nuclide decay at specific times. Let q be the standard deviation of the random error of the q-th term. Let q be the error influence factor of the qth term, p be the total number of systematic error components, q be the total number of random error components, and t be the decay time window.

[0008] Furthermore, the expression for the parallel solution algorithm for the multi-nucleoside components is: ,in, This is a matrix of quantitative results for multiple nuclide components. The percentage of the i-th nuclide in the j-th matrix. This is the nuclide decay chain parameter matrix. To solve the weight matrix in parallel, This is the measured nuclide signal vector. The matrix is ​​the matrix interference bias matrix. For matrix property parameter vectors, To accelerate the computation of the speedup factor matrix in parallel, This represents the Hadamard product of a matrix.

[0009] Furthermore, the matching accuracy optimization model of the nuclide decay timestamp matching platform is as follows: ,in, This is the optimized timestamp for nuclide decay. This is the original timestamp data. The average decay constant of multiple nuclides. For time synchronization calibration coefficients, For transmission delay parameters, This is the time jitter factor. For the time-matching correction factor of the type I matrix, The physical state parameters of the type I matrix are... denoted as the time response characteristic parameter of matrix type I, and k represents the total number of matrix types.

[0010] Furthermore, the storage index model for the database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration is as follows: For database index values, For hash mapping functions, For nuclide type feature vectors, For matrix category feature vectors, The energy range feature vector, For scale level parameters, For storage optimization factors, This represents the comprehensive value of the key factors on the scale, and ⊕ indicates the bitwise XOR operation of the vector.

[0011] Further, step S3 includes the following sub-steps: S31, extracting the nuclide decay constant, matrix physical property parameters, and simulation calculation grid density parameters from the input set constructed in S2, and grouping them according to nuclide type and matrix category to form multiple independent model input subsets; S32, substituting each input subset into the nuclide matrix efficiency coupling calibration model, and performing dimension unification processing on the input parameters through matrix transposition and row and column transformation to generate a standardized parameter matrix; S33, calling the matching coefficients output by the nuclide decay timestamp matching platform, adjusting the corresponding elements in the standardized parameter matrix proportionally, and updating the value range of the model coupling coefficients; S34, performing eigenvalue decomposition on the adjusted parameter matrix, screening out the principal component parameters that affect the nuclide matrix simulation efficiency, and integrating them to form a calibrated nuclide efficiency parameter matrix.

[0012] Further, step S4 includes the following sub-steps: S41, obtaining the nuclide efficiency parameter matrix output by S3, removing outliers through data screening, retaining valid parameter samples, and sorting them according to decay time series; S42, inputting the sorted parameter samples into the nuclide activity reconstruction error compensation model, introducing matrix scattering correction factor and energy deposition deviation coefficient, and establishing the correspondence between error components and parameter samples; S43, calculating the systematic error and random error of each parameter sample based on the correspondence, obtaining the total error value through weighted summation, and generating an error correction vector; S44, performing element-wise operations on the error correction vector and the nuclide efficiency parameter matrix, correcting the activity-related parameters in the parameter matrix, and outputting the error-compensated nuclide activity characteristic parameters.

[0013] Further, step S5 includes the following sub-steps: S51, receiving the nuclide activity characteristic parameters output from S4, splitting them into subsets of single nuclide characteristic parameters according to nuclide type, and determining the decay chain parameters and matrix interaction parameters corresponding to each subset; S52, starting the multi-threaded calculation module of the multi-nuclide component parallel solution algorithm, allocating an independent calculation thread to each nuclide characteristic parameter subset, and synchronously loading the corresponding decay chain parameters and matrix interaction parameters; S53, passing cross parameters through the inter-thread data sharing mechanism, using matrix operations to perform preliminary calculations on the component proportion of each nuclide, and obtaining intermediate calculation results; S54, summarizing the intermediate calculation results of all threads, eliminating conflicting data through consistency verification, and integrating to obtain the quantitative results of multi-nuclide components.

[0014] A method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration is proposed. This method is implemented through different units, including: a multi-nucleus parameter acquisition and preprocessing unit, a nuclide matrix efficiency coupling calibration calculation unit, a nuclide activity reconstruction error compensation calculation unit, a multi-nucleus component parallel solution processing unit, a nuclide decay timestamp matching and control unit, and a calibration key factor classification, storage, and indexing unit. The multi-nucleus parameter acquisition and preprocessing unit is connected to the nuclide decay timestamp matching and control unit via a data interface. The acquired raw data, after preliminary processing, is transmitted to the nuclide matrix efficiency coupling calibration calculation unit. Upon receiving the data, the nuclide matrix efficiency coupling calibration calculation unit calls its built-in model. After completing the calibration calculation, the results are transmitted to the nuclide activity reconstruction error compensation calculation unit. The nuclide activity reconstruction error compensation calculation unit corrects the calibration results for errors and outputs them to the multi-nuclide component parallel solution processing unit. The multi-nuclide component parallel solution processing unit obtains quantitative results through multi-threaded parallel computation and sends them to the calibration key factor classification storage and indexing unit. The nuclide decay timestamp matching and control unit provides time matching parameters to the calibration calculation unit, error compensation calculation unit, and parallel solution processing unit in real time. After receiving the quantitative results, the calibration key factor classification storage and indexing unit extracts key factors and stores them according to preset classification rules, generating a database index for querying and retrieval.

[0015] Beneficial Effects: This invention proposes a method for constructing a database of key factors for calibrating the efficiency of a γ-radioactive nuclide matrix simulation. It deeply integrates nuclide matrix efficiency coupling calibration, activity reconstruction error compensation, and parallel calculation techniques for multiple nuclide components. Through a nuclide decay timestamp matching platform spanning the entire process, it establishes a close correlation between multi-dimensional parameters of the nuclide and the matrix, comprehensively capturing the interaction patterns between the nuclide and the matrix, thus overcoming the lack of multi-model collaboration in existing technologies. Simultaneously, through a clearly defined step breakdown and a six-unit collaborative architecture, it forms a coherent technical chain encompassing parameter acquisition, calibration, error correction, component decomposition calculation, timestamp matching, and classified storage. Each unit transmits data and provides support according to predetermined logic, synchronously optimizing timestamp matching accuracy to ensure the consistency and accuracy of key factor data, solving the problems of fragmented processes and insufficient data reliability in existing technologies. The database constructed by this method covers multi-dimensional calibration key factors, providing comprehensive data support for the accurate calibration of nuclide matrix simulation efficiency, and significantly improving the reliability of technical applications in fields such as nuclide detection and radiation protection. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall steps of the method of the present invention; Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the unit composition for implementing the method of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency includes the following steps: S1, select a multi-nucleus characteristic parameter group, covering nuclide decay constant, matrix component ratio, X-ray energy response coefficient, and simulated geometric parameters, and collect raw data of nuclide radiation transmission under different matrix environments through a nuclide decay timestamp matching platform; Specifically, step S1 involves selecting a multi-nucleus characteristic parameter set that includes 12 types of nuclide decay constants, 8 types of matrix component proportions, 15 levels of radiation energy response coefficients, and 20 sets of simulated geometric parameters. The nuclide decay constants cover the characteristic decay values ​​of common γ-radioactive nuclides. The matrix component proportions are divided into gradient proportion ranges from 1% to 98% according to different matrix types. The radiation energy response coefficients are divided into 15 consecutive levels corresponding to the energy range of 5 keΓ to 10 MeΓ. The simulated geometric parameters include 20 sets of combined parameters in 5 dimensions such as detector distance and shielding layer thickness. Data acquisition was conducted using a nuclide decay timestamp matching platform. This platform was set to a 10ms sampling interval and simultaneously collected raw nuclide radiative transport data under 30 different matrix environments (covering solid, liquid, and gas matrix forms, with 10 component ratios set for each form). During the acquisition process, core data items such as nuclide radiation intensity, transport path offset, and matrix interaction frequency were recorded at a frequency of 1000 data points per second, with a cumulative acquisition time of no less than 72 hours. This ensured that the acquired data covered the radiative transport characteristics under different nuclide decay stages and different matrix component ratios, providing comprehensive and representative raw data support for subsequent model construction. The collected data was standardized into a data format by the platform's built-in format conversion module for easy processing in subsequent steps.

[0019] S2, based on the collected data, construct the input set of the nuclide matrix efficiency coupling calibration model, integrate the initial value of nuclide activity, matrix physical property parameters, and simulation calculation grid density parameters, and establish a multi-dimensional parameter mapping relationship; Specifically, step S2 constructs the input set for the nuclide matrix efficiency coupling calibration model based on the standardized raw data collected in step S1. The input set includes three types of core data: initial nuclide activity values, matrix physical property parameters, and simulation calculation grid density parameters. The initial nuclide activity values ​​are divided into 20 gradient levels ranging from 100 Bq to 10000 Bq, with each level corresponding to 3 sets of parallel data. The matrix physical property parameters cover 8 key parameters, including density, refractive index, and absorption coefficient, with 15 gradient values ​​set for each parameter according to the matrix type. The simulation calculation grid density parameters are divided into 10 density levels ranging from 10×10×10 to 100×100×100, with each level corresponding to different calculation accuracy requirements. A multi-dimensional parameter mapping relationship is established through a data association algorithm. Specifically, a 12×8×15×20 four-dimensional parameter association matrix is ​​constructed to correspond one-to-one with the initial value of nuclide decay constant and nuclide activity. The matrix component ratio and matrix physical property parameters are grouped and associated according to matrix type. The X-ray energy response coefficient and the simulation calculation grid density parameter are matched according to energy level. During the mapping process, a similarity calculation method is used to screen parameter combinations with a correlation degree higher than 90% and remove low correlation data to improve the quality of the input set. Finally, an input set containing 100,000 valid data records is formed. Each record contains 25 data fields, which comprehensively covers various parameter information required for model calibration and provides structured and highly correlated input data for the nuclide matrix efficiency coupling calibration model.

[0020] S3. The input set is matrixed using the nuclide matrix efficiency coupling calibration model. The model coupling coefficient is adjusted by combining the nuclide decay timestamp matching results to generate the calibrated nuclide efficiency parameter matrix. Specifically, step S3 uses the nuclide-matrix efficiency coupling calibration model to matrix-process the input set constructed in step S2. First, the 100,000 valid data records in the input set are converted into a 1000×100 parameter matrix. The row dimension of the matrix corresponds to different nuclide-matrix combination scenarios, and the column dimension corresponds to various feature parameters. Combining the matching coefficients output by the nuclide decay timestamp matching platform (these coefficients are calculated based on time sampling points, with one matching coefficient for each sampling point, and a value range of 0.8 to 1.0), the model coupling coefficient is adjusted in units of 100 data records. The coupling coefficient is initially set to a gradient value of 0.1 to 0.9. Based on the product of the timestamp matching coefficient and the elements of the parameter matrix, the value of the coupling coefficient is dynamically adjusted so that the correlation between the adjusted coupling coefficient and the timestamp matching coefficient reaches more than 85%. During the matrix-processing, a matrix factorization algorithm is used to decompose the parameter matrix into a feature matrix and a weight matrix. The core features of the nuclide-matrix interaction are extracted through the feature matrix, and the weight matrix reflects the influence degree of each parameter. After processing, the two types of matrices are reconstructed to generate the calibrated nuclide efficiency parameter matrix. The matrix has a dimension of 800×80 and contains 64,000 calibrated data records. The parameter values ​​of each record have been optimized by adjusting the coupling coefficient and reconstructing the matrix. The deviation of the nuclide efficiency parameters is controlled within 5%. The matrix data is sorted by nuclide type and matrix category in a secondary group to facilitate error correction in subsequent steps and ensure that the output nuclide efficiency parameter matrix has high accuracy and structured features.

[0021] S4 corrects the error of the calibrated parameter matrix by using the nuclide activity reconstruction error compensation model, introduces the matrix scattering correction factor and energy deposition deviation coefficient, and outputs the nuclide activity characteristic parameters after error compensation. Specifically, step S4 corrects the error in the calibrated parameter matrix generated in step S3 using a nuclide activity reconstruction error compensation model. First, it extracts the nuclide activity-related parameters from 64,000 data records in the matrix, introducing two core correction parameters: a matrix scattering correction factor and an energy deposition deviation coefficient. The matrix scattering correction factor has 30 possible values ​​based on matrix type, each corresponding to a specific matrix component ratio. The energy deposition deviation coefficient has 15 possible values ​​based on the radiation energy level, corresponding one-to-one with the radiation energy response coefficient levels in step S1. During error correction, the systematic and random errors for each data record are calculated. The systematic error is calculated based on the difference between the matrix scattering correction factor and the parameter matrix elements. The random error is obtained by multiplying the energy deposition deviation coefficient by the parameter fluctuation range. Both types of errors are calculated and processed in batches of 1000 data records per batch, with a processing efficiency of at least 500 records per second. The calculated systematic and random errors are weighted and summed, with weighting coefficients set to 0.6 for systematic error and 0.4 for random error, to obtain the total error value for each data record. A corresponding error correction vector is generated based on this total error value, maintaining the same dimensionality (800×80) as the calibrated parameter matrix. The error correction vector is then superimposed on the nuclide efficiency parameter matrix through element-wise operations. The corrected data undergoes validity verification, and data records with error correction exceeding 10% are removed. The final output is a set of error-compensated nuclide activity characteristic parameters containing 58,000 valid data points. This parameter set is sorted by decay time series to ensure temporal consistency and provide a high-precision parameter foundation for subsequent multi-nucleon decomposition calculations.

[0022] S5 employs a parallel multi-nucleoside component solution algorithm to perform component decomposition and quantitative analysis on the error-compensated parameters, simultaneously processing decay chain parameters and matrix interaction parameters of different nuclides to obtain quantitative results of multi-nucleoside components; Specifically, step S5 employs a multi-nucleus component parallel solution algorithm to perform component decomposition and quantitative analysis on the 58,000 nuclide activity characteristic parameters output from step S4. First, the parameter set is divided into 12 single-nucleus parameter subsets according to nuclide type, each containing 4,800 to 5,000 data records. Simultaneously, decay chain parameters (each decay chain contains characteristic parameters of 5 to 8 decay stages) and matrix interaction parameters (covering 6 types of parameters including scattering cross-section and absorption probability) corresponding to each nuclide are loaded. A multi-threaded computation module is then activated, setting up 12 parallel computation threads, each corresponding to one nuclide parameter subset. Cross-parameter transfer between threads is achieved through a shared memory mechanism. The transferred data includes nuclide interaction coefficients and matrix co-influence parameters, with the transfer latency controlled within 1 ms. Each thread performs preliminary calculations of the component proportions of the corresponding nuclide through matrix operations. An iterative calculation method is used during the calculation process, with 50 iterations and a convergence accuracy controlled within 0.001 for each iteration, resulting in 12 sets of intermediate calculation results. Each set contains four core data categories, including component proportion and decay contribution. The intermediate calculation results are then summarized, and records with data conflicts (a conflict threshold of 0.05) are removed using a consistency check algorithm, keeping the conflict data proportion below 3%. Finally, a multi-nucleus component quantitative result set containing 56,000 valid records is obtained. This result set is stored categorized by nuclide type and matrix type, with each record containing eight quantitative data items, comprehensively reflecting the component characteristics of different nuclides in various matrix environments.

[0023] S6. Extract key calibration factors from the analysis results, including nuclide efficiency calibration coefficient, activity reconstruction correction factor, component decomposition weighting parameter, and timestamp matching deviation parameter. Store these factors according to nuclide type, matrix category, and energy range to construct a database of key calibration factors for the simulated efficiency of γ-radioactive nuclide matrix.

[0024] Specifically, step S6 extracts key calibration factors from the quantitative results of the multi-nucleoside components obtained in step S5. The extracted key factors include four categories: nuclide efficiency calibration coefficients, activity reconstruction correction factors, component decomposition weight parameters, and timestamp matching deviation parameters. Among them, the nuclide efficiency calibration coefficients are extracted from the calibrated parameter matrix according to the nuclide-matrix combination scenario, resulting in 800 sets of coefficient data, each set containing coefficient values ​​in three dimensions; the activity reconstruction correction factors are extracted from the parameter set after error compensation, corresponding to the correction amount of 58,000 data records, and averaged at 100 data points to obtain 580 sets of correction factors; the component decomposition weight parameters are extracted from the iterative process of parallel solution, with one set of weight parameters recorded in each iteration step, resulting in 50 sets of core weight parameters; the timestamp matching deviation parameters are extracted from the output results of the nuclide decay timestamp matching platform, and cumulatively calculated according to the time sampling interval to obtain 7,200 sets of deviation parameters. After extraction, the data is categorized and stored according to three dimensions: nuclide type (12 categories), matrix category (30 categories), and energy range (15 levels). A hierarchical storage architecture is used: the first layer divides the data into 12 partitions based on nuclide type; the second layer divides each partition into 30 sub-partitions based on matrix category; and the third layer divides each sub-partition into 15 data blocks based on energy range. Each data block contains all key factor data for its corresponding dimension. A multi-dimensional index is also established, with index keywords including nuclide number, matrix code, and energy level identifier. This index enables rapid data retrieval and access. Ultimately, a key factor database for γ-radioactive nuclide matrix simulation efficiency scaling is constructed, containing 12 × 30 × 15 = 5400 data blocks and a total of 280,000 key factor records. The database supports queries based on single or combined conditions of nuclide type, matrix category, and energy range, with a query response time controlled within 0.5 seconds. This provides comprehensive, accurate, and efficient key factor data support for nuclide matrix simulation efficiency scaling.

[0025] Preferably, the expression for the nuclide matrix efficiency coupling calibration model is: middle, The efficiency of the calibrated nuclide matrix simulation. Let be the decay constant of the i-th nuclide. Let i be the percentage of the i-th nuclide in the matrix. Let be the radiation energy response coefficient of the i-th nuclide. Let be the simulated geometric parameters of the i-th nuclide. Let be the matching coefficient of the i-th nuclide obtained through the nuclide decay timestamp matching platform. The scattering correction factor for the j-th matrix is... Let be the physical property parameters of the j-th type of matrix. These are the model coupling coefficients. This is the offset correction factor. As a matrix type influencing factor, This is the energy response deviation coefficient.

[0026] Specifically, the nuclide-matrix efficiency coupling calibration model is used to achieve coordinated calibration of nuclide characteristic parameters and matrix environment parameters. The implementation process first determines the number of nuclide types involved in the calculation, selecting 5 to 20 common gamma-ray radionuclides based on the actual application scenario, and obtaining the decay constant for each nuclide. Simultaneously, 3 to 10 matrix types are classified, and the component proportion of each nuclide in each matrix type is determined, with values ​​covering a gradient range from 1% to 98%. For each nuclide and matrix combination, a corresponding X-ray energy response coefficient is matched. This coefficient is based on an energy range of 5 keΓ to 10 MeΓ, divided into 15 consecutive levels. Combined with five dimensions of simulated geometric parameters, including the relative position of the detector and the nuclide source, and the thickness of the shielding layer, a basic calculation parameter set is formed. The matching coefficient for each nuclide is obtained through a nuclide decay timestamp matching platform, with values ​​controlled between 0.8 and 1.0. Simultaneously, the scattering correction factor and physical characteristic parameters of each matrix type are collected to establish a matrix parameter library. During model computation, the relevant nuclide parameters are first multiplied and summed, then divided by the product summation of matrix parameters. The resulting ratio is multiplied by the model coupling coefficient in the range of 0.1 to 0.9, and then superimposed with the result of the logarithmic product of the offset correction coefficient, matrix type influence factor, and energy response deviation coefficient. Finally, the calibrated nuclide matrix simulation efficiency is output. This process achieves multi-parameter coordinated control through matrix operations, ensuring the matching degree between efficiency parameters and actual scenarios, and providing accurate basic data for subsequent activity reconstruction.

[0027] Preferably, the expression for the nuclide activity reconstruction error compensation model is: ,in, The activity of the nuclide after error compensation. This represents the initial value of the nuclide activity. Let k be the systematic error component. For the k-th error compensation weight, for The characteristic function of nuclide decay at time t, for The output parameters of the platform are matched with the timestamps of nuclide decay at specific times. Let q be the standard deviation of the random error of the q-th term. Let q be the error influence factor of the qth term, p be the total number of systematic error components, q be the total number of random error components, and t be the decay time window.

[0028] Specifically, the nuclide activity reconstruction error compensation model corrects errors in nuclide activity parameters. During implementation, initial nuclide activity values ​​are first obtained, with an initial value range set from 100 to 10000. Three sets of parallel data are set for each range to improve reliability. Four to eight systematic error components are identified, each with an error compensation weight of 0.01 to 0.1. The systematic error correction coefficient is obtained by multiplying each systematic error component by its corresponding weight and adding 1, followed by a chain multiplication of all results. Simultaneously, five to ten random error components are identified. The product of the square of the standard deviation of each random error and the square of the corresponding error influence factor is calculated, and the summation and square root are used as the denominator of the random error correction coefficient. Based on the time-related parameters output by the nuclide decay timestamp matching platform, the product of the nuclide decay characteristic function and the platform output parameters is integrated within a decay time window of 0 to 72 hours to obtain the time-matching correction integral value. The initial nuclide activity value is multiplied by the systematic error correction coefficient and the time-matching correction integral value, and then divided by the random error correction coefficient to complete the error compensation calculation and output the corrected nuclide activity. This model effectively reduces the activity calculation deviation caused by multiple factors and improves the accuracy of nuclide activity parameters by separately handling systematic and random errors and dynamically correcting them in the time dimension.

[0029] Preferably, the expression for the parallel solution algorithm for multi-nucleoside components is: ,in, This is a matrix of quantitative results for multiple nuclide components. The percentage of the i-th nuclide in the j-th matrix. This is the nuclide decay chain parameter matrix. To solve the weight matrix in parallel, This is the measured nuclide signal vector. The matrix is ​​the matrix interference bias matrix. For matrix property parameter vectors, To accelerate the computation of the speedup factor matrix in parallel, This represents the Hadamard product of a matrix.

[0030] Specifically, the parallel solution algorithm for multiple nuclide components is used to achieve simultaneous quantitative analysis of multiple nuclide components. In implementation, a nuclide decay chain parameter matrix is ​​first constructed. The matrix dimension is determined according to the nuclide type and decay stage, with each row corresponding to one nuclide and each column corresponding to a characteristic parameter of a decay stage. The parameter values ​​are determined based on the nuclide decay law and experimental data. A parallel solution weight matrix is ​​established, with weight values ​​set according to the influence of the nuclide in the matrix, ranging from 0.1 to 0.9. Matrix multiplication is used to obtain the product matrix of the nuclide decay chain parameter matrix and the parallel solution weight matrix. The inverse of this product matrix is ​​then used to obtain the core solution matrix. Measured nuclide signal vectors are collected, with vector elements corresponding to the signal intensity at different detection time points. Simultaneously, a matrix interference deviation matrix and a matrix characteristic parameter vector are constructed, with the matrix dimension and vector length matching the number of nuclide types. Matrix interference correction values ​​are obtained through matrix-vector multiplication. The measured nuclide signal vector is subtracted from the matrix interference correction value to obtain the corrected signal vector. This corrected signal vector is then subjected to a Hadamard product operation with a parallel computing acceleration factor matrix. The elements of this acceleration factor matrix range from 1.2 to 2.0 to improve computational efficiency. The final output is a multi-nucleoside component quantification result matrix. The matrix elements directly reflect the component proportion of each nuclide in various matrices. The algorithm achieves simultaneous multi-nucleoside calculation through multi-threaded parallel processing, significantly reducing computation time. Simultaneously, matrix operations integrate multi-dimensional parameters to ensure the accuracy of the component quantification results.

[0031] Preferably, the matching accuracy optimization model of the nuclide decay timestamp matching platform is as follows: ,in, This is the optimized timestamp for nuclide decay. This is the original timestamp data. The average decay constant of multiple nuclides. For time synchronization calibration coefficients, For transmission delay parameters, This is the time jitter factor. For the time-matching correction factor of the type I matrix, The physical state parameters of the type I matrix are... denoted as the time response characteristic parameter of matrix type I, and k represents the total number of matrix types.

[0032] Specifically, the nuclide decay timestamp matching platform's matching accuracy optimization model is used to improve the matching accuracy of nuclide decay timestamps. During implementation, raw timestamp data is first collected, recorded at 10ms sampling intervals, and accumulated for 72 hours to cover different decay stages. The average decay constant of multiple nuclides is calculated, taking the arithmetic mean of the decay constants of 5 to 20 selected nuclides, and a time synchronization calibration coefficient in the range of 0.95 to 1.05 is set to correct time synchronization deviations. Transmission delay parameters and time jitter coefficients are acquired; both parameters are collected in real-time by the platform's built-in detection module, with values ​​ranging from 0.1 to 1.0 and 0.01 to 0.1, respectively. The product of the transmission delay parameter and the time jitter coefficient is calculated, and its square root is used as the denominator of the time stability correction factor. Simultaneously, 3 to 10 matrix types are classified, with each matrix type corresponding to a time matching correction factor of 0.05 to 0.2, physical state parameters of 1 to 10, and time response characteristic parameters of 0.8 to 1.0. The products of these three types of parameters are summed to obtain the matrix type correction value. The original timestamp data is multiplied by the average decay constant of the multi-nucleus and the time synchronization calibration coefficient, then divided by the time stability correction factor, and finally the matrix type correction value is added to output the optimized nuclide decay timestamp. This model significantly improves the accuracy and stability of timestamp data through multi-dimensional optimization such as time synchronization calibration, transmission delay and jitter correction, and matrix type adaptation, providing a reliable time reference for end-to-end parameter calibration.

[0033] Preferably, the storage index model of the key factor database for γ-radioactive nuclide matrix simulation efficiency calibration is as follows: For database index values, For hash mapping functions, For nuclide type feature vectors, For matrix category feature vectors, The energy range feature vector, For scale level parameters, For storage optimization factors, This represents the comprehensive value of the key factors on the scale, and ⊕ indicates the bitwise XOR operation of the vector.

[0034] Specifically, the γ-radioactive nuclide matrix simulation efficiency scale key factor database storage index model is used to achieve efficient database storage and fast retrieval. In implementation, firstly, nuclide type feature vectors are constructed, with vector lengths set according to 12 nuclide categories. Each nuclide category corresponds to a feature value, which is either 0 or 1 to distinguish nuclide types. Next, matrix category feature vectors are constructed, with vector lengths set according to 30 matrix categories, also using 0 or 1 to identify matrix categories. Finally, energy range feature vectors are constructed, with vector lengths set according to 15 energy ranges, using 0 or 1 to distinguish energy ranges. A bitwise XOR operation is performed on the three feature vectors to obtain a fused feature vector. This vector is then multiplied by the scale level parameter in the 0.5 to 2.0 range to obtain the basic index value. Finally, a hash mapping function is used to map the basic index value, generating an initial hash index value. The hash function employs an asymmetric mapping algorithm to ensure the uniqueness of the index value. A storage optimization coefficient ranging from 0.8 to 1.2 is set, and the comprehensive value of the key scaling factors is calculated. This comprehensive value is obtained by weighted summation of key factors such as the nuclide efficiency calibration coefficient and the activity reconstruction correction factor, with weights set to 0.2 to 0.3 according to factor importance. The comprehensive value of the key scaling factors is increased by 1, and the logarithm to base 2 is taken. This logarithm is then multiplied by the storage optimization coefficient to obtain the index adjustment value. The initial hash index value is added to the index adjustment value to finally generate the database index value. This model constructs an efficient indexing system through a combination of multi-dimensional feature vector fusion, hash mapping, and logarithmic adjustment, enabling rapid retrieval by nuclide type, matrix category, and energy range, thus improving the efficiency of database utilization.

[0035] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, extracting the nuclide decay constant, matrix physical property parameters, and simulation calculation grid density parameters from the input set constructed in S2, and grouping them according to nuclide type and matrix category to form multiple independent model input subsets; S32, substituting each input subset into the nuclide matrix efficiency coupling calibration model, and performing dimension unification processing on the input parameters through matrix transposition and row and column transformation to generate a standardized parameter matrix; S33, calling the matching coefficients output by the nuclide decay timestamp matching platform, adjusting the corresponding elements in the standardized parameter matrix proportionally, and updating the value range of the model coupling coefficients; S34, performing eigenvalue decomposition on the adjusted parameter matrix, screening out the principal component parameters that affect the nuclide matrix simulation efficiency, and integrating them to form a calibrated nuclide efficiency parameter matrix.

[0036] Specifically, step S3 includes steps S31 to S34: S31 extracts the decay constants of 12 types of nuclides, the physical property parameters of 30 types of matrix, and the grid density parameters of simulation calculations at 10 levels from the input set constructed in step S2. These are divided into 360 independent model input subsets based on the 12 nuclide types and 30 matrix categories, with each subset containing 25 core parameter items; S32 substitutes each of the 360 ​​input subsets into the nuclide-matrix efficiency coupling calibration model, converting the one-dimensional parameter sequence of each subset into a 10×10 two-dimensional matrix through matrix transpose, and then unifying the dimension of all matrices to 50×50 through row and column transformations to generate a standardized parameter matrix set; S33 calls the nuclide decay time... The 7200 sets of matching coefficients output by the matching platform are adjusted according to the nuclide-matrix combination corresponding to each subset of inputs. The corresponding elements in the standardized parameter matrix are adjusted in a ratio range of 0.1 to 0.9, and the value range of the model coupling coefficient is updated synchronously to keep the correlation between the coupling coefficient and the matching coefficient above 85%. S34 performs eigenvalue decomposition on the adjusted 50×50 standardized parameter matrix, selects the top 20 principal component parameters, removes minor parameters with a cumulative contribution rate of less than 5%, and re-integrates the principal component parameters according to nuclide type and matrix category to form an 800×80 calibrated nuclide efficiency parameter matrix to ensure that the matrix data can accurately reflect the interaction efficiency characteristics between nuclides and matrix.

[0037] Preferred, such as Figure 3 As shown, step S4 includes the following sub-steps: S41, obtaining the nuclide efficiency parameter matrix output by S3, removing outliers through data filtering, retaining valid parameter samples, and sorting them according to decay time series; S42, inputting the sorted parameter samples into the nuclide activity reconstruction error compensation model, introducing matrix scattering correction factor and energy deposition deviation coefficient, and establishing the correspondence between error components and parameter samples; S43, calculating the systematic error and random error of each parameter sample based on the correspondence, obtaining the total error value through weighted summation, and generating an error correction vector; S44, performing element-wise operations on the error correction vector and the nuclide efficiency parameter matrix, correcting the activity-related parameters in the parameter matrix, and outputting the error-compensated nuclide activity characteristic parameters.

[0038] Specifically, step S4 includes steps S41 to S44: S41: Obtain the 800×80 nuclide efficiency parameter matrix output from step S3. Use the 3σ criterion to filter out outliers from 64,000 data records, retaining 59,000 valid parameter samples. Sort these samples according to the nuclide decay time series from 0 to 72 hours to ensure data continuity. S42: Input the sorted valid parameter samples into the nuclide activity reconstruction error compensation model in batches of 1000. Introduce scattering correction factors corresponding to 30 matrix types and energy deposition deviation coefficients for 15 energy ranges to establish a one-to-one correspondence between each batch of samples and the two correction parameters, forming an error correlation table. S43: Calculate the error correlation table for each batch... The systematic and random errors of the samples are calculated. The systematic error is obtained by the difference between the matrix scattering correction factor and the parameter sample, and the random error is obtained by the product of the energy deposition deviation coefficient and the parameter fluctuation range. The systematic error is weighted and summed with a weight of 0.6 for systematic error and 0.4 for random error, generating 59 sets of total error values ​​and corresponding 50×50 error correction vectors. S44 performs element-wise superposition operation on the error correction vector and the nuclide efficiency parameter matrix, and performs targeted correction on the 20 columns of parameters related to nuclide activity in the matrix. After correction, 1,000 data records with error correction exceeding 10% are removed through validity verification. Finally, a set of nuclide activity feature parameters containing 58,000 valid data is output, providing high-precision parameter support for subsequent group decomposition calculations.

[0039] Preferred, such as Figure 4 As shown, step S5 includes the following sub-steps: S51, receiving the nuclide activity characteristic parameters output from S4, splitting them into subsets of single nuclide characteristic parameters according to nuclide type, and determining the decay chain parameters and matrix interaction parameters corresponding to each subset; S52, starting the multi-threaded calculation module of the parallel solution algorithm for multi-nuclide components, allocating an independent calculation thread for each subset of nuclide characteristic parameters, and synchronously loading the corresponding decay chain parameters and matrix interaction parameters; S53, passing cross parameters through a data sharing mechanism between threads, using matrix operations to perform preliminary calculations on the component proportions of each nuclide, and obtaining intermediate calculation results; S54, summarizing the intermediate calculation results of all threads, eliminating conflicting data through consistency checks, and integrating to obtain quantitative results for multi-nuclide components.

[0040] Specifically, step S5 includes steps S51 to S54: S51 receives 58,000 nuclide activity characteristic parameters output from step S4, and splits them into 12 single nuclide characteristic parameter subsets according to 12 nuclide types. Each subset contains 4,800 to 5,000 data records, and simultaneously determines the decay chain parameters of 5 to 8 decay stages and 6 matrix interaction parameters corresponding to each subset; S52 starts 12 independent computing threads of the multi-nuclide component parallel solution algorithm, each thread is allocated one nuclide parameter subset, and the decay chain parameters and matrix interaction parameters of the corresponding nuclide are loaded into the thread memory through thread initialization settings, with the loading time controlled within 100ms; S53 implements the shared memory mechanism... The system involves cross-parameter transfer between 12 threads, transmitting key data such as inter-nuclein interaction coefficients and matrix co-influence parameters, with a transmission delay of no more than 1ms. Each thread performs preliminary calculations of the component proportion of the corresponding nuclide using matrix multiplication. After 50 iterations, 12 sets of intermediate calculation results are obtained. S54 summarizes the 12 sets of intermediate calculation results, uses a consistency check algorithm to set a conflict judgment threshold of 0.05, removes 3% of conflicting data records, and classifies and integrates the remaining valid data according to nuclide type and matrix category. Finally, a multi-nucleus component quantitative result set containing 56,000 valid records is formed, ensuring that the results can comprehensively and accurately reflect the component proportion of different nuclides in various matrices.

[0041] The nuclide-matrix efficiency coupling calibration model is a core model used to integrate multi-dimensional parameters of nuclides and matrices to achieve accurate calibration of simulation efficiency. Its functionality is implemented through multi-step parameter processing and matrix operations: First, 12 types of nuclide decay constants, 30 types of matrix physical property parameters, and 10 levels of simulation calculation grid density parameters are extracted and divided into 360 input subsets according to nuclide type and matrix category, with each subset containing 25 core parameter items. Then, the subsets are substituted into the model, and after matrix transposition and row / column transformation, the dimension is unified to 50×50, generating a standardized parameter matrix set. The 7200 sets of matching coefficients output by the nuclide decay timestamp matching platform are called, and the matrix elements are adjusted at a ratio of 0.1 to 0.9 to update the model coupling coefficients, maintaining the correlation above 85%. Finally, the top 20 principal component parameters are screened through eigenvalue decomposition, and minor parameters with a cumulative contribution rate of less than 5% are removed, integrating to form an 800×80 calibrated nuclide efficiency parameter matrix. This model establishes a dynamic mapping relationship between nuclides and matrix parameters, corrects coupling deviations between parameters, and solves the problem that traditional models cannot take into account the complex interactions between multiple nuclides and multiple matrices. It keeps the deviation of nuclide simulation efficiency parameters within 5%, providing high-precision basic data support for subsequent activity reconstruction and component decomposition calculation.

[0042] The nuclide activity reconstruction error compensation model is an error correction tool for the calibrated parameter matrix. It achieves accurate optimization of activity parameters by separately processing systematic and random errors. The specific implementation process is as follows: First, an 800×80 nuclide efficiency parameter matrix is ​​obtained. Outliers in 64,000 data points are removed using the 3σ criterion, retaining 59,000 valid samples and sorting them according to the decay time series from 0 to 72 hours. The samples are input into the model in batches of 1,000, and 30 types of matrix scattering correction factors and 15 levels of energy deposition deviation coefficients are introduced to establish an error correlation table. The total error is calculated with a weight of 0.6 for systematic error and 0.4 for random error, generating 59 sets of error correction vectors. The 20 columns of activity-related parameters in the matrix are corrected by element-wise superposition operations. 1,000 data points with corrections exceeding 10% are removed, and 58,000 valid activity feature parameters are output. This model accurately reduces the activity calculation errors caused by multiple factors such as matrix interference and energy deviation, breaking through the limitations of traditional single-factor error correction and improving the accuracy of nuclide activity parameters to over 95%, providing a reliable parameter basis for the quantitative analysis of multi-nucleoside components.

[0043] The multi-nucleoside component parallel solution algorithm is a highly efficient algorithm for the simultaneous quantitative analysis of multiple nuclide components. It achieves fast and accurate solution through multi-threaded parallel processing and data collaboration: First, it receives 58,000 nuclide activity characteristic parameters, which are divided into 12 subsets according to 12 nuclide types. Each subset contains 4,800 to 5,000 data points, and simultaneously determines 5 to 8 decay stage parameters and 6 matrix interaction parameters corresponding to each subset. Then, it starts 12 independent computing threads, each thread is assigned one subset, and the corresponding parameters are loaded into memory, with the loading time controlled within 100ms. Cross-parameter transfer between threads is achieved through a shared memory mechanism, with a transfer delay of no more than 1ms. Each thread obtains intermediate solution results through 50 iterative matrix multiplication operations. Finally, it summarizes 12 sets of intermediate results, removes 3% of conflicting data according to a conflict judgment threshold of 0.05, and classifies and integrates them to form a set of 56,000 valid quantitative results. This algorithm enables rapid separation and accurate quantification of multi-nucleoside components, solving the problems of low efficiency and difficult data conflict handling in traditional serial algorithms. It reduces the calculation time to 1 / 12 of the traditional method, while ensuring that the component proportion analysis accuracy reaches 97%, providing comprehensive quantitative data for the extraction of key factors from the database.

[0044] The nuclide decay timestamp matching platform is a time reference support tool that runs through the entire process. It achieves accurate timestamp output through high-precision data acquisition and matching optimization. Specifically, it collects raw data on the radiation transmission of nuclides under 30 different matrix environments at a sampling interval of 10ms, with a collection frequency of 1000 data points per second and a cumulative collection time of no less than 72 hours, to obtain core data such as nuclide radiation intensity and transmission path offset. Based on the collected raw timestamp data, it calculates the average decay constant of 12 nuclides and sets a time synchronization calibration coefficient of 0.95 to 1.05. It collects transmission delay parameters of 0.1 to 1.0 and time jitter coefficients of 0.01 to 0.1 in real time, and calculates the square root of their product as a time stability correction factor. Combining the time matching correction factor of 0.05 to 0.2 corresponding to the 30 matrix types, physical state parameters of 1 to 10, and time response characteristic parameters of 0.8 to 1.0, it outputs accurate timestamps through multi-dimensional optimization calculations. This platform provides a unified time reference for calibration models, error compensation models, and parallel solution algorithms, controlling timestamp matching errors to within 1ms, ensuring the time consistency of data in each stage, avoiding parameter parsing errors caused by time deviations, and providing stable time support for the entire database construction process.

[0045] like Figure 5 As shown, a method for constructing a database of key factors for the calibration efficiency of a γ-radioactive nuclide matrix simulation is presented. This method is implemented through different units, including: a multi-nucleus parameter acquisition and preprocessing unit, a nuclide matrix efficiency coupling calibration calculation unit, a nuclide activity reconstruction error compensation calculation unit, a multi-nucleus component parallel solution processing unit, a nuclide decay timestamp matching and control unit, and a calibration key factor classification, storage, and indexing unit. The multi-nucleus parameter acquisition and preprocessing unit is connected to the nuclide decay timestamp matching and control unit via a data interface. The acquired raw data, after preliminary processing, is transmitted to the nuclide matrix efficiency coupling calibration calculation unit. Upon receiving the data, the nuclide matrix efficiency coupling calibration calculation unit calls its built-in model... The calibration calculation is completed, and the result is transmitted to the nuclide activity reconstruction error compensation calculation unit. After the nuclide activity reconstruction error compensation calculation unit corrects the calibration result, it outputs it to the multi-nuclide component parallel solution processing unit. The multi-nuclide component parallel solution processing unit obtains the quantitative result through multi-threaded parallel operation and sends it to the scale key factor classification storage and indexing unit. The nuclide decay timestamp matching and control unit provides time matching parameters to the calibration calculation unit, error compensation calculation unit, and parallel solution processing unit in real time. After receiving the quantitative result, the scale key factor classification storage and indexing unit extracts the key factors and stores them according to the preset classification rules, and generates a database index for querying and calling.

[0046] The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration uses a nuclide decay timestamp matching platform as the technical support throughout the entire process. It organically integrates nuclide matrix efficiency coupling calibration, activity reconstruction error compensation, and parallel solution technology for multiple nuclide components to achieve deep correlation between nuclides and matrix multi-dimensional parameters. At the same time, a closed-loop technology chain is formed through a six-unit collaborative architecture to ensure standardized processing of key factors from acquisition, calibration to storage, which greatly improves the comprehensiveness and reliability of the data.

[0047] This method addresses the lack of multi-model collaboration by deeply binding three core models with a timestamp matching platform to establish a dynamic mapping relationship between parameters. This comprehensively captures the interaction between nuclide decay characteristics and the matrix environment, compensating for the inability of a single model to cover complex scenarios. To address the issues of fragmented processes and insufficient data reliability, the method breaks down key steps into quantifiable and executable operations through clear step-by-step decomposition and refinement. Simultaneously, by leveraging the orderly connection and data interaction of the six units, it optimizes timestamp matching accuracy, reduces data deviations at each stage, and ensures the consistency of key factor extraction and storage, providing stable and comprehensive data support for the efficiency scaling of nuclide matrix simulation.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a database of key factors for calibrating the efficiency of a γ-radioactive nuclide matrix simulation, characterized in that, The process includes the following steps: S1, selecting a multi-nucleus characteristic parameter set, covering nuclide decay constant, matrix component ratio, X-ray energy response coefficient, and simulation geometric parameters, and collecting raw data of nuclide radiative transport under different matrix environments through a nuclide decay timestamp matching platform; S2, constructing an input set for a nuclide matrix efficiency coupling calibration model based on the collected data, integrating initial values ​​of nuclide activity, matrix physical property parameters, and simulation calculation grid density parameters, and establishing a multi-dimensional parameter mapping relationship; S3. The input set is matrixed using the nuclide matrix efficiency coupling calibration model. The model coupling coefficient is adjusted by combining the nuclide decay timestamp matching results to generate the calibrated nuclide efficiency parameter matrix. S4 corrects the error of the calibrated parameter matrix by using the nuclide activity reconstruction error compensation model, introduces the matrix scattering correction factor and energy deposition deviation coefficient, and outputs the nuclide activity characteristic parameters after error compensation. S5. A parallel solution algorithm for multi-nucleon components is used to decompose and quantify the parameters after error compensation. The decay chain parameters and matrix interaction parameters of different nuclides are processed simultaneously to obtain quantitative results of multi-nucleon components. S6. Key calibration factors in the analysis results are extracted, including nuclide efficiency calibration coefficient, activity reconstruction correction factor, component calculation weight parameter, and timestamp matching deviation parameter. These factors are classified and stored according to nuclide type, matrix category, and energy range to construct a database of key calibration factors for the simulated efficiency of γ-radioactive nuclides matrix.

2. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, The expression for the nuclide matrix efficiency coupling calibration model is as follows: middle, The efficiency of the calibrated nuclide matrix simulation. Let be the decay constant of the i-th nuclide. Let i be the percentage of the i-th nuclide in the matrix. Let be the radiation energy response coefficient of the i-th nuclide. Let be the simulated geometric parameters of the i-th nuclide. Let be the matching coefficient of the i-th nuclide obtained through the nuclide decay timestamp matching platform. The scattering correction factor for the j-th matrix is... Let be the physical property parameters of the j-th type of matrix. These are the model coupling coefficients. This is the offset correction factor. As a matrix type influencing factor, This is the energy response deviation coefficient.

3. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, The expression for the nuclide activity reconstruction error compensation model is as follows: ,in, The activity of the nuclide after error compensation. This represents the initial value of the nuclide activity. Let k be the systematic error component. For the k-th error compensation weight, for The characteristic function of nuclide decay at time t, for The output parameters of the platform are matched with the timestamps of nuclide decay at specific times. Let q be the standard deviation of the random error of the q-th term. Let q be the error influence factor of the qth term, p be the total number of systematic error components, q be the total number of random error components, and t be the decay time window.

4. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, The expression for the parallel solution algorithm for the multi-nucleoside components is: ,in, This is a matrix of quantitative results for multiple nuclide components. The percentage of the i-th nuclide in the j-th matrix. This is the nuclide decay chain parameter matrix. To solve the weight matrix in parallel, This is the measured nuclide signal vector. The matrix is ​​the matrix interference bias matrix. For matrix property parameter vectors, To accelerate the computation of the speedup factor matrix in parallel, This represents the Hadamard product of a matrix.

5. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, The matching accuracy optimization model of the nuclide decay timestamp matching platform is as follows: ,in, This is the optimized timestamp for nuclide decay. This is the original timestamp data. The average decay constant of multiple nuclides. For time synchronization calibration coefficients, For transmission delay parameters, This is the time jitter coefficient. For the time-matching correction factor of the type I matrix, These are the physical state parameters of the type I matrix. denoted as the time response characteristic parameter of matrix type I, and k represents the total number of matrix types.

6. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, The storage index model for the key factor database for γ-radioactive nuclide matrix simulation efficiency calibration is as follows: For database index values, For hash mapping functions, For nuclide type feature vectors, For matrix category feature vectors, The energy range feature vector, For scale level parameters, For storage optimization factors, This represents the comprehensive value of the key factors on the scale, and ⊕ indicates a bitwise XOR operation on the vector.

7. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, S3 includes the following sub-steps: S31, extracting the nuclide decay constant, matrix physical property parameters, and simulation calculation grid density parameters from the input set constructed in S2, and grouping them according to nuclide type and matrix category to form multiple independent model input subsets; S32, substituting each input subset into the nuclide matrix efficiency coupling calibration model, and performing dimension unification processing on the input parameters through matrix transposition and row and column transformation to generate a standardized parameter matrix; S33, calling the matching coefficients output by the nuclide decay timestamp matching platform, adjusting the corresponding elements in the standardized parameter matrix proportionally, and updating the value range of the model coupling coefficients; S34, performing eigenvalue decomposition on the adjusted parameter matrix, screening out the principal component parameters that affect the nuclide matrix simulation efficiency, and integrating them to form a calibrated nuclide efficiency parameter matrix.

8. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, S4 includes the following sub-steps: S41, obtain the nuclide efficiency parameter matrix output by S3, remove outliers through data filtering, retain valid parameter samples and sort them according to decay time series; S42, input the sorted parameter samples into the nuclide activity reconstruction error compensation model, introduce the matrix scattering correction factor and energy deposition deviation coefficient, and establish the correspondence between error components and parameter samples; S43, calculate the systematic error and random error of each parameter sample based on the correspondence, obtain the total error value by weighted summation, and generate the error correction vector; S44 performs element-wise operations on the error correction vector and the nuclide efficiency parameter matrix, corrects the activity-related parameters in the parameter matrix, and outputs the nuclide activity characteristic parameters after error compensation.

9. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to claim 1, characterized in that, S5 includes the following sub-steps: S51, receiving the nuclide activity characteristic parameters output from S4, splitting them into subsets of single nuclide characteristic parameters according to nuclide type, and determining the decay chain parameters and matrix interaction parameters corresponding to each subset; S52, starting the multi-threaded calculation module of the parallel solution algorithm for multi-nuclide components, allocating an independent calculation thread for each subset of nuclide characteristic parameters, and synchronously loading the corresponding decay chain parameters and matrix interaction parameters; S53, passing cross parameters through a data sharing mechanism between threads, using matrix operations to perform preliminary calculations on the component proportions of each nuclide, and obtaining intermediate calculation results; S54, summarizing the intermediate calculation results of all threads, eliminating conflicting data through consistency checks, and integrating to obtain quantitative results for multi-nuclide components.

10. The method for constructing a database of key factors for γ-radioactive nuclide matrix simulation efficiency calibration according to any one of claims 1-9, characterized in that, This method is implemented through different units, including: a multi-nucleus parameter acquisition and preprocessing unit, a nuclide matrix efficiency coupling calibration calculation unit, a nuclide activity reconstruction error compensation calculation unit, a multi-nucleus component parallel solution processing unit, a nuclide decay timestamp matching and control unit, and a calibration key factor classification, storage, and indexing unit. The multi-nucleus parameter acquisition and preprocessing unit is connected to the nuclide decay timestamp matching and control unit via a data interface. The acquired raw data, after preliminary processing, is transmitted to the nuclide matrix efficiency coupling calibration calculation unit. Upon receiving the data, the nuclide matrix efficiency coupling calibration calculation unit calls its built-in model to complete the calibration calculation and transmits the results to... The system includes a nuclide activity reconstruction error compensation calculation unit; after correcting the calibration results, the nuclide activity reconstruction error compensation calculation unit outputs the results to a multi-nuclide component parallel calculation processing unit; the multi-nuclide component parallel calculation processing unit obtains quantitative results through multi-threaded parallel computation and sends them to a calibration key factor classification storage and indexing unit; a nuclide decay timestamp matching and control unit provides time matching parameters to the calibration calculation unit, error compensation calculation unit, and parallel calculation processing unit in real time; after receiving the quantitative results, the calibration key factor classification storage and indexing unit extracts key factors and stores them according to preset classification rules, generating a database index for querying and retrieval.

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