Methods, apparatus and media for detecting PGNAA pollutants in organic contaminated soil

Abstract

This application discloses a PGNAA detection method, device, and medium for pollutants in organically contaminated soil, relating to the field of pollutant detection. The method includes quantifying the influencing factors of the pollutant detection process to obtain quantitative functional relationships between multiple core influencing factors and core detection indicators; performing progressive correction processing on the original transient gamma neutron activation analysis signal of the organically contaminated soil to extract the characteristic signals of the pollutant target elements; constructing a quantitative mapping model between the characteristic gamma spectrum and the content of the pollutant target elements based on the quantitative functional relationships and the characteristic signals of the pollutant target elements; acquiring the key physical parameters of the organically contaminated soil to be tested and collecting the original transient gamma neutron activation analysis energy spectrum of the organically contaminated soil to be tested; inputting the key physical parameters and the original transient gamma neutron activation analysis energy spectrum of the organically contaminated soil to be tested into the quantitative mapping model to invert and obtain the content of the pollutant to be tested. This application achieves accurate quantitative detection of pollutants in organically contaminated soil.

Description

Technical Field

[0001] This application relates to the field of pollutant detection technology, and in particular to a PGNAA detection method, apparatus and medium for pollutants in organically contaminated soil. Background Technology

[0002] Current conventional methods for detecting pollutants in organically contaminated soil mainly rely on destructive sampling followed by laboratory chemical analysis. This method suffers from drawbacks such as poor sample representativeness, long detection cycles, inability to achieve in-situ dynamic monitoring, and a tendency to cause secondary pollution. Prompt Gamma Neutron Activation Analysis (PGNAA) is a nuclear analysis technique with advantages such as non-destructiveness, simultaneous multi-element detection, high detection speed, and in-situ online monitoring. However, for the quantitative detection of pollutants in organically contaminated soil using PGNAA, there is still a lack of systematic theoretical and methodological support. Existing technologies lack interference suppression and correction processing specific to organically contaminated soil scenarios, failing to meet the demand for accurate quantitative detection of pollutants in organically contaminated soil. Summary of the Invention

[0003] The purpose of this application is to provide a PGNAA detection method, device and medium for pollutants in organically contaminated soil, which solves the problem that the existing technology lacks interference suppression and correction processing for organically contaminated soil scenarios, and cannot meet the needs of accurate quantitative detection of pollutants in organically contaminated soil, thus realizing accurate quantitative detection of pollutants in organically contaminated soil.

[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for detecting PGNAA (post-polluting nitrogen atomization) of pollutants in organically contaminated soil, comprising: The influencing factors of the pollutant detection process in organically contaminated soil were quantified, and the quantitative functional relationship between multiple core influencing factors and core detection indicators was obtained. Progressive correction processing was performed on the original transient gamma neutron activation analysis signal of the collected organic contaminated soil to extract the characteristic signals of the pollutant target elements; Based on the quantitative functional relationship and the characteristic signals of the pollutant target elements, a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target elements is constructed. Key physical parameters of the organic contaminated soil to be tested were obtained, and the original transient gamma neutron activation analysis energy spectrum of the organic contaminated soil to be tested was collected. The key physical parameters of the organic contaminated soil to be tested and the original transient gamma neutron activation analysis energy spectrum are input into the quantitative mapping model to obtain the pollutant content in the organic contaminated soil to be tested.

[0005] Optionally, the process of detecting pollutants in organically contaminated soil is quantified to obtain quantitative functional relationships between multiple core influencing factors and core detection indicators, specifically including: A three-dimensional neutron transport model matching the actual occurrence state of organically contaminated soil was constructed using Monte Carlo particle transport simulation software. The single-factor variable method is adopted to fix all parameters in the three-dimensional neutron transport model except for the target variable, and to perform independent simulation calculations on multiple core variables in sequence. The multiple core variables include neutron energy range, type and content of pollutant target elements, water content of water phase and elemental composition and type of soil mineral phase. Based on the simulation output results, the quantitative functional relationship between each core variable and the detection core index is obtained by fitting, and the influence weight ratio of each core variable on the neutron signal is calculated. At the same time, based on the optimal signal-to-noise ratio principle, the optimal neutron energy range for the detection of pollutant target elements is determined.

[0006] Optionally, the progressive correction processing performed on the collected raw transient γ-neutron activation analysis signal of the organic contaminated soil to extract the characteristic signals of the pollutant target elements specifically includes: Adaptive smoothing and denoising processing is performed to eliminate statistical fluctuation noise in the original transient gamma neutron activation analysis signal; Perform iterative background subtraction to eliminate background elevation interference caused by moisture. Iterative deconvolution is performed to decouple the overlapping interference of characteristic peaks of soil background elements, separate the independent characteristic peaks of pollutant target elements, and integrate to obtain the net peak area of ​​pollutant target elements.

[0007] Optionally, the adaptive smoothing and denoising process specifically includes: using Savitzky-Golay convolutional smoothing algorithm, continuous wavelet transform denoising method, Fourier low-pass filtering method or empirical mode decomposition algorithm to smoothly fit the count-channel address data of the original transient γ neutron activation analysis signal. The iterative background subtraction process includes: using a sensitive nonlinear iterative peak clipping algorithm, an asymmetric least squares method, a morphological top-hat transformation baseline correction method, or a polynomial iterative fitting subtraction method to perform continuous background baseline fitting and subtraction on the smoothed transient gamma neutron activation analysis signal. The iterative deconvolution process includes: using a Gaussian iterative deconvolution algorithm, a global heuristic optimization algorithm, the maximum entropy method, or a deep learning-based analytical model to separate overlapping peaks in the background-subtracted transient gamma neutron activation analysis signal.

[0008] Optionally, the general expression for the quantitative mapping model between the characteristic γ-ray spectrum and the content of the target pollutant element is: ; in, It is the first i Mass content of target elements of pollutants It is a target element of pollutants i The net peak area corresponding to the characteristic γ peak. It is the background element feature peak overlap interference correction coefficient. It is the energy spectrum statistical fluctuation noise correction coefficient. It is the quantitative calibration coefficient of the detection system. It is a target element of pollutants i Thermal neutron capture cross section at optimal neutron energy It is a target element of pollutants i The absolute branching ratio corresponding to the characteristic gamma rays, It is a gamma detector for pollutant target elements i The absolute detection efficiency of characteristic energy. It is the model baseline constant term. It is a multiphase media coupling correction factor for organically contaminated soil.

[0009] Optionally, after extracting the characteristic signals of the pollutant target elements and before constructing a quantitative mapping model between the characteristic γ-ray spectrum and the content of the pollutant target elements, the PGNAA detection method for pollutants in organically contaminated soil further includes: Perform energy spectrum data quality checks and algorithm anomaly handling; The energy spectrum data quality check includes: using the characteristic peak of hydrogen as an energy stabilization anchor point; if the drift of the identified characteristic peak position compared with the theoretical characteristic gamma-ray energy exceeds the system's allowable threshold, the stabilization compensation program is automatically triggered to perform full-spectrum energy recalibration; the half-width at half maximum (WHM) of the hydrogen characteristic peak is extracted; if the WHM is wider than the calibration upper limit of the high-purity germanium detector, an automatic warning is issued and the current time window data is discarded. The algorithm's fault tolerance handling includes: when the signal-to-noise ratio of the feature peaks of the target element after smoothing and denoising is lower than the detection limit requirement, dynamically switching to the continuous wavelet transform algorithm for deep denoising; when the overlapping peak decoupling algorithm fails to converge after reaching the maximum number of iterations, automatically calling the global heuristic optimization algorithm to reinitialize the parameter boundaries to complete the deconvolution calculation of the feature peaks.

[0010] Optionally, before constructing the quantitative mapping model between the characteristic γ-ray energy spectrum and the content of pollutant target elements, the PGNAA detection method for pollutants in organically contaminated soil further includes: establishing a one-to-one correspondence between the characteristic γ-ray energy and the types of pollutant target elements based on the international nuclear database, and establishing a list of interference peaks to exclude from soil background elements.

[0011] Optionally, after constructing the quantitative mapping model between the characteristic γ-ray spectrum and the content of target elements of pollutants, the PGNAA detection method for pollutants in organically contaminated soil further includes: A standardized correction coefficient database is established, which includes correction coefficients for different clay mineral types, different water contents, and different pollution levels. This database supports both direct application and rapid on-site calibration mechanisms. The direct application mechanism refers to obtaining the correction coefficient by directly looking up a table or interpolating based on the soil type and moisture content measured on-site. The rapid on-site calibration mechanism refers to collecting clean, uncontaminated background soil samples at the edge of the target site, measuring the moisture content in situ, performing a single transient γ-neutron activation analysis background time window scan, extracting background element characteristic peaks and background baseline parameters, and then deriving and updating the baseline constant term and mineral matrix interference correction coefficient in reverse.

[0012] Secondly, this application provides a PGNAA detection device for pollutants in organically contaminated soil, comprising: The quantification unit is used to quantify the influencing factors of the pollutant detection process in organically contaminated soil, and obtain the quantitative functional relationship between multiple core influencing factors and core detection indicators. The correction unit is used to perform progressive correction processing on the original transient γ-neutron activation analysis signal of the collected organic polluted soil and extract the characteristic signals of the pollutant target elements. The construction unit is used to construct a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target element based on the quantitative functional relationship and the characteristic signal of the pollutant target element. The acquisition unit is used to acquire the key physical parameters of the organic contaminated soil to be tested, and to collect the original transient gamma neutron activation analysis energy spectrum of the organic contaminated soil to be tested. The inversion unit is used to input the key physical parameters of the organic contaminated soil to be tested and the original transient gamma neutron activation analysis energy spectrum into the quantitative mapping model to invert the pollutant content in the organic contaminated soil to be tested.

[0013] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the PGNAA detection method for pollutants in organically contaminated soil as described above.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the PGNAA detection method for pollutants in organically contaminated soil as described above.

[0015] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the PGNAA detection method for pollutants in organically contaminated soil as described above.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a PGNAA detection method, device, and medium for pollutants in organically contaminated soil. By quantifying the influencing factors of the pollutant detection process in organically contaminated soil, a quantitative functional relationship between multiple core influencing factors and core detection indicators is obtained, realizing the digital characterization of key influencing factors in the pollutant detection process. Through progressive correction processing of the collected raw transient gamma neutron activation analysis signal of organically contaminated soil, the characteristic signals of the pollutant target elements are extracted, thereby achieving correction processing of the raw signal and solving the problem of the lack of interference suppression and correction processing for organically contaminated soil scenarios in existing technologies. Based on the quantitative functional relationship and the characteristic signals of the pollutant target elements, a quantitative mapping model between the characteristic gamma spectrum and the content of the pollutant target elements is constructed, establishing a mathematical mapping relationship from the detection signal to the pollutant content. This eliminates the need for repeated modeling for a single scenario and can be directly applied to in-situ transient gamma neutron activation analysis detection in various organically contaminated sites, filling the gap in quantitative detection methods for transient gamma neutron activation analysis of organically contaminated soil. By acquiring the key physical parameters of the organic contaminated soil to be tested and collecting the original transient gamma neutron activation analysis energy spectrum, the key physical parameters and the original energy spectrum are input into the quantitative mapping model, and the pollutant content in the organic contaminated soil to be tested is obtained by inversion, thus realizing the quantitative detection of pollutants in organic contaminated soil. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart illustrating a PGNAA detection method for pollutants in organically contaminated soil, provided as an embodiment of this application; Figure 2 A schematic diagram of the functional modules of a PGNAA detection device for pollutants in organically contaminated soil provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In one exemplary embodiment, such as Figure 1 As shown, a PGNAA detection method for pollutants in organically contaminated soil is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, it includes the following steps 101 to 105. Wherein: Step 101: Quantify the influencing factors of the pollutant detection process in organic contaminated soil, and obtain the quantitative functional relationship between multiple core influencing factors and core detection indicators.

[0022] In this embodiment of the application, numerical simulation is used to quantify the independent influence of each key parameter on the detection results of transient gamma neutron activation analysis under the background of organic contaminated soil, and to complete the pre-optimization of the detection parameters.

[0023] As an optional implementation method, the process of detecting pollutants in organically contaminated soil is quantified to obtain quantitative functional relationships between multiple core influencing factors and core detection indicators, specifically including: A three-dimensional neutron transport model matching the actual occurrence state of organically contaminated soil was constructed using Monte Carlo particle transport simulation software. The single-factor variable method is adopted to fix all parameters in the three-dimensional neutron transport model except for the target variable, and to perform independent simulation calculations on multiple core variables in sequence. The multiple core variables include neutron energy range, type and content of pollutant target elements, water content of water phase and elemental composition and type of soil mineral phase. Based on the simulation output results, the quantitative functional relationship between each core variable and the detection core index is obtained by fitting, and the influence weight ratio of each core variable on the neutron signal is calculated. At the same time, based on the optimal signal-to-noise ratio principle, the optimal neutron energy range for the detection of pollutant target elements is determined.

[0024] This implementation method, through standardized Monte Carlo neutron transport modeling and single-factor variable analysis procedures, systematically quantifies the independent influence and weighting of key parameters such as neutron energy, target element type / content, moisture content, and mineral element composition on the detection results of transient gamma neutron activation analysis. It clarifies the optimal neutron energy detection range under the background of organically contaminated soil, providing a universal parameter design basis for transient gamma neutron activation analysis detection. It improves the detection sensitivity and signal-to-noise ratio of target element characteristic signals from the source, solving the problems of weak signal and large interference caused by indiscriminate detection in existing technologies.

[0025] In this embodiment, the Monte Carlo particle transport simulation software MCNP6.2 is used to construct a three-dimensional neutron transport model that matches the actual detection system. As a feasible alternative, the three-dimensional neutron transport model can also be constructed using other commercial or open-source software with neutron transport calculation capabilities, including but not limited to: Geant4, OpenMC, FLUKA, PHITS, etc. The model is divided into three phases: the soil mineral phase is defined based on the standard elemental composition, density, and porosity parameters of common clay minerals (such as montmorillonite and kaolinite) in contaminated sites, covering the matrix characteristics of different soil types; the aqueous phase has a volumetric water content range of 0% to 30%, covering the full hydrological conditions of unsaturated organic contaminated sites; the organic pollutant phase is based on the characteristic target elements of organic pollutants (carbon and hydrogen), with elemental content ranges set (e.g., carbon: 0~10 wt%, hydrogen: 0~5 wt%), covering contaminated sites of pollution levels 1 to 5; at the same time, the model boundary conditions are set, including the type of neutron source, emission geometry, energy range, and the detection efficiency and solid angle parameters of the gamma-ray detector, reconstructing the complete physical process of transient gamma neutron activation analysis and detection.

[0026] The type of neutron source is not limited to a single source and can be selected according to the specific detection scenario of organic contaminated soil: ① Reactor or spallation neutron source: provides high-flux, high-collimation cold / hot neutron beams, mainly used in laboratory-level high-precision multimodal imaging and trace element quantitative detection scenarios. ② Controllable neutron generator: provides monoenergetic fast neutrons, with electronic start-stop control and pulse emission capabilities, mainly used for in-situ physical simulation well detection calibration and organic contaminated soil depth profile time-series detection scenarios. ③ Isotope neutron source (including but not limited to californium-252 source or americium-beryllium source): among which californium-252 source ( 252 Cf has the characteristics of small size and high spontaneous fission neutron yield, making it suitable for portable in-situ rapid detection equipment; americium-beryllium source (Am-Be) has the characteristics of long half-life and extremely stable flux, making it suitable for long-term, maintenance-free continuous monitoring scenarios in the process of remediation of contaminated sites.

[0027] In this embodiment, a single-factor variable method is used, fixing all parameters in the model except for the target variable, and independently simulating the following core variables in sequence: setting three typical energy ranges—thermal neutrons, hyperthermal neutrons, and fast neutrons—to cover the entire energy range; independently changing the types and contents of target elements carbon and hydrogen, while keeping other parameters fixed; independently changing the water content of the aqueous phase, while keeping other parameters fixed; and independently changing the elemental composition and type of soil mineral phases (such as montmorillonite, kaolinite, and illite), while keeping other parameters fixed. A sufficient number of simulations are performed for each variable condition, and the core output indicators include the probability of nuclear reactions occurring with the target elements, the characteristic gamma-ray yield, and the detection signal-to-noise ratio.

[0028] Based on the simulation output, a quantitative functional relationship (such as linear, exponential, or polynomial form) between each single-factor variable and the core detection index was fitted. The influence weight of each factor on the neutron signal was calculated (e.g., through analysis of variance or normalized sensitivity coefficient), clarifying the independent influence of each factor on the detection results of the transient gamma neutron activation analysis. Simultaneously, based on the principle of optimal signal-to-noise ratio, the signal-to-noise ratios of the target element characteristic peaks under different neutron energy ranges were compared to determine the optimal neutron energy range for target element detection in the organic contaminated soil scenario, completing the preliminary optimization of the transient gamma neutron activation analysis detection parameters. Furthermore, the moisture interference correction coefficients corresponding to different water contents, matrix correction coefficients corresponding to different mineral matrices, and background element overlap interference correction coefficients were fitted, providing prior parameters for subsequent correction and modeling.

[0029] In this embodiment, MCNP6.2 software is used to construct a three-dimensional neutron transport model that matches the actual detection system, and the neutron source is selected as... 252 Cf, an isotropic point source with a neutron emission rate of 1×10⁻⁶. 7 n / s, energy range 0~10 MeV; sample geometry is cylindrical (diameter 5 cm, height 10 cm); material composition includes: montmorillonite clay (silicon 21.5 wt%, aluminum 9.2 wt%, iron 2.8 wt%, oxygen 48.5 wt%, other 18 wt%, density 1.6 g / cm³). 3 ), pure water (volume water content set at 0%, 10%, 20%, 30% respectively), n-hexadecane (C 16 H 34As a petroleum hydrocarbon pollutant (total carbon content 0.5–8 wt%), a high-purity germanium detector was used, 25 cm from the sample center, with an energy response range of 0.1–10 MeV. Single-factor simulation conditions were set up, keeping other parameters in the model constant. The simulations were performed independently on four core variables: neutron energy (thermal neutron 0.025 eV, ultrathermal neutron 0.1 eV–1 keV, fast neutron 0.1–10 MeV), target element carbon / hydrogen content (carbon: 0, 0.5, 2, 5, 8 wt%; hydrogen: 0.1, 0.5, 1.5, 2.5 wt%), sample water content (0%, 10%, 20%, 30%), and clay mineral type (montmorillonite, kaolinite, illite). For each condition, the simulated neutron count (NPS) was set to 1 × 10⁻⁶. 8 To ensure a relative statistical error of <0.5%, the net counts and background standard deviations of the characteristic peaks for carbon (4.945 MeV) and hydrogen (2.223 MeV) under different operating conditions were statistically analyzed to calculate the signal-to-noise ratio (SNR) (SNR = net peak count / background standard deviation). Based on the principle of optimal SNR, comparisons revealed that carbon and hydrogen exhibited the largest capture cross-sections under thermal neutron excitation, with SNRs 8–12 times higher than those for fast neutrons. Therefore, 0.025 eV thermal neutrons were determined as the optimal neutron energy for detecting organically contaminated clay. Simultaneously, moisture interference correction coefficients corresponding to different water contents were obtained through fitting. f w =1 / (1+0.12×θ) (θ is the volumetric water content, 0~0.3); matrix correction factor for clay minerals. f m (Montmorillonite 0.95, Kaolinite 1.02, Illite 0.98); Overlap interference correction coefficients for background elements. (The carbon peak is slightly disturbed by iron and silicon elements.) ).

[0030] Step 102: Perform progressive correction processing on the original transient γ-neutron activation analysis signal of the collected organic contaminated soil to extract the characteristic signals of the pollutant target elements.

[0031] In this embodiment, a progressive algorithm is used to eliminate multi-source coupling interference in organically contaminated soil and accurately extract the characteristic signals of target elements. Correction processing is then performed on the original transient γ-neutron activation analysis signals of the collected organically contaminated soil.

[0032] As an optional implementation, the progressive correction processing performed on the collected raw transient γ-neutron activation analysis signal of the organically contaminated soil to extract the characteristic signals of the pollutant target elements specifically includes: Adaptive smoothing and denoising processing is performed to eliminate statistical fluctuation noise in the original transient gamma neutron activation analysis signal; Perform iterative background subtraction to eliminate background elevation interference caused by moisture. Iterative deconvolution is performed to decouple the overlapping interference of characteristic peaks of soil background elements, separate the independent characteristic peaks of pollutant target elements, and integrate to obtain the net peak area of ​​pollutant target elements.

[0033] This implementation method addresses three core coupled interferences in organically contaminated soil: statistical fluctuation noise, background elevation caused by moisture, and overlapping characteristic peaks of soil background elements. It constructs a standardized algorithm processing flow for smoothing and denoising, background correction, and decoupling of overlapping peaks. Through targeted algorithm selection and operational logic, it can systematically decouple multi-source interference signals and eliminate the nonlinear response of neutron signals and target element content caused by interference. This solves the pain point of existing technologies lacking targeted interference treatment methods for organically contaminated soil scenarios.

[0034] As an optional implementation, the adaptive smoothing and denoising process specifically includes: using Savitzky-Golay convolutional smoothing algorithm, continuous wavelet transform denoising method, Fourier low-pass filtering method or empirical mode decomposition algorithm to smooth and fit the count-channel address data of the original transient γ neutron activation analysis signal. The iterative background subtraction process includes: using a sensitive nonlinear iterative peak clipping algorithm, an asymmetric least squares method, a morphological top-hat transformation baseline correction method, or a polynomial iterative fitting subtraction method to perform continuous background baseline fitting and subtraction on the smoothed transient gamma neutron activation analysis signal. The iterative deconvolution process includes: using a Gaussian iterative deconvolution algorithm, a global heuristic optimization algorithm, the maximum entropy method, or a deep learning-based analytical model to separate overlapping peaks in the background-subtracted transient gamma neutron activation analysis signal.

[0035] This implementation method provides multiple optional algorithm implementation paths, enabling the method to flexibly select the optimal algorithm based on actual data quality and computing resources, thereby enhancing the method's adaptability and robustness.

[0036] In this embodiment, the adaptive smoothing and denoising process employs the Savitzky-Golay convolutional smoothing algorithm to smoothly fit the count-channel data of the original energy spectrum. A sliding window width of 5-15 channels and a polynomial fitting order of 2-5 are set. Weighted least-squares fitting is performed on the counts at each channel, eliminating random noise caused by neutron count statistical fluctuations while preserving the peak position, shape, and net peak area information of the characteristic peaks, resulting in a denoised smooth energy spectrum. Mirror filling or constant extrapolation is used at the boundaries to avoid edge distortion.

[0037] In this embodiment, the iterative background subtraction process employs a sensitive nonlinear iterative peak clipping algorithm to fit and subtract a continuous background baseline from the smoothed energy spectrum. The number of iterations is set to 10-50, and the filter window width gradually increases from one channel to a number of channels comparable to the full width at half maximum (FWHM) of the characteristic peak. Through iterative peak clipping, the continuous background rise signal caused by the neutron moderation effect of hydrogen in water is separated, and the fitted background baseline is subtracted from the smoothed energy spectrum. Simultaneously, based on the water interference correction coefficient obtained in step 101, background interference correction coefficients corresponding to different water contents are fitted, and amplitude correction is performed on the subtracted energy spectrum to obtain the background-corrected energy spectrum.

[0038] In this embodiment, the iterative deconvolution processing employs a Gaussian iterative deconvolution algorithm (such as Levenberg-Marquardt nonlinear fitting) to separate overlapping peaks in the background-corrected energy spectrum. Based on the ENDF / B-VIII.0 international nuclear database, the characteristic gamma-ray peak positions, full width at half maximum (FWHM), and other peak shape parameters of common soil background elements (silicon, aluminum, iron, oxygen, etc.) are pre-calibrated. Using a Gaussian function as the peak shape model, a multi-peak fitting function is constructed, containing characteristic peaks of target elements (carbon, hydrogen) and multiple background interference peaks. Multi-peak iterative fitting is performed on the energy spectrum, with 50-100 iterations. The convergence threshold is set to a relative change of <1×10⁴ squared sum of squared residuals between two adjacent fittings. -6 The independent characteristic peaks of the target elements carbon and hydrogen are separated, and the net peak area is obtained by integration. At the same time, the background element overlap interference correction coefficient and mineral matrix interference correction coefficient are obtained by combining the prior correction coefficients from step 101. Finally, the corrected target element characteristic energy spectrum is output for subsequent quantitative modeling.

[0039] In this embodiment, taking the original energy spectrum (4096 channels, 0~8 MeV energy) of a sample with 20% water content and 5% carbon content as an example, the first-order adaptive smoothing and denoising process is performed: the Savitzky-Golay convolution smoothing algorithm is adopted, the sliding window is set to a full width of 7 channels, third-order polynomial fitting is used, and the convolution kernel weight coefficients are [-0.0952, 0.1429, 0.2857, 0.3333, 0.2857, 0.1429, -0.0952]; mirror filling is used at the boundary of the energy spectrum. After processing, the standard deviation of random noise is reduced from 124 counts to 38 counts, the peak position shift of the characteristic peak is less than 0.5 channels, and the smoothed energy spectrum data is output. Second-order continuous background subtraction is performed: a sensitive nonlinear iterative peak-shaving algorithm is used to fit the smoothed energy spectrum to the continuous background. The number of iterations is set to 30, and the window width increases synchronously from channel 1 to channel 30 with the number of iterations. The convergence threshold is that the relative change in background count between two adjacent iterations is <0.1%. The continuous background baseline caused by the slowing effect of hydrogen in water is obtained by fitting and subtracted (the background of the carbon peak region is reduced from ~850 counts to ~120 counts). Combined with the water interference correction coefficient obtained in step 101... f w The energy spectrum after background correction is normalized in amplitude, and the background-corrected energy spectrum data is output. Third-order overlapping peak decoupling processing is performed: based on the ENDF / B-VIII.0 kernel database, the characteristic peak positions and HWV parameters of carbon (4.945 MeV, HWV 68 keV), hydrogen (2.223 MeV, HWV 35 keV), silicon (3.539 MeV, HWV 52 keV), aluminum (1.779 MeV, HWV 29 keV), and iron (7.631 MeV, HWV 96 keV) are pre-calibrated. A multi-peak model containing 5 Gaussian peaks is constructed using the Levenberg-Marquardt nonlinear fitting algorithm, with 100 iterations and a convergence threshold of 1×10⁻⁶. -6 Independent characteristic peaks of the target elements carbon and hydrogen were obtained by separation, and the net peak area S was obtained by integration. C =12560 count (original overlapping peak area 15800, deducting silicon element contribution 3240), S H =8740 count. Simultaneously verify and fine-tune the matrix correction factor. f m =0.95, overlap interference correction coefficient f b =0.98, output normalized corrected energy spectrum.

[0040] Step 103: Based on the quantitative functional relationship and the characteristic signals of the pollutant target elements, construct a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target elements.

[0041] In this embodiment, a universal quantitative mapping model is established to achieve accurate quantification of target elements of organic pollutants based on characteristic γ-ray spectra.

[0042] As an optional implementation, the general expression for the quantitative mapping model between the characteristic γ-ray spectrum and the content of the target pollutant element is: ; in, It is the first i Mass content of target elements of pollutants It is a target element of pollutants i The net peak area corresponding to the characteristic γ peak. It is the background element feature peak overlap interference correction coefficient. It is the energy spectrum statistical fluctuation noise correction coefficient. It is the quantitative calibration coefficient of the detection system. It is a target element of pollutants i Thermal neutron capture cross section at optimal neutron energy It is a target element of pollutants i The absolute branching ratio corresponding to the characteristic gamma rays, It is a gamma detector for pollutant target elements i The absolute detection efficiency of characteristic energy. It is the model baseline constant term. It is a multiphase media coupling correction factor for organically contaminated soil.

[0043] Among them, the multiphase medium coupling correction factor The expression is: In the formula, It is an independent weighting coefficient for water content. These are the independent weighting coefficients of the mineral matrix. It is the nonlinear cross-coupling coefficient of moisture and minerals on the distortion of thermal neutron flux. It is the moisture interference correction factor. It is the mineral matrix interference correction coefficient.

[0044] This implementation method employs a two-step modeling logic of qualitative calibration and quantitative fitting. First, it establishes a one-to-one correspondence between target elements and characteristic gamma-ray energies through a nuclear database, achieving qualitative identification of target elements. Then, it combines the quantification results of multiple factors and interference correction coefficients to construct a universal quantitative mapping model covering different mineral types, water contents, and pollution levels. This eliminates the need for repeated modeling for single scenarios and can be directly applied to in-situ transient gamma-neutron activation analysis and detection in various organically contaminated sites, filling the gap in quantitative detection methods for transient gamma-neutron activation analysis of organically contaminated soil.

[0045] In this embodiment, the qualitative calibration of target elements is completed based on the internationally recognized ENDF / B-VIII.0 nuclear database. Characteristic gamma-ray energies and branching ratios, etc., corresponding to the thermal neutron capture reactions of characteristic target elements of organic pollutants, carbon and hydrogen, are screened to establish a one-to-one correspondence between target element types and characteristic gamma-ray energies (e.g., carbon corresponds to 4.945 MeV, hydrogen corresponds to 2.223 MeV). This is then mapped onto the transient gamma spectrum to form a list of characteristic peak positions for the target elements. Simultaneously, characteristic gamma-ray energies of common soil background elements (silicon, aluminum, iron, oxygen, etc.) are screened, and a list of background interference peak positions to be excluded is established on the energy spectrum for overlapping peak identification and exclusion in step 102.

[0046] For the corrected target element characteristic energy spectrum obtained in step 102, a Gaussian function is used for single-peak fitting to extract the core parameters of net peak area, peak position, and full width at half maximum (FWHM) of the corresponding characteristic peak of the target element. The net peak area of ​​the characteristic peak is used as the core response parameter of the target element content. The single-factor influence quantification results obtained in step 101, as well as the moisture interference correction coefficient, mineral matrix interference correction coefficient, and background element overlap interference correction coefficient obtained in step 102, are introduced into the model as multi-factor nonlinear correction terms to eliminate the interference of environmental parameters on the quantitative results. With the mass content of the target element as the dependent variable and the net peak area of ​​the characteristic peak as the core independent variable, combined with the multi-factor correction terms, a characteristic γ-energy spectrum-target element quantitative mapping model under the background of organic polluted soil is established through linear regression and nonlinear fitting.

[0047] Based on the Monte Carlo simulation results in step 101, the correction coefficients for different clay mineral types (such as montmorillonite, kaolinite, and illite), different water contents (0-30%), and different pollution levels (variations in carbon and hydrogen content) are pre-calibrated to form a standardized correction coefficient database. In practical applications, the database employs two adaptive mechanisms: ① Direct application mechanism: For typical soil types, neutron source types, and sites with conventional water content already covered in the database, the corresponding correction coefficients can be directly obtained by looking up tables or interpolation based on the soil type and water content measured on-site, without the need for repeated simulations or calibrations, directly retrieving pollutant content. ② Rapid on-site calibration mechanism: For extremely special and unknown sites not covered in the database (such as high-humus soils, special industrial slag, or neutron generators with different energy spectra), a limited, one-time on-site calibration is performed before testing. Clean background soil samples (uncontaminated with organic matter) were collected from the edge of the target site. Their moisture content was measured in situ, and a single transient gamma neutron activation analysis was performed to scan the background time window. Characteristic peaks of background elements and background baseline parameters were extracted from the energy spectrum. These parameters were then substituted into the general model from step 103 to derive and update the site-specific baseline constants and mineral matrix interference correction coefficients. After updating the parameters, the general model can be directly applied to the full-area dynamic continuous detection of this specific site.

[0048] In this embodiment, a one-to-one correspondence between characteristic gamma-ray energies and target element types is established based on the ENDF / B-VIII.0 international nuclear database. The characteristic gamma-ray principal energy for carbon in thermal neutron capture reactions is 4.945 ± 0.010 MeV, and for hydrogen it is 2.223 ± 0.005 MeV. Simultaneously, a list of interference peak positions for background elements such as silicon (3.539 MeV), aluminum (1.779 MeV), and iron (7.631 MeV) is established to exclude them. Obvious characteristic peaks appear at 4.94 MeV and 2.22 MeV in the measured energy spectrum, consistent with the calibration of carbon and hydrogen, achieving accurate qualitative identification of the target elements. For the standardized energy spectrum corrected in step 102, a Gaussian function is used for single-peak fitting to extract the net peak area S corresponding to the characteristic peaks of carbon and hydrogen. C =12560 count, S H =8740 counts were used as the core response parameter for the target element content. Twelve standard samples (carbon content 0.5, 2, 5, and 8 wt%, each corresponding to three moisture contents of 0%, 15%, and 30%) were used for model training. The carbon and hydrogen content of the standard samples were used as dependent variables, and the net peak area of ​​the corresponding characteristic peaks as independent variables. The moisture interference correction coefficient, mineral matrix correction coefficient, and background element overlap interference correction coefficient obtained in step 101 were introduced. A universal quantitative mapping model was obtained through least squares linear regression fitting. For carbon, the detection system has a quantitative calibration coefficient for carbon. The baseline constant term of the model for carbon. wt%; For hydrogen, the quantitative calibration coefficient of the detection system for hydrogen. The baseline constant term of the model for hydrogen. wt%. Five independently prepared validation samples (carbon content 1.0, 3.0, 6.0, 7.5 wt%, and water content 5% and 25% randomly combined) were used for blind testing. The content obtained by transient gamma neutron activation analysis was compared with the content measured by gas chromatography. The average relative error was ≤1%, and the linear correlation coefficient R was ≤1%. 2 The method detection limit is approximately 0.1 wt% (carbon element) with a value ≥0.999; the average relative error for hydrogen element validation is ≤2%, and the model meets the requirements.

[0049] Step 104: Obtain the key physical parameters of the organic contaminated soil to be tested, and collect the original transient γ-neutron activation analysis energy spectrum of the organic contaminated soil to be tested.

[0050] In this embodiment, a portable time-domain reflectometer or frequency-domain reflectometer is used to insert a probe into the contaminated soil to be tested and read the volumetric water content. Based on site geological survey data or on-site rapid mineral analysis tools such as portable X-ray diffractometers, the main clay mineral types in the soil and special background elements such as high iron and high aluminum content are determined. After the above parameters are measured, they are recorded and input into the data processing terminal.

[0051] Specifically, the probe of the transient gamma neutron activation analysis device is placed close to the soil surface or into a shallow hole to collect the original energy spectrum of organically contaminated soil. During the collection process, the probe position is kept fixed to avoid vibration and external gamma ray interference.

[0052] Step 105: Input the key physical parameters of the organic contaminated soil to be tested and the original transient γ-neutron activation analysis energy spectrum into the quantitative mapping model to invert and obtain the pollutant content in the organic contaminated soil to be tested.

[0053] In this embodiment of the application, after the data acquisition is completed, the data is transmitted to the data processing terminal, and smoothing and denoising, background subtraction, and feature peak algorithms are executed sequentially to obtain the net peak area by integration. At the same time, based on the prior parameters in the established standardized correction coefficient database, the moisture interference correction coefficient, mineral matrix correction coefficient, and background element overlap interference correction coefficient are dynamically fitted to obtain the correction coefficient.

[0054] Based on the moisture interference correction coefficient, mineral matrix correction coefficient, and background element overlap interference correction coefficient obtained from the fitting, the multiphase medium coupling correction factor is calculated. At the same time, the statistical fluctuation noise correction coefficient, detection system calibration coefficient, target element capture cross section, characteristic γ branch ratio, detector absolute detection efficiency, and model baseline constant are extracted from the database. The above coefficients and net peak area are substituted into the general model in step 103 to calculate the precise mass content of carbon and hydrogen elements respectively. Based on the stoichiometry of the target organic pollutants, the total content of pollutants is further inverted, or the mass content of carbon and hydrogen elements is directly output as an indicator of the degree of organic pollution.

[0055] By implementing steps 101 to 105 above, the influencing factors of the pollutant detection process in organically contaminated soil are quantified, resulting in quantitative functional relationships between multiple core influencing factors and core detection indicators. This achieves digital characterization of key influencing factors during pollutant detection. By performing progressive correction processing on the collected raw transient gamma neutron activation analysis signal of organically contaminated soil, the characteristic signals of pollutant target elements are extracted, thus achieving correction processing of the raw signal and solving the problem of existing technologies lacking interference suppression and correction processing for organically contaminated soil scenarios. Based on the quantitative functional relationships and the characteristic signals of the pollutant target elements, a quantitative mapping model between the characteristic gamma energy spectrum and the content of pollutant target elements is constructed, establishing a mathematical mapping relationship from the detection signal to the pollutant content. This eliminates the need for repeated modeling for a single scenario and can be directly applied to in-situ transient gamma neutron activation analysis detection in various organically contaminated sites, filling the gap in quantitative detection methods for transient gamma neutron activation analysis of organically contaminated soil. By acquiring the key physical parameters of the organic contaminated soil to be tested and collecting the original transient gamma neutron activation analysis energy spectrum, the key physical parameters and the original energy spectrum are input into the quantitative mapping model, and the pollutant content in the organic contaminated soil to be tested is obtained by inversion, thus realizing the quantitative detection of pollutants in organic contaminated soil.

[0056] In another exemplary embodiment of this application, after the characteristic signals of the pollutant target elements are extracted and before the quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target elements is constructed, the method may further include: performing energy spectrum data quality checks and algorithm anomaly tolerance processing.

[0057] The energy spectrum data quality check includes: using the characteristic peak of hydrogen as an energy stabilization anchor point; if the drift of the identified characteristic peak position compared with the theoretical characteristic gamma-ray energy exceeds a preset allowable threshold, the stabilization compensation program is automatically triggered to perform full-spectrum energy recalibration; the half-width at half maximum (WHM) of the hydrogen characteristic peak is extracted; if the WHM is wider than the calibration upper limit of the high-purity germanium detector, an automatic warning is issued and the current time window data is discarded. The algorithm's fault tolerance handling includes: when the signal-to-noise ratio of the feature peaks of the target element after smoothing and denoising is lower than the detection limit requirement, dynamically switching to the continuous wavelet transform algorithm for deep denoising; when the overlapping peak decoupling algorithm fails to converge after reaching the maximum number of iterations, automatically calling the global heuristic optimization algorithm to reinitialize the parameter boundaries to complete the deconvolution calculation of the feature peaks.

[0058] This implementation method, through energy spectrum data quality verification and algorithm anomaly tolerance processing, ensures the robustness of the detection method in complex engineering environments and improves the stability and reliability of the method.

[0059] In another exemplary embodiment of this application, before constructing the quantitative mapping model between the characteristic gamma spectrum and the content of pollutant target elements, the method may further include: establishing a one-to-one correspondence between the characteristic gamma ray energy and the types of pollutant target elements based on the international nuclear database, and establishing an exclusion list of interference peaks of soil background elements.

[0060] This implementation method enables accurate qualitative identification of target elements, providing a reliable basis for element attribution in subsequent quantitative analysis and avoiding quantitative errors caused by element misjudgment.

[0061] In another exemplary embodiment of this application, after constructing the quantitative mapping model between the characteristic γ-ray spectrum and the content of pollutant target elements, the method may further include: forming a standardized correction coefficient database, the standardized correction coefficient database containing correction coefficients under different clay mineral types, different water contents, and different pollution levels; the standardized correction coefficient database supports a direct application mechanism and a rapid on-site calibration mechanism, wherein the direct application mechanism refers to obtaining the correction coefficients by directly looking up tables or interpolating based on the soil type and water content measured on-site, and the rapid on-site calibration mechanism refers to collecting clean, uncontaminated background soil samples at the edge of the target site, measuring the water content in situ, performing a single transient γ-neutron activation analysis background time window scan, extracting background element characteristic peaks and background baseline parameters, and deriving and updating the baseline constant term and mineral matrix interference correction coefficients in reverse.

[0062] In this embodiment, based on the Monte Carlo simulation results of step 101 and the quantitative mapping model constructed in step 103, the correction coefficients for different clay mineral types (such as montmorillonite, kaolinite, illite), different water contents (0~30%), and different pollution levels (changes in carbon and hydrogen content) are pre-calibrated to form a standardized correction coefficient database. In practical applications, the standardized correction coefficient database supports the following two adaptive adaptation mechanisms: ① Direct application mechanism: For typical soil types, neutron source types, and sites with conventional water content already covered in the database, the corresponding correction coefficients can be directly obtained by looking up tables or interpolation based on the soil type and water content measured on-site, without the need for repeated simulation or calibration, and the pollutant content can be directly inverted. ② Rapid on-site calibration mechanism: For extremely special unknown sites not covered by the database (such as high humus soil, special industrial slag, or neutron generators with different energy spectra), a limited one-time on-site calibration is performed before detection. Clean background soil samples (uncontaminated with organic matter) were collected from the edge of the target site. Their moisture content was measured in situ, and a single transient gamma neutron activation analysis was performed to scan the background time window. Characteristic peaks of background elements and background baseline parameters were extracted from the energy spectrum. These parameters were then substituted into the general model from step 103 to derive and update the site-specific baseline constants and mineral matrix interference correction coefficients. After updating the parameters, the general model can be directly applied to the full-area dynamic continuous detection of this specific site.

[0063] This implementation method, by constructing a standardized correction coefficient database and supporting two adaptive adaptation mechanisms, eliminates the need for repeated modeling for a single scenario. It can be directly applied to various known or unknown organic contaminated sites, greatly improving the method's versatility and field applicability.

[0064] Based on the same inventive concept, this application also provides a PGNAA detection device for organic pollutants in contaminated soil, used to implement the aforementioned PGNAA detection method for pollutants in organically contaminated soil. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more PGNAA detection device embodiments for organic pollutants in contaminated soil provided below can be found in the limitations of the PGNAA detection method for organic pollutants in contaminated soil described above, and will not be repeated here.

[0065] In one exemplary embodiment, such as Figure 2 As shown, a PGNAA detection device for pollutants in organically contaminated soil is provided, comprising: The quantification unit 201 is used to quantify the influencing factors of the pollutant detection process in organic contaminated soil and obtain the quantitative functional relationship between multiple core influencing factors and core detection indicators. The correction unit 202 is used to perform progressive correction processing on the original transient γ-neutron activation analysis signal of the collected organic polluted soil and extract the characteristic signals of the pollutant target elements. Construction unit 203 is used to construct a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target element based on the quantitative functional relationship and the characteristic signal of the pollutant target element; The acquisition unit 204 is used to acquire the key physical parameters of the organic contaminated soil to be tested, and to collect the original transient γ-neutron activation analysis energy spectrum of the organic contaminated soil to be tested. The inversion unit 205 is used to input the key physical parameters of the organic contaminated soil to be tested and the original transient γ-neutron activation analysis energy spectrum into the quantitative mapping model to invert the pollutant content in the organic contaminated soil to be tested.

[0066] This implementation method provides parameter design basis for detection through quantification unit, systematically decouples multi-source interference through correction unit, establishes a universal quantitative mapping model through construction unit, and achieves accurate quantitative detection of pollutants through acquisition unit and inversion unit. It solves the problems of lack of targeted interference treatment and poor quantitative accuracy in existing technologies, and realizes accurate, rapid and in-situ quantitative detection of pollutants in organic contaminated soil.

[0067] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores PGNAA detection data of pollutants in organically contaminated soil. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a PGNAA detection method for pollutants in organically contaminated soil.

[0068] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0069] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0070] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0071] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0072] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0073] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0074] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A PGNAA detection method for pollutants in organically contaminated soil, characterized in that, The PGNAA detection method for pollutants in organically contaminated soil includes: The influencing factors of the pollutant detection process in organically contaminated soil were quantified, and the quantitative functional relationship between multiple core influencing factors and core detection indicators was obtained. Progressive correction processing was performed on the original transient gamma neutron activation analysis signal of the collected organic contaminated soil to extract the characteristic signals of the pollutant target elements; Based on the quantitative functional relationship and the characteristic signals of the pollutant target elements, a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target elements is constructed. Key physical parameters of the organic contaminated soil to be tested were obtained, and the original transient gamma neutron activation analysis energy spectrum of the organic contaminated soil to be tested was collected. The key physical parameters of the organic contaminated soil to be tested and the original transient gamma neutron activation analysis energy spectrum are input into the quantitative mapping model to obtain the pollutant content in the organic contaminated soil to be tested. After extracting the characteristic signals of the pollutant target elements and before constructing a quantitative mapping model between the characteristic γ-ray spectrum and the content of the pollutant target elements, the PGNAA detection method for pollutants in organically contaminated soil further includes: Perform energy spectrum data quality checks and algorithm anomaly handling; The energy spectrum data quality check includes: using the characteristic peak of hydrogen as an energy stabilization anchor point; if the drift of the identified characteristic peak position compared with the theoretical characteristic gamma-ray energy exceeds the system's allowable threshold, the stabilization compensation program is automatically triggered to perform full-spectrum energy recalibration; the half-width at half maximum (WHM) of the hydrogen characteristic peak is extracted; if the WHM is wider than the calibration upper limit of the high-purity germanium detector, an automatic warning is issued and the current time window data is discarded. The algorithm's fault tolerance handling includes: when the signal-to-noise ratio of the feature peaks of the target element after smoothing and denoising is lower than the detection limit requirement, dynamically switching to the continuous wavelet transform algorithm for deep denoising; when the overlapping peak decoupling algorithm fails to converge after reaching the maximum number of iterations, automatically calling the global heuristic optimization algorithm to reinitialize the parameter boundaries to complete the deconvolution calculation of the feature peaks.

2. The PGNAA detection method for pollutants in organically contaminated soil according to claim 1, characterized in that, The process of detecting pollutants in organically contaminated soil is subjected to quantification of influencing factors, resulting in quantitative functional relationships between multiple core influencing factors and core detection indicators. Specifically, these relationships include: A three-dimensional neutron transport model matching the actual occurrence state of organically contaminated soil was constructed using Monte Carlo particle transport simulation software. The single-factor variable method is adopted to fix all parameters in the three-dimensional neutron transport model except for the target variable, and to perform independent simulation calculations on multiple core variables in sequence. The multiple core variables include neutron energy range, type and content of pollutant target elements, water content of water phase and elemental composition and type of soil mineral phase. Based on the simulation output results, the quantitative functional relationship between each core variable and the detection core index is obtained by fitting, and the influence weight ratio of each core variable on the neutron signal is calculated. At the same time, based on the optimal signal-to-noise ratio principle, the optimal neutron energy range for the detection of pollutant target elements is determined.

3. The PGNAA detection method for pollutants in organically contaminated soil according to claim 1, characterized in that, The progressive correction processing performed on the collected raw transient gamma neutron activation analysis signal of organic contaminated soil extracts the characteristic signals of pollutant target elements, specifically including: Adaptive smoothing and denoising processing is performed to eliminate statistical fluctuation noise in the original transient gamma neutron activation analysis signal; Perform iterative background subtraction to eliminate background elevation interference caused by moisture. Iterative deconvolution is performed to decouple the overlapping interference of characteristic peaks of soil background elements, separate the independent characteristic peaks of pollutant target elements, and integrate to obtain the net peak area of ​​pollutant target elements.

4. The PGNAA detection method for pollutants in organically contaminated soil according to claim 3, characterized in that, The adaptive smoothing and denoising process includes: using Savitzky-Golay convolutional smoothing algorithm, continuous wavelet transform denoising method, Fourier low-pass filtering method or empirical mode decomposition algorithm to smooth and fit the count-channel address data of the original transient γ neutron activation analysis signal. The iterative background subtraction process includes: using a sensitive nonlinear iterative peak clipping algorithm, an asymmetric least squares method, a morphological top-hat transformation baseline correction method, or a polynomial iterative fitting subtraction method to perform continuous background baseline fitting and subtraction on the smoothed transient gamma neutron activation analysis signal. The iterative deconvolution process includes: using a Gaussian iterative deconvolution algorithm, a global heuristic optimization algorithm, the maximum entropy method, or a deep learning-based analytical model to separate overlapping peaks in the background-subtracted transient gamma neutron activation analysis signal.

5. The PGNAA detection method for pollutants in organically contaminated soil according to claim 1, characterized in that, The general expression for the quantitative mapping model between the characteristic γ-ray spectrum and the content of the target pollutant element is: ； in, It is the first i Mass content of target elements of pollutants It is a target element of pollutants i The net peak area corresponding to the characteristic γ peak. It is the background element feature peak overlap interference correction coefficient. It is the energy spectrum statistical fluctuation noise correction coefficient. It is the quantitative calibration coefficient of the detection system. It is a target element of pollutants i Thermal neutron capture cross section at optimal neutron energy It is a target element of pollutants i The absolute branching ratio corresponding to the characteristic gamma rays, It is a gamma detector for pollutant target elements i The absolute detection efficiency of characteristic energy. It is the model baseline constant term. It is a multiphase media coupling correction factor for organically contaminated soil.

6. The PGNAA detection method for pollutants in organically contaminated soil according to claim 1, characterized in that, Before constructing the quantitative mapping model between the characteristic gamma-ray energy spectrum and the content of pollutant target elements, the PGNAA detection method for pollutants in organically contaminated soil further includes: establishing a one-to-one correspondence between the characteristic gamma-ray energy and the types of pollutant target elements based on the international nuclear database, and establishing a list of interference peaks to exclude from soil background elements.

7. The PGNAA detection method for pollutants in organically contaminated soil according to claim 1, characterized in that, After constructing the quantitative mapping model between the characteristic γ-ray spectrum and the content of target elements in pollutants, the PGNAA detection method for pollutants in organically contaminated soil further includes: A standardized correction coefficient database is established, which includes correction coefficients for different clay mineral types, different water contents, and different pollution levels. This database supports both direct application and rapid on-site calibration mechanisms. The direct application mechanism refers to obtaining the correction coefficient by directly looking up a table or interpolating based on the soil type and moisture content measured on-site. The rapid on-site calibration mechanism refers to collecting clean, uncontaminated background soil samples at the edge of the target site, measuring the moisture content in situ, performing a single transient γ-neutron activation analysis background time window scan, extracting background element characteristic peaks and background baseline parameters, and then deriving and updating the baseline constant term and mineral matrix interference correction coefficient in reverse.

8. A PGNAA detection device for pollutants in organically contaminated soil, characterized in that, The PGNAA detection device for pollutants in the organically contaminated soil includes: The quantification unit is used to quantify the influencing factors of the pollutant detection process in organically contaminated soil, and obtain the quantitative functional relationship between multiple core influencing factors and core detection indicators. The correction unit is used to perform progressive correction processing on the original transient γ-neutron activation analysis signal of the collected organic polluted soil and extract the characteristic signals of the pollutant target elements. The processing unit is used to perform energy spectrum data quality checks and algorithm anomaly fault tolerance handling; The energy spectrum data quality check includes: using the characteristic peak of hydrogen as an energy stabilization anchor point; if the drift of the identified characteristic peak position compared with the theoretical characteristic gamma-ray energy exceeds the system's allowable threshold, the stabilization compensation program is automatically triggered to perform full-spectrum energy recalibration; the half-width at half maximum (WHM) of the hydrogen characteristic peak is extracted; if the WHM is wider than the calibration upper limit of the high-purity germanium detector, an automatic warning is issued and the current time window data is discarded. The algorithm's fault tolerance processing includes: when the signal-to-noise ratio of the feature peaks of the target element after smoothing and denoising is lower than the detection limit requirement, dynamically switching to the continuous wavelet transform algorithm for deep denoising; when the overlapping peak decoupling algorithm fails to converge after reaching the maximum number of iterations, automatically calling the global heuristic optimization algorithm to reinitialize the parameter boundaries to complete the deconvolution calculation of the feature peaks. The construction unit is used to construct a quantitative mapping model between the characteristic γ-energy spectrum and the content of the pollutant target element based on the quantitative functional relationship and the characteristic signal of the pollutant target element. The acquisition unit is used to acquire the key physical parameters of the organic contaminated soil to be tested, and to collect the original transient gamma neutron activation analysis energy spectrum of the organic contaminated soil to be tested. The inversion unit is used to input the key physical parameters of the organic contaminated soil to be tested and the original transient gamma neutron activation analysis energy spectrum into the quantitative mapping model to invert the pollutant content in the organic contaminated soil to be tested.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the PGNAA detection method for pollutants in organically contaminated soil as described in any one of claims 1-7.

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