A method, device and system for rapid detection of heavy metal pollution in land

Abstract

The application relates to the technical field of pollution detection, in particular to a land heavy metal pollution rapid detection method, device and system, which are used to solve the technical problem of insufficient accuracy of land heavy metal pollution detection in the prior art. The method comprises the following steps: acquiring X-ray energy spectrum data and polarized optical image data of a soil region to be detected; determining a light element scattering ratio based on the X-ray energy spectrum data, and determining a water film reflectance based on the polarized optical image data; jointly constraining and decoupling the light element scattering ratio and the water film reflectance to determine a water film existence probability; determining a spectral attenuation index spectrum based on the water film existence probability; compensating the X-ray energy spectrum data based on the spectral attenuation index spectrum; and determining a detection result of the region to be detected based on the compensated X-ray energy spectrum data.

Description

Technical Field

[0001] This application relates to the field of pollution detection technology, specifically to a rapid detection method, equipment, and system for heavy metal pollution in land. Background Technology

[0002] Currently, the detection of heavy metal pollution in soil is usually based on portable X-ray fluorescence spectroscopy (pXRF) technology. Its working principle is to use high-energy X-rays to excite the inner-shell electrons of the target heavy metal element in the soil sample, and obtain the concentration information of the target heavy metal element by detecting the intensity of the characteristic X-rays released by the sample.

[0003] However, soil samples from the field often contain moisture, which significantly interferes with the detection accuracy of pXRF technology. Moisture in soil can be distributed in two distinct forms: interstitial water filling the pores of soil particles and a continuous surface water film forming on the soil surface. These two forms exhibit significantly different attenuation mechanisms for X-ray signals. Current techniques typically treat moisture as a homogeneous mixture, performing linear compensation based solely on total moisture content, which fails to accurately identify and distinguish between different moisture distribution forms. When encountering moist or muddy soils with surface water films, existing methods cannot effectively compensate for the drastic attenuation of low-energy X-ray signals caused by the surface water film, resulting in severely underestimated or even missed detections of low-energy heavy metal elements (such as lead and arsenic), seriously affecting the accuracy of land heavy metal pollution detection. Summary of the Invention

[0004] To address the technical problem of insufficient accuracy in existing land heavy metal pollution detection technologies, the purpose of this application is to provide a rapid detection method for land heavy metal pollution. The specific technical solution adopted is as follows:

[0005] Acquire X-ray energy spectrum data and polarization optical image data of the soil area to be tested;

[0006] The light element scattering ratio is determined based on X-ray energy dispersive spectroscopy data, and the water film reflectance is determined based on polarized optical image data; wherein, the light element scattering ratio is used to characterize the total amount of light element substances in the soil region, and the water film reflectance is used to characterize the degree of coverage of a continuous water film on the surface of the soil region.

[0007] By jointly constraining and decoupling the scattering ratio of light elements and the reflectance of water film, the probability of the existence of water film is determined.

[0008] The spectral attenuation index spectrum is determined based on the probability of the presence of a water film; the spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix.

[0009] X-ray energy spectrum data is compensated based on the spectral attenuation index, and the detection result of the area to be detected is determined based on the compensated X-ray energy spectrum data.

[0010] In one possible implementation, determining the scattering ratio of light elements based on X-ray energy spectrum data includes: determining the energy ranges of inelastic scattering peaks and elastic scattering peaks from the X-ray energy spectrum data; calculating the photon count integral within the energy range of the inelastic scattering peaks to obtain the total inelastic scattering count; calculating the photon count integral within the energy range of the elastic scattering peaks to obtain the total elastic scattering count; and determining the scattering ratio of light elements based on the ratio of the total inelastic scattering count to the total elastic scattering count.

[0011] In one possible implementation, the water film reflectance is determined based on polarized optical image data, including: converting the polarized optical image data into brightness information; using an adaptive thresholding algorithm to determine a highlight determination threshold for distinguishing specular reflections; counting the number of pixels with brightness higher than the highlight determination threshold within the image region corresponding to the X-ray excitation spot; and determining the water film reflectance based on the ratio of the number of pixels to the total number of pixels in the image region.

[0012] In one possible implementation, the light element scattering ratio and water film reflectance are jointly constrained and decoupled to determine the probability of water film existence. This includes: determining the light matrix mass thickness based on the light element scattering ratio and the reference scattering ratio; wherein the reference scattering ratio is used to characterize the scattering characteristics of the mineral skeleton of dry soil; the light matrix mass thickness is used to characterize the equivalent mass thickness of light element substances along the X-ray path; determining the activation weight for water film formation based on the light matrix mass thickness and the morphological transformation threshold parameter; the activation weight is used to characterize the probability of water changing from a pore-filled state to a surface water film state; and determining the probability of water film existence based on the product of the water film reflectance and the activation weight.

[0013] In one possible implementation, the spectral attenuation index spectrum is determined based on the probability of the presence of a water film, including: calculating a first attenuation component of the corresponding bulk dilution model based on the mass thickness of the light matrix and a preset water mass attenuation coefficient; calculating a second attenuation component of the corresponding layered shielding model based on the mass thickness of the light matrix, the water mass attenuation coefficient, and a preset path multiplication coefficient; wherein the path multiplication coefficient is determined by the geometry of the X-ray incident and detected X-rays; and weighting and synthesizing the first and second attenuation components based on the probability of the presence of a water film to generate a spectral attenuation index spectrum; wherein each value of the spectral attenuation index spectrum represents the total optical thickness of the corresponding energy X-rays.

[0014] In one possible implementation, X-ray energy spectrum data is compensated based on the spectral attenuation index spectrum, including: determining the target attenuation index from the spectral attenuation index spectrum according to the characteristic X-ray energy of the target heavy metal element; determining whether the target attenuation index exceeds a preset confidence cutoff threshold; if it does not exceed the threshold, performing an exponential compensation calculation on the intensity of the corresponding energy position in the X-ray energy spectrum data based on the target attenuation index to obtain the dry basis equivalent intensity of the target heavy metal element.

[0015] In one possible implementation, the detection result of the region to be detected is determined based on the compensated X-ray energy spectrum data, including: calculating the dry basis mass fraction of the target heavy metal element based on the dry basis equivalent intensity using a preset quantitative model, as the detection result.

[0016] In one possible implementation, the method further includes generating and outputting an alarm message indicating that the signal is blocked if the target attenuation index exceeds a preset confidence cutoff threshold.

[0017] This application also provides a rapid detection device for heavy metal pollution in land, comprising:

[0018] The data acquisition unit is used to acquire X-ray energy spectrum data and polarization optical image data of the soil area to be tested;

[0019] The feature extraction unit is used to determine the light element scattering ratio based on X-ray energy spectrum data and the water film reflectance based on polarization optical image data; wherein, the light element scattering ratio is used to characterize the total amount of light element substances in the soil region, and the water film reflectance is used to characterize the degree of coverage of the continuous water film on the surface of the soil region.

[0020] The decoupling analysis unit is used to jointly constrain and decouple the light element scattering ratio and water film reflectance, determine the probability of water film presence, and determine the spectral attenuation index spectrum based on the probability of water film presence; the spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix;

[0021] The signal processing unit is used to compensate the X-ray energy spectrum data based on the spectral attenuation index spectrum, and to determine the detection result of the area to be detected based on the compensated X-ray energy spectrum data.

[0022] This application also provides a rapid detection system for heavy metal pollution in soil, comprising: an X-ray excitation module, a polarization imaging module, and a rapid detection device for heavy metal pollution in soil; the X-ray excitation module is used to emit high-energy X-rays to irradiate soil samples, and a detector captures the characteristic X-rays and scattered X-rays released by the elements in the sample after excitation, thereby obtaining X-ray energy spectrum data; the polarization imaging module is used to capture surface images of the soil samples, which are then filtered by a linear polarizer to obtain polarization optical image data; the rapid detection device for heavy metal pollution in soil is used to perform the above-mentioned rapid detection method for heavy metal pollution in soil.

[0023] This application offers the following advantages: By simultaneously acquiring X-ray energy dispersive spectroscopy and polarization optical images, it integrates material composition information with surface morphology information during detection. It quantifies the total amount of moisture and organic matter in the soil using the scattering ratio of light elements and identifies continuous water films on the surface using the water film reflectance, overcoming the limitations of a single data source. Furthermore, by constructing a joint constraint decoupling model to calculate the probability of water film presence, it distinguishes between two forms of water interference, avoiding misidentification of dry, smooth minerals as water films and improving the model's anti-interference capability. Based on this, it dynamically synthesizes a spectral attenuation index spectrum according to the probability of water film presence, enabling the attenuation correction model to adapt to the actual moisture distribution. Finally, based on this spectrum, it performs precise physical compensation on the original signal, effectively recovering the low-energy characteristic signals lost due to water film shielding, thereby significantly improving the accuracy and reliability of soil heavy metal pollution detection under complex conditions such as high water content and mud. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0025] Figure 1 A flowchart illustrating a rapid detection method for heavy metal pollution in land, provided as an embodiment of this application;

[0026] Figure 2 A schematic diagram of a rapid detection device for heavy metal pollution in land provided in one embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the system architecture of a rapid detection system for heavy metal pollution in land, provided as an embodiment of this application. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a rapid detection method for heavy metal pollution in land according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] Unless otherwise specified, the normalization function Norm() mentioned in this application uses maximum and minimum value normalization. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculation result exceeds the [0,1] interval, a truncation function is used to limit it to the [0,1] range (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the evaluation index.

[0031] The specific scheme of a rapid detection method for heavy metal pollution in land provided in this application is described below with reference to the accompanying drawings.

[0032] Please see Figure 1 It illustrates a flowchart of a rapid detection method for heavy metal pollution in land according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0033] Step 101: Obtain X-ray energy spectrum data and polarization optical image data of the soil area to be tested.

[0034] Among them, the X-ray energy spectrum signal is generated by irradiating the soil sample with high-energy X-rays emitted by the X-ray excitation module. The detector captures the characteristic X-rays and scattered X-rays released by the elements in the sample after being excited. The original energy spectrum data is stored according to the energy channel and contains the composition information of heavy metal elements and matrix elements in the soil.

[0035] Polarized optical image signals are images of the soil sample surface captured by a polarization imaging module. After being filtered by a linear polarizer, they can effectively retain specular reflection light and suppress diffuse reflection light, thereby reflecting morphological features such as the presence of a continuous water film on the soil surface.

[0036] Optionally, this step employs a coaxial heterogeneous spatial layout design, ensuring that the field of view center of the polarization imaging module coincides with the spot center of the X-ray excitation module in physical space, and that the imaging field of view covers and exceeds the range of the X-ray excitation spot, thus ensuring consistency in the detection areas corresponding to the two signals. Upon receiving the detection command, the system initiates a standard detection cycle, synchronously triggering the acquisition of the two signals according to a preset timing sequence to avoid data mismatch caused by changes in sample state or equipment displacement.

[0037] Specifically, this step can be implemented as follows: record the start time of the detection cycle. ,exist The polarization imaging module is triggered at specific times to capture a single frame of polarization optical image and generate a polarization image matrix. Within a preset delay (exemplarily less than 50 milliseconds) after image capture is completed, the X-ray tube is triggered to emit high-energy rays, and the detector is controlled to continuously acquire data for the specified duration. (Example value is 30 seconds) of X-ray photon signal to generate the original energy spectrum array. ,in The energy channel index is an integer, ranging from 1 to... (Example) =2048), each Corresponding to a specific photon energy value .

[0038] Polarization image matrix acquired within the same detection period With the original energy spectrum array Data marked as originating from the same source is stored in the system cache. The preset delay setting is to balance data synchronization with device responsiveness, preventing changes in soil sample conditions (such as moisture distribution) due to excessive delay, while ensuring that the polarization imaging module completes image storage before initiating X-ray acquisition to avoid electromagnetic interference. Continuous acquisition duration. The signal-to-noise ratio of the original energy spectrum signal is determined based on the requirements of detector sensitivity and detection accuracy, ensuring that it meets the requirements of subsequent feature extraction.

[0039] Step 102: Determine the scattering ratio of light elements based on X-ray energy spectrum data, and determine the water film reflectance based on polarization optical image data.

[0040] Among them, the scattering ratio of light elements (denoted as ) This parameter is used to characterize the total amount of light elements (such as water, hydrogen, carbon, and oxygen in organic matter) in a soil region and is dimensionless. Since the inelastic scattering (Compton scattering) effect of light elements on incident X-rays is significantly stronger than that of elastic scattering (Rayleigh scattering), the ratio of the intensities of the two scattering peaks can eliminate the interference of geometric factors such as measurement distance and surface flatness, and accurately quantify the total amount of light elements.

[0041] Water film reflectance (denoted as) The water film is used to characterize the degree of coverage of a continuous water film on the soil surface. It is a dimensionless parameter with a value range of [0,1]. When a continuous water film exists on the soil surface, the gas-liquid interface will produce specular reflection that conforms to Fresnel's law. After polarization filtering, it appears as a high-brightness area in the image. The presence and coverage of the water film can be reflected by statistically analyzing the proportion of this area.

[0042] In some embodiments, after signal acquisition, preprocessing is required, including noise suppression (such as smoothing filtering) of X-ray energy spectrum signals and distortion correction of polarization optical image signals to ensure the reliability of the original signals.

[0043] Step 103: Perform joint constraint decoupling on the light element scattering ratio and water film reflectance to determine the probability of water film existence.

[0044] It should be noted that the joint probability model is a nonlinear model constructed based on the physical characteristics of soil moisture phase change. The joint probability model only determines that moisture exists in the form of a layered water film when the total amount of light elements is sufficient (indicating the presence of enough moisture) and the surface specular reflection is significant (indicating the presence of a continuous water film). This avoids misjudgment caused by the reflection from dry, smooth mineral surfaces. The probability of water film existence (denoted as...) ) is a dimensionless weight with a value range of [0,1]. The larger the value, the higher the probability that water exists in the form of a layered water film.

[0045] Optionally, the joint probability model uses a sigmoid function to characterize the transition process of water from the pore-filling state to the surface film-forming state. Combined with preset calibration parameters such as pore saturation threshold and steepness coefficient, it can realize the quantitative calculation of the probability of water film existence.

[0046] Step 104: Determine the spectral attenuation index spectrum based on the probability of water film presence. The spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix.

[0047] Among them, the spectral attenuation index spectrum (denoted as ) , The energy channel index is a dimensionless curve characterizing the total optical thickness of X-rays of different energies transmitted through the soil matrix. A larger value indicates more severe attenuation loss of the X-rays. The drag component of the bulk dilution model is based on the assumption that water is uniformly dispersed in the soil pores, and the loss parameter is calculated considering only the linear attenuation effect of light elements on X-rays. The drag component of the layered shielding model is based on the assumption that water forms a continuous water film on the surface, and the loss parameter is calculated considering the nonlinear cutoff shielding effect of the water film on low-energy X-rays and the geometric multiplication effect of the optical path.

[0048] As an implementation method, the core logic of dynamic weighting is based on the probability of the water film's existence. Linear fusion of the drag components of the two models is performed when When the value approaches 0, the drag component of the bulk dilution model is mainly used; when When the value approaches 1, the drag component of the layered shielding model is mainly used; when When the value is in the middle, it is synthesized according to the weight ratio to ensure that the decay model matches the actual water distribution pattern.

[0049] Step 105: Compensate the X-ray energy spectrum data based on the spectral attenuation index spectrum, and determine the detection result of the area to be detected based on the compensated X-ray energy spectrum data.

[0050] Among them, the characteristic signal of the target heavy metal element refers to the intensity of the characteristic X-ray peak corresponding to a specific heavy metal element (such as lead Pb, arsenic As, cadmium Cd, etc.) in the energy spectrum, and its intensity is positively correlated with the element concentration. The dry basis concentration refers to the mass fraction of heavy metal elements in the soil sample under dry conditions after deducting moisture interference, which can truly reflect the degree of soil pollution.

[0051] In some implementations, this application first extracts the corresponding attenuation index from the spectral attenuation index spectrum based on the characteristic energy of the target heavy metal element. Then, based on the inverse operation of the Beer-Lambert law, it compensates for the intensity of the characteristic peaks in the original energy spectrum, restoring the equivalent dry-basis intensity free from moisture interference. Next, using a preset quantitative model (such as the basic parameter method or empirical coefficient method), the equivalent dry-basis intensity is converted into a dry-basis concentration. If the attenuation index exceeds a preset confidence cutoff threshold, indicating that the characteristic signal has been submerged in background noise and cannot be effectively restored, an abnormal alarm signal is output to prompt the user to adjust the detection conditions.

[0052] Based on the above technical solution, this application integrates material composition information and surface morphology information during detection by simultaneously acquiring X-ray energy dispersive spectroscopy and polarization optical images. It quantifies the total amount of moisture and organic matter in the soil using the light element scattering ratio and identifies continuous water films on the surface using the water film reflectance ratio, overcoming the limitations of a single data source. Furthermore, by constructing a joint constraint decoupling model to calculate the probability of water film presence, it distinguishes between two forms of water interference, avoiding misidentification of dry, smooth minerals as water films and improving the model's anti-interference capability. On this basis, it dynamically synthesizes a spectral attenuation index spectrum based on the water film presence probability, enabling the attenuation correction model to adapt to the actual moisture distribution. Finally, based on this spectrum, precise physical compensation is performed on the original signal, effectively recovering the low-energy characteristic signals lost due to water film shielding, thereby significantly improving the accuracy and reliability of soil heavy metal pollution detection under complex conditions such as high water content and mud.

[0053] In one possible implementation, the specific process of determining the scattering ratio of light elements based on X-ray energy spectrum data includes:

[0054] Step 201: Determine the energy ranges of the inelastic scattering peaks and the elastic scattering peaks from the X-ray energy spectrum data.

[0055] Specifically, in this step, based on the target material type of the X-ray tube (e.g., a silver target Ag), the original energy spectrum array is... Mid-positioning (Corresponding to energies of approximately 18.5-21.0 keV) and the energy range of the elastic scattering peak. (Corresponding energy is approximately 21.5-22.5 keV).

[0056] Optionally, the inelastic scattering in this application is such as Compton scattering, and the elastic scattering is such as Rayleigh scattering.

[0057] Step 202: Calculate the photon count integral within the energy range of the inelastic scattering peak to obtain the total inelastic scattering count.

[0058] As an example, the total count of inelastic scattering Satisfy the following formula:

[0059] ;

[0060] in, This represents the minimum value in the energy range of the inelastic scattering peak. This represents the maximum value in the energy range of the inelastic scattering peak.

[0061] Step 203: Calculate the photon count integral within the energy range of the elastic scattering peak to obtain the total elastic scattering count.

[0062] As an example, total elastic scattering count Satisfy the following formula:

[0063] ;

[0064] in, This represents the minimum value in the energy range of the elastic scattering peak. This represents the maximum value in the energy range of the elastic scattering peak.

[0065] Step 204: Determine the scattering ratio of light elements based on the ratio of the total inelastic scattering count to the total elastic scattering count.

[0066] As an example, the scattering ratio of light elements Satisfy the following formula:

[0067] ;

[0068] In this formula, These are parameter tuning coefficients, and their values ​​are extremely small positive numbers (e.g., ...). ), to avoid the denominator being 0.

[0069] Based on the above technical solution, this application makes the extraction process of light element scattering ratio more operable and accurate by clarifying the energy range positioning rules of scattering peaks, the counting integration method, and the ratio calculation logic. This extraction method fully utilizes the differences in scattering characteristics between light and heavy elements, effectively eliminating interference from geometric factors and framework elements, thus improving the accuracy of the extracted light element scattering ratio. The parameters more accurately reflect the total amount of light elements, providing high-quality feature input for the accurate identification of subsequent moisture distribution patterns, and further improving the anti-interference ability and detection accuracy of the detection method.

[0070] In one possible implementation, the specific process of determining the water film reflectance based on polarized optical image data includes:

[0071] Step 301: Convert polarization optical image data into brightness information.

[0072] The aforementioned polarized optical image data is typically multi-channel color image data. In order to extract the reflection features related to the surface water film, it needs to be converted into single-channel brightness information.

[0073] Optionally, this application employs existing luminance conversion algorithms, such as converting an RGB color image into a luminance grayscale image using the formula Y=0.299R+0.587G+0.114B (where Y is the luminance value, and R, G, and B are the pixel values ​​of the red, green, and blue channels, respectively), to obtain a luminance information matrix characterizing the luminance distribution of different areas on the soil surface. This luminance information can directly reflect the differences in reflectance intensity on the soil surface, providing a basis for subsequent identification of highlight areas.

[0074] Step 302: Use an adaptive threshold algorithm to determine the specular threshold used to distinguish specular reflections.

[0075] It should be noted that the lighting conditions in field detection environments are complex and varied, with significant differences in light intensity under conditions such as cloudy days, direct sunlight, and shadows. Using a fixed threshold cannot accurately distinguish specular reflection areas under different lighting conditions. Therefore, this application employs an adaptive threshold algorithm to dynamically determine the specular highlight determination threshold based on the brightness distribution characteristics of the currently acquired polarized optical image. For example, the Otsu algorithm (maximum inter-class variance method) can be used. This algorithm calculates the inter-class variance between the foreground (highlight area) and the background (non-highlight area) at different gray-level thresholds by statistically analyzing the gray-level histogram of the brightness gray-level image. The gray-level value with the maximum inter-class variance is used as the highlight determination threshold. This threshold can optimally segment the highlight areas and background areas in the current image, ensuring accurate identification of specular reflection features even under complex lighting conditions.

[0076] Step 303: Within the image area corresponding to the X-ray excitation spot, count the number of pixels whose brightness is higher than the highlight determination threshold.

[0077] To ensure spatial consistency between optical features and X-ray energy spectrum features, the statistical image region needs to be defined as the projection region of the X-ray excitation spot in the polarization optical image. This region can be determined by a pre-calibrated spatial mask, denoted as the region. After determining the region, traverse the region. Compare the brightness value of each pixel within the range with the highlight detection threshold. Statistical brightness value greater than Number of pixels The number of pixels directly corresponds to the pixel distribution of specular reflection within the area, and is related to the coverage of the surface water film.

[0078] Step 304: Determine the water film reflectance based on the ratio of the number of pixels to the total number of pixels in the image area.

[0079] As an example, water film reflectance Satisfy the following formula:

[0080] ;

[0081] Among them, water film reflectance For the range of values A dimensionless scalar. When When the water level approaches 0, it indicates that there is no standing water on the soil surface or only pore water; when... A significant increase indicates the presence of a continuous water film on the soil surface. This indicator serves as a key input for determining the probability of water film presence in subsequent stages.

[0082] Based on the above technical solution, this application accurately extracts the water film reflectance, which characterizes the degree of continuous water film coverage on the soil surface, through steps such as brightness conversion, adaptive threshold segmentation, spatially constrained statistics, and ratio calculation. This method can adapt to complex and variable lighting conditions in the field, effectively eliminating interference from light intensity fluctuations. Simultaneously, through spatial consistency constraints, it ensures the matching between optical characteristics and X-ray energy spectrum characteristics, providing reliable optical dimension feature parameters for subsequent joint constraint decoupling. The water film reflectance obtained by this method can accurately distinguish the optical characteristic differences between surface water films and pore water, providing crucial support for the accurate determination of the probability of water film presence.

[0083] In one possible implementation, the specific implementation process of the above steps—jointly constraining and decoupling the scattering ratio of light elements and the reflectance of water film to determine the probability of the existence of water film—includes the following:

[0084] Step 401: Determine the mass thickness of the light matrix based on the light element scattering ratio and the reference scattering ratio.

[0085] Among them, the reference scattering ratio is used to characterize the scattering characteristics of the mineral skeleton of dry soil; the light matrix mass thickness is used to characterize the equivalent mass thickness of light element materials along the X-ray path.

[0086] Optional, reference scattering ratio This is a preset parameter characterizing the scattering properties of the mineral skeleton in dry soil. It represents the light element scattering ratio of the soil mineral skeleton under dry conditions. It can be obtained by testing various dry standard soil samples (such as dry silica powder, dry neutral loam, etc.) in a laboratory environment, statistically analyzing their average light element scattering ratios, and pre-storing the values ​​in the detection system. Light matrix mass thickness This method is used to characterize the equivalent mass thickness of light element materials along the X-ray path. Light element materials in soil mainly include water and organic matter, and their total amount is positively correlated with the light element scattering ratio. In this step, the increment of the light element scattering ratio can be converted into light matrix mass thickness using a linear mapping model, i.e., utilizing the light element scattering ratio... Compared with the reference scattering ratio The difference, combined with the pre-calibrated scattering-mass conversion coefficient Calculations yielded For example, if Greater than This indicates that the total amount of light elements in the detected area is higher than that in the mineral skeleton of dry soil. The larger the difference, the greater the total amount of light elements. The larger; if Less than or equal to This indicates that the total amount of light elements in the detection area did not exceed the baseline level of the mineral skeleton of the dry soil. Setting it to 0 avoids the unreasonable situation of negative mass thickness. For example, the scattering-mass conversion coefficient... The value is 0.02 g / cm², obtained through linear regression of standard sand samples with a moisture content of 0-20%.

[0087] As an example, lightweight matrix mass thickness Satisfy the following formula:

[0088] ;

[0089] in, Scattering-mass conversion factor (unit: The method was obtained by preparing standard samples with known water content, measuring their scattering ratio increment and corresponding water mass thickness, and performing linear regression analysis. The baseline scattering ratio is obtained by measuring the X-ray scattering of dry standard powder samples in a laboratory environment and calculating the average value of their Compton / Rayleigh scattering ratios. This represents the scattering ratio of light elements. If the calculated value is... Then a mandatory order .

[0090] Step 402: Determine the activation weight of the water separation membrane based on the mass thickness of the light matrix and the morphological transformation threshold parameters.

[0091] The activation weight is used to characterize the probability of water changing from a pore-filling state to a surface water film state.

[0092] Morphological transformation threshold parameters include pore saturation threshold. and steepness coefficient ,in It is the critical light matrix thickness at which water transitions from a pore-filled state to a surface water film state. When the light matrix thickness reaches or exceeds [a certain value], [the value is determined by the specific parameters of the light matrix]. At this time, moisture begins to seep out from the pores, forming a surface water film; Used to characterize the rate at which moisture transitions from a pore-filled state to a surface water film state. The larger the value, the steeper the transition process. These parameters can be calibrated through gradient moisture content experiments. Standard soil samples are selected, and water is gradually added while maintaining the soil structure. The process of soil surface condition changing from no water accumulation to the appearance of a continuous water film is monitored, and the corresponding changes in lightweight matrix mass thickness are recorded. These parameters are then obtained through nonlinear fitting. and The specific value.

[0093] The activation weight is a parameter characterizing the probability of water transitioning from a pore-filling state to a surface water film state. It can be calculated using the Sigmoid function. much smaller When the activation weight approaches 0, it indicates that water mainly exists in a pore-filling state; when Much larger When the activation weight approaches 1, it indicates that water mainly exists in the form of a surface water film; when near At that time, the activation weight follows The rapid increase in activation weight reflects the threshold effect of water filling pores and forming a film on the surface. For example, the formula for calculating the activation weight is: .

[0094] For example, pore saturation threshold The value is taken as 0.05 g / cm² (based on neutral loam calibration), with a range of 0.03-0.08 g / cm² (adjusted according to soil type). Steepness coefficient. The value is 5.0 cm² / g, and the range is 3.0-8.0 cm² / g (the higher the soil porosity, the larger the α value).

[0095] Step 403: Determine the probability of water film existence based on the product of water film reflectance and activation weight.

[0096] As an example, the probability of water film existence Satisfying the formula:

[0097] ;

[0098] in: The water film reflectance (dimensionless, [0,1]) characterizes the intensity of specular reflection on the soil surface and reflects the optical characteristics of the surface water film. Indicates activation weight, The steepness coefficient (unit: cm² / g) characterizes the rate at which water transitions from pore filling to surface film formation. The mass thickness of the light matrix (unit: g / cm²) characterizes the equivalent mass thickness of light element material along the X-ray path; The pore saturation threshold (unit: g / cm²) is the critical light matrix mass thickness at which water begins to form a surface water film. It is a natural exponential function used to construct the Sigmoid function, achieving a smooth transition of activation weights.

[0099] Based on the above technical solution, this application determines the mass thickness of the light matrix by comparing the scattering ratio of light elements with the reference scattering ratio, obtains the activation weight by combining it with the morphological transformation threshold parameter, and then calculates the probability of water film presence in conjunction with the water film reflectance. This method achieves joint constraint decoupling between the total amount of light element matter (mass dimension) and the surface optical morphology (light dimension), effectively eliminating interference factors caused by single feature analysis, and can accurately determine the distribution pattern of water, providing a core basis for subsequent dynamic adaptation attenuation models. The probability of water film presence obtained by this method can truly reflect the transition state of water from pore filling to surface film formation, laying the foundation for the construction of the spectral attenuation index spectrum, and further improving the adaptability of the detection method to complex moisture conditions.

[0100] In one possible implementation, the specific process of determining the spectral attenuation index spectrum based on the probability of the water film presence in the above steps includes:

[0101] Step 501: Calculate the first attenuation component of the corresponding bulk dilution model based on the thickness of the light matrix and the preset water mass attenuation coefficient.

[0102] The bulk dilution model is suitable for scenarios where water exists in a porous, pore-filled state. In this case, water is uniformly distributed in the soil pores, acting as a light element matrix to reduce the density of the heavy metal element being measured per unit volume, resulting in an approximately linear attenuation of the X-ray signal. (Water mass attenuation coefficient) It is a pre-defined physical constant that characterizes the energy of water. ( The mass attenuation capability of X-rays (indexed by energy channel) can be referenced from the XCOM photon cross-section database published by NIST; different energies of X-rays correspond to different mass attenuation capabilities. Value, the lower the energy of X-rays, The larger the value, the more significant the decay.

[0103] First attenuation component The calculation logic for (the attenuation intensity corresponding to the bulk dilution effect) is the mass thickness of the light matrix. With water mass attenuation coefficient The product of, i.e. This component quantifies the bulk dilution and attenuation effect of water in pore water on X-rays of different energies. The bigger, The larger the value, the larger the first attenuation component, and the more obvious the attenuation of the X-ray signal.

[0104] Step 502: Calculate the second attenuation component of the corresponding layered shielding model based on the thickness of the lightweight matrix, the water mass attenuation coefficient, and the preset path multiplication coefficient.

[0105] The path multiplication factor is determined by the geometry of the X-ray incident and detection.

[0106] The layered shielding model is suitable for scenarios where water exists in the form of a surface water film. In this case, the water film covers the soil surface and produces a strong nonlinear cutoff shield for low-energy X-rays.

[0107] Path multiplication factor Determined by the geometry of X-ray incident and detection, its calculation formula is as follows: ,in The angle of incidence of the X-rays. To detect the exit angle, this coefficient is used to correct for the geometric elongation effect of the layered water film on the X-ray path, i.e., the actual path length of an obliquely incident X-ray passing through the water film is greater than the physical thickness of the water film. As a fixed geometric factor, it can be calculated and preset by calibrating the geometric parameters of the X-ray detection module.

[0108] Second attenuation component The calculation logic for (the attenuation intensity corresponding to the layered shielding effect) is based on the thickness of the lightweight matrix mass. Water quality attenuation coefficient With path multiplication factor The product of, i.e. This component quantifies the degree of layered shielding attenuation of X-rays of different energies by the surface water film. Compared to the first attenuation component, because... The presence of this component makes the attenuation of low-energy X-rays by the second attenuation component more significant, consistent with the shielding characteristics of a layered water film.

[0109] Step 503: Based on the probability of the water film's existence, the first attenuation component and the second attenuation component are weighted and synthesized to generate a spectral attenuation index spectrum.

[0110] In this context, each value of the spectral attenuation index spectrum represents the total optical thickness of the corresponding energy X-ray.

[0111] As an example, the spectral attenuation index spectrum satisfies the following formula:

[0112] ;

[0113] in: is the probability of the water film's existence (dimensionless, [0,1]), which is used as a weighting coefficient; The second attenuation component (dimensionless) characterizes the attenuation intensity of the layered shielding effect; The first attenuation component (dimensionless) characterizes the attenuation intensity of the bulk dilution effect; This is an index for energy channels, corresponding to different X-ray energies. .

[0114] Based on the above technical solution, this application constructs a bulk dilution model and a layered shielding model to calculate the attenuation components under two different water distribution patterns. Then, based on the probability of water film presence, a weighted synthesis is performed to obtain a spectral attenuation index spectrum that accurately reflects the current attenuation characteristics of the soil matrix. This method overcomes the limitations of existing single attenuation models, achieving dynamic adaptation between the attenuation model and the water distribution pattern. It can accurately quantify the transmission attenuation of X-rays of different energies in complex soil matrices, providing a scientific and reliable attenuation basis for subsequent energy spectrum data compensation, and further improving the accuracy of detection results under complex conditions such as high water content and mud.

[0115] In one possible implementation, the specific process of compensating X-ray energy spectrum data based on the spectral attenuation index spectrum includes:

[0116] Step 601: Determine the target attenuation index from the spectral attenuation index spectrum based on the characteristic X-ray energy of the target heavy metal element.

[0117] The target heavy metal elements include common soil heavy metal pollutants such as lead (Pb), arsenic (As), and cadmium (Cd). Each heavy metal element has its characteristic X-ray energy (e.g., the characteristic X-ray energy of lead's L-series is approximately 10-15 keV, and the characteristic X-ray energy of arsenic's K-series is approximately 11.7 keV). These characteristic energy values ​​are pre-stored in the element characteristic energy library of the detection system. During the detection process, the target heavy metal element can be selected according to the detection requirements. The characteristic X-ray energy of that element is retrieved from the element characteristic energy library, and then the corresponding energy channel index is found in the spectral attenuation index spectrum based on that energy. Extract the spectral attenuation index value under this index. This refers to the target attenuation index. The target attenuation index quantifies the degree of transmission attenuation of the characteristic X-rays of the target heavy metal element in the current soil matrix and is a core parameter for subsequent compensation calculations.

[0118] Step 602: Determine whether the target decay index exceeds the preset confidence cutoff threshold.

[0119] Credibility truncation threshold This is the critical physical optical path length value used to determine whether the characteristic signal of a target heavy metal element is submerged in background noise. It is an empirical boundary in engineering that distinguishes effective signals from background noise, typically set to 2.3 (corresponding to approximately 10% signal transmittance). It can be fine-tuned based on the detector performance of the detection system (such as signal-to-noise ratio and detection efficiency) and is pre-stored in the system. In this step, the target attenuation index... With confidence cutoff threshold Comparison: If > This indicates that the attenuation of the target's characteristic X-rays is too severe, and the signal received by the detector is mainly background noise, belonging to the physically unmeasurable range; if ≤ This indicates that although the target's characteristic X-rays are attenuated, there is still an identifiable and effective signal, which is within the physically recoverable range and has the conditions for compensation and correction.

[0120] Step 603: If not exceeded, perform exponential compensation calculation on the intensity of the corresponding energy position in the X-ray energy spectrum data according to the target attenuation index to obtain the dry basis equivalent intensity of the target heavy metal element.

[0121] exist ≤ In this case, the transmission attenuation of X-rays in the soil matrix follows the Beer-Lambert law, that is, the intensity of characteristic X-rays decreases exponentially with transmission distance (or optical thickness). Therefore, the compensation calculation uses an exponential function for inverse operation.

[0122] As an example, dry basis equivalent strength Satisfy the following formula:

[0123] ;

[0124] in, Target energy position in X-ray energy spectrum data The corresponding raw intensity (unit: count rate) is the raw count intensity of the target feature X-rays directly acquired by the detector; The target attenuation index (dimensionless) characterizes the total optical thickness of the target's characteristic X-rays; This is the natural exponential function, used to implement the inverse operation based on the Beer-Lambert law; The dry-basis equivalent intensity (unit: count rate) is the theoretical intensity of the characteristic X-rays of the target heavy metal element after removing moisture attenuation interference. The above formula is an inverse compensation operation based on the physical attenuation law, aiming to eliminate the attenuation effect and restore the theoretical dry-basis signal intensity. The larger the value, the more severe the moisture-induced degradation, and therefore the higher the required compensation factor. The larger the intensity, the greater the attenuation of the observed intensity. Compensation back to the theoretical dry basis equivalent strength .

[0125] Step 604: If the error exceeds the limit, generate and output an alarm message indicating that the signal is blocked.

[0126] exist > In such cases, the characteristic X-ray signal of the target heavy metal element is submerged in background noise. Forcing quantitative calculations under these conditions would result in significant errors and could even mislead decision-making. Therefore, the detection system automatically terminates the quantitative calculation process for the target heavy metal element and generates an alarm message indicating signal obstruction.

[0127] Optionally, the alarm message should be clear and unambiguous, easy for on-site operators to understand. For example, the alarm message may include "The surface water film is too thick, the target element characteristic signal is blocked, and the detection result is unreliable," and may also include suggested actions, such as "Please clean the surface water and retest" or "Please change the detection point." The alarm message can be output in various ways, such as displaying a text prompt on the detection equipment's screen, issuing a voice alarm through the equipment's speaker, or sending an alarm notification to a linked mobile terminal, ensuring that operators can receive the alarm information in a timely manner.

[0128] In addition, the system can record relevant parameters when an alarm occurs, such as detection time, detection location, target attenuation index value, and probability of water film presence, which facilitates subsequent traceability and analysis and provides a reference for optimizing detection schemes and adjusting detection strategies.

[0129] Based on the above technical solution, this application achieves accurate compensation of X-ray energy spectrum data through the extraction of the target attenuation index, confidence determination, and index compensation calculation. This method first clarifies the attenuation degree corresponding to the characteristic energy of the target heavy metal element, then filters out recoverable effective signals through a confidence truncation threshold, avoiding ineffective compensation for noise signals. Finally, based on physical laws, index compensation is performed to accurately restore the dry-basis equivalent intensity. This process ensures both the scientific nature of the compensation and the engineering reliability of the results, effectively eliminating the attenuation interference of different moisture distribution forms on the target characteristic signal, and providing a key guarantee for the accuracy of the detection results.

[0130] In one possible implementation, the process of determining the detection result of the area to be detected based on the compensated X-ray energy spectrum data can be as follows: based on the dry basis equivalent intensity, the dry basis mass fraction of the target heavy metal element is calculated through a preset quantitative model as the detection result.

[0131] Specifically, the core of this application's determination of detection results based on compensated X-ray energy spectrum data is to convert the dry-basis equivalent intensity... Convert to the dry basis mass fraction of the target heavy metal element. The preset quantitative model is an element intensity-content mapping model pre-built and stored in the detection system.

[0132] For example, this model can employ the industry-standard basic parameter method, specifically including: pre-acquiring relevant physical parameters of the detection system (such as the target material, tube voltage, tube current, and detector efficiency of the X-ray tube), and compositional parameters of the soil matrix (such as the types and content ranges of major elements). Based on the physical processes of X-ray fluorescence generation and detection, a theoretical calculation model for the dry-basis equivalent intensity and dry-basis mass fraction is established. This model calculates the generation efficiency, transmission efficiency, and detection efficiency of characteristic X-rays, combined with the dry-basis equivalent intensity. The dry basis mass fraction of the target heavy metal element is obtained through inversion. The final output dry basis mass fraction is the detection result of the area to be tested. This result removes the interference of moisture and can truly reflect the actual pollution level of the target heavy metal element in the soil. The unit is usually mg / kg.

[0133] Please see Figure 2 The diagram illustrates a structural schematic of a rapid detection device for heavy metal pollution in soil according to an embodiment of the present invention. This device includes a data acquisition unit 201, a feature extraction unit 202, a decoupling analysis unit 203, and a signal processing unit 204. The units communicate bidirectionally via a communication link, ensuring real-time interaction between the acquired data and analysis results. The communication link can employ wired or wireless transmission methods to meet the communication needs of different monitoring scenarios.

[0134] Data acquisition unit 201 is used to acquire X-ray energy spectrum data and polarization optical image data of the soil area to be detected;

[0135] The feature extraction unit 202 is used to determine the light element scattering ratio based on X-ray energy spectrum data and the water film reflectance based on polarization optical image data; wherein, the light element scattering ratio is used to characterize the total amount of light element substances in the soil region, and the water film reflectance is used to characterize the degree of coverage of the continuous water film on the surface of the soil region.

[0136] The decoupling analysis unit 203 is used to perform joint constraint decoupling of light element scattering ratio and water film reflectance, determine the probability of water film existence, and determine the spectral attenuation index spectrum based on the probability of water film existence; the spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix;

[0137] The signal processing unit 204 is used to compensate the X-ray energy spectrum data based on the spectral attenuation index spectrum, and to determine the detection result of the area to be detected based on the compensated X-ray energy spectrum data.

[0138] Please see Figure 3 This diagram illustrates a system architecture of a rapid detection system for heavy metal pollution in soil according to an embodiment of the present invention. The system includes an X-ray excitation module 301, a polarization imaging module 302, and a rapid detection device 303. The X-ray excitation module 301 emits high-energy X-rays to irradiate a soil sample, and a detector captures the characteristic X-rays and scattered X-rays released by the excited elements in the sample to obtain X-ray energy spectrum data. The polarization imaging module 302 captures images of the soil sample surface, which are then filtered by a linear polarizer to obtain polarized optical image data. The rapid detection device 302 executes the rapid detection method for heavy metal pollution in soil described in the aforementioned embodiment.

[0139] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A rapid detection method for heavy metal pollution in land, characterized in that, The method includes: Acquire X-ray energy spectrum data and polarization optical image data of the soil area to be tested; The light element scattering ratio is determined based on the X-ray energy spectrum data, and the water film reflectance is determined based on the polarization optical image data; wherein, the light element scattering ratio is used to characterize the total amount of light element substances in the soil region, and the water film reflectance is used to characterize the degree of coverage of a continuous water film on the surface of the soil region. By jointly constraining and decoupling the light element scattering ratio and the water film reflectance, the probability of the water film's existence is determined. The spectral attenuation index spectrum is determined based on the probability of the presence of the water film; the spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix. The X-ray energy spectrum data is compensated based on the spectral attenuation index spectrum, and the detection result of the region to be detected is determined based on the compensated X-ray energy spectrum data. The determination of the probability of water film presence involves jointly constraining and decoupling the light element scattering ratio and the water film reflectance, including: The mass thickness of the light matrix is ​​determined based on the light element scattering ratio and the reference scattering ratio; wherein, the reference scattering ratio is used to characterize the scattering characteristics of the mineral skeleton of dry soil; and the mass thickness of the light matrix is ​​used to characterize the equivalent mass thickness of light element material along the X-ray path. The activation weight of water film formation is determined based on the mass thickness of the light matrix and the morphological transformation threshold parameter; the activation weight is used to characterize the possibility of water changing from a pore-filled state to a surface water film state. The probability of the water film's presence is determined by multiplying the water film reflectance by the activation weight. The determination of the spectral attenuation index spectrum based on the probability of the presence of the water film includes: Based on the light matrix mass thickness and the preset water mass attenuation coefficient, calculate the first attenuation component of the corresponding bulk dilution model; The second attenuation component of the corresponding layered shielding model is calculated based on the mass thickness of the lightweight matrix, the mass attenuation coefficient of the water, and the preset path multiplication coefficient; wherein, the path multiplication coefficient is determined by the geometry of the X-ray incident and detection. Based on the probability of the water film's presence, the first attenuation component and the second attenuation component are weighted and synthesized to generate the spectral attenuation index spectrum; wherein, each value of the spectral attenuation index spectrum represents the total optical thickness of the corresponding energy X-ray.

2. The rapid detection method for heavy metal pollution in land according to claim 1, characterized in that, Determining the scattering ratio of light elements based on the X-ray energy spectrum data includes: The energy ranges of the inelastic scattering peaks and the energy ranges of the elastic scattering peaks are determined from the X-ray energy spectrum data. Calculate the photon count integral within the energy range of the inelastic scattering peak to obtain the total inelastic scattering count; Calculate the photon count integral within the energy range of the elastic scattering peak to obtain the total elastic scattering count; The light element scattering ratio is determined based on the ratio of the total inelastic scattering count to the total elastic scattering count.

3. The rapid detection method for heavy metal pollution in land according to claim 1, characterized in that, Determining the water film reflectance based on the polarized optical image data includes: The polarization optical image data is converted into brightness information; An adaptive thresholding algorithm is used to determine the specular threshold for distinguishing specular reflections; Within the image region corresponding to the X-ray excitation spot, count the number of pixels whose brightness is higher than the highlight determination threshold; The water film reflectance is determined based on the ratio of the number of pixels to the total number of pixels in the image area.

4. The rapid detection method for heavy metal pollution in land according to claim 1, characterized in that, Compensation of the X-ray energy spectrum data based on the spectral attenuation index spectrum includes: The target attenuation index is determined from the spectral attenuation index spectrum based on the characteristic X-ray energy of the target heavy metal element; Determine whether the target decay index exceeds a preset confidence cutoff threshold; If the target attenuation index is not exceeded, an exponential compensation calculation is performed on the intensity of the corresponding energy position in the X-ray energy spectrum data according to the target attenuation index to obtain the dry basis equivalent intensity of the target heavy metal element.

5. The rapid detection method for heavy metal pollution in land according to claim 4, characterized in that, Based on the compensated X-ray energy spectrum data, the detection result of the region to be detected is determined, including: Based on the dry basis equivalent strength, the dry basis mass fraction of the target heavy metal element is calculated using a preset quantitative model, and this is taken as the detection result.

6. The rapid detection method for heavy metal pollution in land according to claim 4, characterized in that, The method further includes: If the target attenuation index exceeds the preset confidence cutoff threshold, an alarm message indicating that the signal is blocked will be generated and output.

7. A rapid detection device for heavy metal pollution in land, characterized in that, include: The data acquisition unit is used to acquire X-ray energy spectrum data and polarization optical image data of the soil area to be tested; The feature extraction unit is used to determine the light element scattering ratio based on the X-ray energy spectrum data and the water film reflectance based on the polarization optical image data; wherein, the light element scattering ratio is used to characterize the total amount of light element substances in the soil region, and the water film reflectance is used to characterize the degree of coverage of the continuous water film on the surface of the soil region. The decoupling analysis unit is used to perform joint constraint decoupling on the light element scattering ratio and the water film reflectance, determine the probability of the water film presence, and determine the spectral attenuation index spectrum based on the probability of the water film presence; the spectral attenuation index spectrum is used to characterize the transmission attenuation of X-rays in the soil matrix. The signal processing unit is used to compensate the X-ray energy spectrum data based on the spectral attenuation index spectrum, and to determine the detection result of the region to be detected based on the compensated X-ray energy spectrum data. The decoupling analysis unit is specifically used for: The mass thickness of the light matrix is ​​determined based on the light element scattering ratio and the reference scattering ratio; wherein, the reference scattering ratio is used to characterize the scattering characteristics of the mineral skeleton of dry soil; and the mass thickness of the light matrix is ​​used to characterize the equivalent mass thickness of light element material along the X-ray path. The activation weight of water film formation is determined based on the mass thickness of the light matrix and the morphological transformation threshold parameter; the activation weight is used to characterize the possibility of water changing from a pore-filled state to a surface water film state. The probability of the water film's presence is determined by multiplying the water film reflectance by the activation weight. Based on the light matrix mass thickness and the preset water mass attenuation coefficient, calculate the first attenuation component of the corresponding bulk dilution model; The second attenuation component of the corresponding layered shielding model is calculated based on the mass thickness of the lightweight matrix, the mass attenuation coefficient of the water, and the preset path multiplication coefficient; wherein, the path multiplication coefficient is determined by the geometry of the X-ray incident and detection. Based on the probability of the water film's presence, the first attenuation component and the second attenuation component are weighted and synthesized to generate the spectral attenuation index spectrum; wherein, each value of the spectral attenuation index spectrum represents the total optical thickness of the corresponding energy X-ray.

8. A rapid detection system for heavy metal pollution in land, characterized in that, include: X-ray excitation module, polarization imaging module, and rapid detection equipment for heavy metal pollution in soil; The X-ray excitation module is used to emit high-energy X-rays to irradiate the soil sample. The detector captures the characteristic X-rays and scattered X-rays released by the elements in the sample after they are excited, and obtains the X-ray energy spectrum data. The polarization imaging module is used to capture surface images of soil samples, which are then processed by a linear polarizer to obtain polarized optical image data; the rapid detection device for heavy metal pollution in land is used to perform the rapid detection method for heavy metal pollution in land as described in any one of claims 1-6.

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