A rapid detection method and system for soil pollutant concentration

Abstract

This application relates to the technical field of pollutant concentration detection, and particularly to a rapid detection method and system for soil pollutant concentration. The method includes: sampling soils at different positions in the area to be detected, obtaining the diffusion characteristic difference values of each soil sample according to the differences between the extreme value change degrees of the Raman spectroscopy data of each soil sample in the area to be detected and its multiple neighboring soil samples; obtaining the deviation coefficients of each soil sample according to the differences in the peak information of the Raman spectroscopy data between each soil sample and other soil samples; determining the interference coefficients of each soil sample based on the change degrees of the diffusion characteristic difference values of each soil sample and the deviation coefficients of each soil sample; and performing baseline correction on the Raman spectroscopy data of the soil samples in the area to be detected according to the interference coefficients of the soil samples to obtain the detection result of the soil pollutant concentration in the area to be detected. This application can improve the accuracy of soil pollutant concentration detection.

Description

Technical Field

[0001] This application relates to the technical field of pollutant concentration detection, and particularly relates to a method and system for rapid detection of soil pollutant concentration. Background Art

[0002] The rapid detection of soil pollutant concentration is crucial for evaluating soil quality and preventing and controlling soil pollution. The soil in cities mainly comes from the natural weathering process of rocks. Due to the serious impact of human activities on urban soil, the types of pollutants and the concentration of pollutants in the soil increase rapidly. The continuous accumulation of pollutants in the soil will have a serious negative impact on the regional environment, leading to the deterioration of soil quality. Secondly, the long-term retention and diffusion of pollutants in the soil will cause secondary pollution of the surrounding soil. Therefore, rapid and accurate detection of soil pollutant concentration is required to timely formulate and implement soil pollution treatment.

[0003] Currently, the main methods for detecting soil pollutant concentration include physical methods, chemical methods, biological technologies, 3S technologies (GPS, RS, GIS), information technologies, etc. Among them, using spectral detection means in physical methods has relatively high detection efficiency and accuracy for soil pollutant concentration, and can quickly obtain the detection results of soil pollutant concentration. However, due to the complex distribution characteristics of pollution sources in the soil and the intricate groundwater flow, there are relative deviations in the pollutant concentration data of different sampling samples, which affects the detection of pollution concentration based on Raman spectral data of different samples. Existing technologies such as the segmented fitting algorithm and the moving window smoothing algorithm cannot effectively avoid the influence of pollution sources and geological characteristics on the analysis of the noise interference characteristics of Raman spectral data, thereby reducing the accuracy of the rapid detection results of soil pollutant concentration. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for rapid detection of soil pollutant concentration, and the specific technical solutions adopted are as follows:

[0005] An embodiment of this application provides a method for rapid detection of soil pollutant concentration, including the following steps:

[0006] Sample the soil at different positions in the area to be detected to obtain the Raman spectral data of each soil sample;

[0007] Obtain the diffusion characteristic difference value of each soil sample according to the difference between the extreme value change degree of the Raman spectral data of each soil sample in the area to be detected and its multiple neighboring soil samples;

[0008] Obtain the deviation coefficient of each soil sample according to the difference in the peak information of the Raman spectral data of each soil sample and other soil samples;

[0009] Determine the interference coefficient of each soil sample based on the degree of change in the difference value of the diffusion characteristics of each soil sample and the deviation coefficient of each soil sample;

[0010] Perform baseline correction on the Raman spectral data of the soil samples in the area to be detected according to the interference coefficient of the soil samples, and obtain the detection result of the soil pollutant concentration in the area to be detected.

[0011] Preferably, the neighboring soil samples are: taking the collection position of each soil sample as the center, the soil samples at other collection positions within a circular area with a preset radius are used as the neighboring soil samples of each soil sample.

[0012] Preferably, the obtaining of the difference value of the diffusion characteristics of each soil sample includes:

[0013] Statistically analyze the extreme values of the Raman spectral data of each soil sample, arrange the extreme values of each soil sample in ascending order of Raman shift to form an extreme value sequence of each soil sample, and obtain the difference sequence of each extreme value sequence;

[0014] The difference value of the diffusion characteristics of each soil sample is the mean value of the differences between the difference sequence corresponding to each soil sample and the difference sequences of all its neighboring soil samples.

[0015] Preferably, the process of obtaining the deviation coefficient is:

[0016] Perform clustering division through the difference values of the diffusion characteristics of all soil samples, and all soil samples corresponding to the difference values of the diffusion characteristics within each clustering cluster form each sample set;

[0017] Respectively determine the peak shape difference coefficient and the peak position difference coefficient between each soil sample and each other soil sample in its sample set according to the difference in peak width and peak position in the Raman spectral data between each soil sample and each other soil sample in its sample set;

[0018] Based on the peak shape difference coefficient and the peak position difference coefficient, obtain the deviation coefficient of each soil sample.

[0019] Preferably, the process of obtaining the peak shape difference coefficient and the peak position difference coefficient is:

[0020] Respectively form the width sequence and the position sequence of all peaks in the Raman spectral data of each soil sample; respectively take the distances between the width sequences and the distances between the position sequences between each soil sample and each other soil sample in its sample set as the peak shape difference coefficient and the peak position difference coefficient.

[0021] Preferably, the deviation coefficient further includes:

[0022] Analyze the product of the peak shape difference coefficient and the peak position difference coefficient between each soil sample and every other soil sample in the sample set where it is located; determine the mean value of the said products between each soil sample and all other soil samples in the sample set where it is located as the deviation coefficient of each soil sample.

[0023] Preferably, the calculation formula for the interference coefficient of each soil sample is:

[0024] where h x represents the interference coefficient of the x-th soil sample, s x and t x represent the deviation coefficient and the confidence value of the x-th soil sample respectively, and r is a value to avoid the denominator being zero.

[0025] Preferably, the confidence value is the difference between the diffusion characteristic difference value of each soil sample and the mean value of the diffusion characteristic difference values of all soil samples in the sample set where it is located.

[0026] Preferably, further including for the Raman spectral data of the soil samples in the area to be detected to perform baseline correction:

[0027] Use the airPLS algorithm to obtain the Raman spectral data after baseline correction of each soil sample, where the expression of the smoothing parameter of the airPLS algorithm is: λ x = 100×(0.5 + w x ), in the formula, λ x represents the smoothing parameter when performing baseline correction on the Raman spectral data of the x-th soil sample, and w x represents the normalization result of the interference coefficient of the x-th soil sample.

[0028] The embodiment of the present application also provides a rapid detection system for soil pollutant concentration. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the rapid detection method for soil pollutant concentration described in any one of the above.

[0029] As can be seen from the above, the rapid detection method and system for soil pollutant concentration provided by the present application at least have the following beneficial effects:

[0030] This application obtains soil samples by uniformly sampling the soil in the area to be detected, and obtains the Raman spectral data of the soil samples. Considering the influence of the distribution of pollution sources and the characteristics of groundwater flow in the area to be detected, which leads to the existence of a diffusion gradient change in the pollutant concentration of different soil samples, a relative analysis is carried out on the change characteristics of the Raman data in the local area where each soil sample is located. The beneficial effect is to avoid the influence of the difference in pollutant concentration data of different soil samples caused by the distribution of pollution sources and the characteristics of groundwater flow on the analysis of the interference characteristics of the Raman spectral data of the soil samples;

[0031] Furthermore, based on the relative analysis results of the change characteristics of the Raman data in each local area of soil pollutants, this application conducts clustering division on the soil samples in the area to be detected, and conducts a comparative analysis on the peak characteristics of the Raman data of different soil samples according to the clustering division results to obtain the deviation coefficient of the soil samples. The beneficial effect is that the soil samples divided based on the change characteristics of the Raman data in the local area have similar diffusion change characteristics. Through the difference in the peak characteristics of the Raman data of each group of soil samples after division, the relative deviation of the interference characteristics of the Raman spectral data of each soil sample by noise can be accurately obtained;

[0032] Secondly, based on the deviation of the change characteristics of the Raman data of each soil sample in each group of soil samples after clustering division from the overall Raman data change characteristics, this application conducts a fusion process on the deviation coefficient of the soil samples, and adjusts the parameters in the baseline correction process of the Raman spectral data according to the fusion process results. The beneficial effect is to combine the accurate Raman data deviation relationship of each soil sample to obtain the influence degree of noise interference on the Raman spectral data of each soil sample, improve the accuracy of the baseline correction of the Raman spectral data of the soil samples, reduce the influence of pollution diffusion caused by the distribution of pollution sources and groundwater flow on the analysis of the interference characteristics difference of the detection data, and further improve the accuracy of the detection of soil pollutant concentration. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a step flow chart of a rapid detection method for soil pollutant concentration provided by this application;

[0035] Figure 2 It is a schematic diagram of the process for obtaining the deviation coefficient provided by this application. Detailed Embodiments

[0036] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on a rapid soil pollutant concentration detection method and system proposed according to this application, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise specified and limited, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the said element. Additionally, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0038] The following will specifically describe the specific solution of a rapid soil pollutant concentration detection method and system provided by this application in conjunction with the accompanying drawings.

[0039] Please refer to Figure 1 , which shows a step flowchart of a rapid soil pollutant concentration detection method provided by an embodiment of this application, including the following steps:

[0040] Step 1: Sample the soil at different positions in the area to be detected to obtain the Raman spectral data of each soil sample.

[0041] Generally, for rapid detection of soil pollution in an area, sampling detection methods are adopted, including random sampling and uniform sampling, etc. However, due to the influence of factors such as groundwater flow on the pollutant concentration in the soil, there are differences in the pollutant concentration at different positions in the soil. Its main feature is a decreasing concentration along the direction of groundwater flow. However, due to the intricate distribution of groundwater in the soil, this decreasing feature of pollutant concentration exists in a relatively small range. Moreover, due to the different distributions of pollution sources, the distance from the pollution source will also affect the deviation of pollutant concentration.

[0042] Therefore, the soil samples are collected by uniform sampling in the area to be detected. The specific uniform sampling method is as follows: Uniform sampling is carried out at an interval of 1 meter between sampling points. For each soil sample collected, the surface-enhanced Raman spectroscopy (SERS) is used to detect the pollutant concentration, and the Raman spectral data of the pollutants in each soil sample during the detection process is obtained. The Raman spectral data is the Raman scattering intensity at different Raman shifts.

[0043] So far, the pollutant concentration data and Raman spectral data of the soil samples in the area to be detected have been obtained.

[0044] Step 2: Obtain the diffusion characteristic difference values of each soil sample according to the difference between the extreme value change degrees of the Raman spectral data of each soil sample in the area to be detected and its multiple neighboring soil samples.

[0045] Generally, the distribution of pollutants in the soil diffuses from the pollution source to the surrounding areas, and its diffusion characteristics are affected by the groundwater flow. Therefore, if there is soil pollution in the area to be detected, the change of the pollution concentration shows a decreasing characteristic in a local area of the area to be detected, and the relative characteristics of the pollutant concentrations at different positions in the local area are close, that is, as the pollutants diffuse, the change of the relative difference of the pollutant concentration contents at different positions is similar. Based on the above characteristics, the Raman spectral data of different soil samples collected are analyzed, and the noise influence characteristics during the detection process of the Raman spectral data reflecting the soil pollutant concentration are obtained based on the analysis results, and then the Raman spectral data of the soil pollutants are accurately corrected for the baseline to achieve rapid and accurate detection of the soil pollutant concentration.

[0046] Furthermore, since the distribution of pollutants in the soil is affected by the distribution of the pollution source and the groundwater flow, resulting in deviations in the pollutant concentrations obtained from different soil samples, the relative characteristics of the Raman spectra of different soil pollutants are analyzed according to the diffusion distribution characteristics of the pollutants in the soil. The specific calculation and analysis process is as follows:

[0047] According to the above analysis, considering the difference characteristics of the concentration diffusion decreasing change caused by the distribution of the pollution source and the groundwater flow in the local area, the Raman data characteristics of the neighboring areas of the soil samples are compared and analyzed. Specifically, the extreme values of the Raman spectral data of each soil sample are obtained, and the sequence formed by arranging the extreme values in ascending order according to the Raman shift is used as the extreme value sequence of each soil sample, and the difference characteristics of the concentration changes of different pollutants in the area where each soil sample is located are reflected through the extreme value sequence; taking the collection position of each soil sample as the center, the soil samples at other collection positions within a circular area with a radius of 5 meters are used as the neighboring soil samples of each soil sample;

[0048] Obtain the difference sequence of the extreme value sequence of each soil sample, denoted as l. The difference sequence represents the relative characteristics of different pollutant concentrations in the soil sample. Based on the difference sequence, perform a relative analysis on the Raman spectral feature differences between each soil sample and its corresponding neighboring soil samples to obtain the diffusion feature difference value of each soil sample. The diffusion feature difference value of each soil sample is the mean of the differences between the difference sequence corresponding to each soil sample and the difference sequences of all its neighboring soil samples.

[0049] Preferably, in this embodiment, the specific calculation formula for the diffusion feature difference value is:

[0050] where k x represents the diffusion feature difference value of the x-th soil sample; l x represents the difference sequence corresponding to the x-th soil sample, and l x,i represents the difference sequence corresponding to the i-th neighboring soil sample of the x-th sample; n represents the number of neighboring soil samples of the soil sample. The larger the calculated diffusion feature difference value, the greater the possibility that the Raman spectral data for soil pollutant concentration detection has a deviation in scattering intensity due to noise interference based on the comparison of the concentration difference characteristics of different pollutants between each soil sample and its neighboring soil samples.

[0051] Take the diffusion feature difference values of all soil samples as inputs, and use the agglomerative hierarchical clustering algorithm to obtain the clustering results of all the diffusion feature difference values. The set composed of the soil samples corresponding to all the diffusion feature difference values in each clustering cluster is used as each sample set. The purpose of the above division based on the diffusion feature difference values between the region where the soil sample is located and its neighboring soil samples in this embodiment is to accurately perform a comparative analysis on the Raman spectral data of soil samples with similar diffusion characteristics and improve the accuracy of the analysis of the interference influence characteristics of the Raman spectral data.

[0052] Step 3: Obtain the deviation coefficient of each soil sample according to the difference in the peak information of the Raman spectral data curves between each soil sample and other soil samples.

[0053] Furthermore, for each soil sample in each sample set, take the Raman spectral data of the soil sample as input, and use the findpeaks function in MATLAB to obtain the height, width, and position data of the peaks in the Raman spectral data of each soil sample. The sequences composed of the width and position data of all the peaks in the Raman spectral data curve of each soil sample are used as the width sequence and the position sequence respectively;

[0054] If there are significant differences in the peak width and position of the Raman heat dissipation intensity of the Raman spectral data of soil samples with similar diffusion characteristics after the above division, it indicates that the Raman spectral characteristics shift due to noise interference during the analysis of soil pollutant concentration. Therefore, by comparing the differences in different width sequences and different position sequences in each sample set, the deviation coefficient of the soil samples is obtained. The deviation coefficient is used to accurately reflect the relative characteristics of the Raman spectral noise interference in the detection of pollutant concentration in each soil sample, avoiding the problem of deviation in the analysis of Raman data characteristics caused by the influence of pollution sources and groundwater flow.

[0055] Therefore, in this embodiment, according to the peak width difference and peak position difference in the Raman spectral data between each soil sample and other soil samples in its sample set, the peak shape difference coefficient and peak position difference coefficient of each soil sample in the sample set are determined.

[0056] The widths and positions of all peaks in the Raman spectral data of each soil sample are respectively used to form the width sequence and position sequence of each soil sample; the distances between the width sequences of each soil sample and each other soil sample in its sample set, and the distances between the position sequences of each soil sample and each other soil sample in its sample set are respectively used as the peak shape difference coefficient and the peak position difference coefficient between each soil sample and each other soil sample in its sample set.

[0057] Preferably, in this embodiment, the Manhattan distance is used for the distance between sequences. In the actual application process, the implementer can use other distance metrics to analyze the distance between two sequences, and this embodiment does not make special restrictions on this.

[0058] It can be understood that the larger the peak shape difference coefficient and the peak position difference coefficient between each soil sample and each other soil sample in its sample set, the greater the possibility that the Raman spectral data of the soil sample is offset due to noise interference.

[0059] Furthermore, according to the peak shape difference coefficient and peak position difference coefficient between each soil sample and each other soil sample in its sample set, the deviation coefficient of the soil sample is calculated.

[0060] Preferably, in this embodiment, the specific calculation process is as follows: calculate the product of the peak shape difference coefficient and the peak position difference coefficient between each soil sample and each other soil sample in its sample set, and take the mean of the products between each soil sample and all other soil samples in its sample set as the deviation coefficient of each soil sample. It can be understood that the larger the deviation coefficient of each soil sample, the more significant the influence of the noise interference on the Raman spectral data of the x-th soil sample.

[0061] In this embodiment, for the schematic diagram of the process of obtaining the deviation coefficient of each soil sample, please refer specifically to Figure 2 .

[0062] Step 4: Determine the interference coefficient of each soil sample based on the change degree of the diffusion characteristic difference value of each soil sample and the deviation coefficient of each soil sample.

[0063] Generally, if the deviation of the diffusion characteristic difference value of a soil sample from the diffusion characteristic difference values of all soil samples in the sample set to which it belongs is smaller, then the confidence level of the noise interference influence characteristic of the x-th soil sample in the sample set for relative analysis is greater;

[0064] Therefore, for each soil sample in each sample set, the difference between the diffusion characteristic difference value of each soil sample and the mean value of the diffusion characteristic difference values of all soil samples in the sample set to which it belongs is used as the confidence value of each soil sample. Preferably, in this embodiment, the confidence level is the absolute value of the difference between the diffusion characteristic difference value of each soil sample and the mean value of the diffusion characteristic difference values of all soil samples in the sample set to which it belongs. The larger the confidence value, the greater the confidence level of the noise interference influence characteristic of the soil sample in the sample set for relative analysis;

[0065] Further, the interference coefficient of each soil sample is obtained according to the confidence value and deviation coefficient of each soil sample in the sample set; the specific calculation formula is: where h x represents the interference coefficient of the x-th soil sample, s x and t x respectively represent the deviation coefficient and confidence value of the x-th soil sample, and r is a value to avoid the denominator being zero. In this embodiment, the value is 0.01; the larger the calculated interference coefficient, the greater the noise interference influence on the Raman spectrum data of the x-th soil sample.

[0066] Step 5: Perform baseline correction on the Raman spectrum data of the soil samples in the area to be detected according to the interference coefficient of the soil samples, and obtain the detection result of the soil pollutant concentration in the area to be detected.

[0067] Taking the interference coefficients of all soil samples in the area to be detected as the input, the normalization result of the interference coefficients of all soil samples in the area to be detected is obtained by using the Z-score normalization algorithm. The specific implementation process of the Z-score normalization algorithm is a well-known technology and will not be elaborated here.

[0068] Further, the input is the Raman spectral data of each soil sample. The airPLS (adaptive iterative reweighted penalty least square) algorithm is used to obtain the Raman spectral data of each soil sample after baseline correction, where the smoothing parameter λ in the airPLS algorithm is adjusted according to the interference coefficient of each soil sample. The specific adjustment relationship is that λ x = 100×(0.5 + w x ), where λ x represents the smoothing parameter when performing baseline correction on the Raman spectral data of the x-th soil sample, and w x represents the normalized result of the interference coefficient of the x-th soil sample; if through the relative analysis of the Raman spectral data of different soil samples, the relative characteristics of the Raman spectral data of the x-th soil sample being affected by noise are larger, then the adjustment range of the corresponding smoothing parameter λ is relatively increased to improve the suppression degree of the noise influence on the Raman spectral data of the x-th soil sample.

[0069] Further, the surface enhanced Raman spectroscopy technology is used to analyze the Raman spectral data of each soil sample after baseline correction, including establishing a standard curve to correlate the peak intensity in the Raman spectrum with the pollutant concentration, so as to realize the quantitative analysis of the pollutant concentration in the soil and obtain the pollutant concentration data of each soil sample; the specific process of using the surface enhanced Raman spectroscopy technology to obtain the pollutant concentration data in the soil sample is a well-known technology to those skilled in the art and will not be elaborated here.

[0070] Based on the same inventive concept as the above method, the embodiment of the present application also provides a rapid detection system for soil pollutant concentration, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it realizes the steps of the rapid detection method for soil pollutant concentration described in any one of the above.

[0071] It can be understood that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are to illustrate the differences from other embodiments.

[0073] The above content is only the implementation mode of this application and is not used to limit the scope of this application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be similarly included in the protection scope of this application.

Claims

1. A method for rapid detection of soil pollutant concentration, characterized in that: The following steps are involved: Sampling soil at different locations in the area to be tested to obtain Raman spectrum data of each soil sample; Counting the extreme values ​​of the Raman spectrum data of each soil sample, arranging the extreme values ​​of each soil sample in ascending order of Raman shift to form an extreme value sequence of each soil sample, and obtaining a differential sequence of each extreme value sequence; The diffusion characteristic difference value of each soil sample is the mean of the difference between the differential sequences corresponding to each soil sample and all its neighboring soil samples; Clustering is performed through the diffusion characteristic difference values ​​of all soil samples, and all the diffusion characteristic difference values ​​in each cluster correspond to soil samples to form each sample set; Determine a peak shape difference coefficient and a peak position difference coefficient between each soil sample and each other soil sample in the sample set according to the peak width difference and peak position difference in the Raman spectrum data of each soil sample and each other soil sample in the sample set; Analyze the product of the peak shape difference coefficient and the peak position difference coefficient between each soil sample and each other soil sample in the sample set to which it belongs; determine the mean of the product between each soil sample and all other soil samples in the sample set to which it belongs as the deviation coefficient of each soil sample; Based on the degree of change of the diffusion characteristic difference value of each soil sample and the deviation coefficient of each soil sample, the interference coefficient of each soil sample is determined; the calculation formula of the interference coefficient is: ,in Indicates The interference coefficient of each soil sample, and Respectively represent The coefficient of variation and confidence value of each soil sample, r is a value to avoid the denominator being zero Baseline correction is performed on the Raman spectrum data of the soil samples in the area to be tested according to the interference coefficient of the soil samples to obtain the soil pollutant concentration test results of the area to be tested.

2. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The neighboring soil samples are: taking the collection position of each soil sample as the center, soil samples at other collection positions within a circular area with a preset radius are used as the neighboring soil samples of each soil sample.

3. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The process of obtaining the peak shape difference coefficient and the peak position difference coefficient is as follows: The widths and positions of all peaks in the Raman spectrum data of each soil sample are respectively combined into a width sequence and a position sequence of each soil sample; the distance between the width sequence of each soil sample and the distance between the position sequences of each soil sample in the sample set to which it belongs are respectively used as the peak shape difference coefficient and the peak position difference coefficient.

4. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The confidence value is the difference between the diffusion characteristic difference value of each soil sample and the mean value of the diffusion characteristic difference values ​​of all soil samples in the sample set to which the soil sample belongs.

5. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The baseline correction of the Raman spectrum data of the soil sample in the detection area further includes: The airPLS algorithm is used to obtain the baseline-corrected Raman spectral data of each soil sample, where the smoothing parameter of the airPLS algorithm is expressed as: , where Expressing the The smoothing parameter for baseline correction of Raman spectroscopy data of soil samples is Indicates Normalized results of the interference coefficient for each soil sample.

6. A soil pollutant concentration rapid detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for rapid detection of soil pollutant concentration as described in any one of claims 1 to 5 are implemented.

Images