Method and system for rapidly detecting concentration of soil pollutants

Abstract

The invention relates to the technical field of pollutant concentration detection, in particular to a soil pollutant concentration rapid detection method and a soil pollutant concentration rapid detection system. According to the difference between the extreme value change degrees of the Raman spectrum data of each soil sample and the plurality of adjacent soil samples in the to-be-detected area, acquiring a diffusion characteristic difference value of each soil sample; according to the difference between the peak information of the Raman spectrum data of each soil sample and the peak information of the Raman spectrum data of other soil samples, obtaining a deviation coefficient of each soil sample; determining 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; performing baseline correction on the Raman spectrum data of the soil sample in the to-be-detected area according to the interference coefficient of the soil sample to obtain a soil pollutant concentration detection result of the to-be-detected area. According to the invention, the accuracy of soil pollutant concentration detection can be improved.

Description

Technical Field

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

[0002] Rapid detection of soil pollutant concentrations is crucial for assessing soil quality and preventing and controlling soil pollution. Soil in cities mainly comes from the natural weathering process of rocks. Since urban soil is severely affected by human activities, the types of pollutants in the soil and the concentration of pollutants are growing rapidly. The continuous accumulation of pollutants in the soil will have a serious negative impact on the regional environment and lead to 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, it is necessary to conduct rapid and accurate detection of soil pollutant concentrations and formulate and implement soil pollution treatment in a timely manner.

[0003] Nowadays, there are mainly physical methods, chemical methods, biotechnology, 3S technology (GPS, RS, GIS), information technology, etc. for detecting soil pollutant concentrations. Among them, the spectral detection method in the physical method has relatively high efficiency and accuracy in detecting pollutant concentrations in soil, and can quickly obtain soil pollutant concentration detection results. However, due to the influence of the distribution characteristics of pollution sources in the soil and the complex groundwater flow, there are relative deviations in the pollutant concentration data of different sampling samples, which affects the influence of Raman spectral data based on different samples on pollution concentration detection. The 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 noise interference feature difference analysis of Raman spectral data, thereby reducing the accuracy of the rapid detection results of soil pollutant concentrations. 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. The technical solutions adopted are as follows:

[0005] The present invention provides a method for rapid detection of soil pollutant concentration, comprising the following steps:

[0006] Sampling soil at different locations in the area to be tested to obtain Raman spectrum data of each soil sample;

[0007] Obtaining diffusion characteristic difference values ​​of each soil sample according to the difference between the extreme value change degrees of Raman spectrum data of each soil sample and its multiple neighboring soil samples in the area to be detected;

[0008] Obtaining a deviation coefficient of each soil sample according to a difference in peak information between Raman spectrum data of each soil sample and other soil samples;

[0009] Determining the interference 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;

[0010] 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.

[0011] Preferably, 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.

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

[0013] 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;

[0014] 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.

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

[0016] 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;

[0017] 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;

[0018] The deviation coefficient of each soil sample is obtained based on the peak shape difference coefficient and the peak position difference coefficient.

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

[0020] 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.

[0021] Preferably, the deviation coefficient further includes:

[0022] 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 is analyzed; the average of the products between each soil sample and all other soil samples in the sample set to which it belongs is determined as the deviation coefficient of each soil sample.

[0023] Preferably, the calculation formula of 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 coefficient of deviation and confidence value of the x-th soil sample respectively, and r is the 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 to which the soil sample belongs.

[0026] Preferably, the baseline correction of the Raman spectrum data of the soil sample in the detection area further includes:

[0027] 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: x =100×(0.5+w x ), where λ x represents the smoothing parameter for baseline correction of the Raman spectrum data of the x-th soil sample, w x Represents the normalized result of the interference coefficient of the xth soil sample.

[0028] An embodiment of the present application also provides a system for rapid detection of soil pollutant concentrations, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned methods for rapid detection of soil pollutant concentrations when executing the computer program.

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

[0030] The present application obtains soil samples by uniformly sampling the soil in the area to be tested, and obtains Raman spectrum data of the soil samples, taking into account the influence of the diffusion gradient change of the pollutant concentration of different soil samples caused by the distribution of pollution sources in the area to be tested and the groundwater flow characteristics, and performs a relative analysis of the Raman data change characteristics in the local area where each soil sample is located, which has the beneficial effect of avoiding the influence of the difference in pollutant concentration data of different soil samples caused by the distribution of pollution sources and the groundwater flow characteristics on the interference characteristic analysis of the Raman spectrum data of the soil samples;

[0031] Furthermore, the present application performs a relative analysis result based on the Raman data change characteristics of each local area of ​​soil pollutants, clusters the soil samples in the detection area, and compares and analyzes the Raman data peak characteristics of different soil samples according to the clustering results to obtain the deviation coefficient of the soil samples. The beneficial effect is that the soil samples divided based on the Raman data change characteristics of the local area have similar similar diffusion change characteristics. Through the difference in the Raman data peak characteristics of each group of soil samples after division, the relative deviation of the noise interference characteristics of the Raman spectrum data of each soil sample can be accurately obtained.

[0032] Secondly, the present application fuses the deviation coefficient of the soil sample based on the deviation of the Raman data change characteristics of each soil sample in each group of soil samples after clustering division relative to the overall Raman data change characteristics, and adjusts the parameters in the Raman spectrum data baseline correction process according to the fusion processing results. The beneficial effect is that the degree of noise interference influence of the Raman spectrum data of each soil sample is obtained by combining the accurate Raman data deviation relationship of each soil sample, thereby improving the accuracy of the baseline correction of the Raman spectrum data of the soil sample, reducing the influence of the distribution of pollution sources and the diffusion of pollution caused by groundwater flow on the analysis of the interference characteristics of the detection data, and thereby improving the accuracy of the detection of soil pollutant concentrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 A flowchart of the steps of a method for rapid detection of soil pollutant concentration provided in this application;

[0035] Figure 2 Schematic diagram of the deviation coefficient acquisition process provided in this application. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the soil pollutant concentration rapid detection method and system proposed in this application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless otherwise specified and limited, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0038] The specific scheme of a method and system for rapid detection of soil pollutant concentration provided by the present application is described in detail below with reference to the accompanying drawings.

[0039] See also Figure 1 , which shows a flowchart of a method for rapid detection of soil pollutant concentration provided by an embodiment of the present application, including the following steps:

[0040] Step 1: Sample the soil at different locations in the detection area and obtain the Raman spectrum data of each soil sample.

[0041] Normally, rapid detection of soil pollution in a region is carried out by sampling detection, including random sampling and uniform sampling. However, since the concentration of pollutants in the soil is affected by factors such as groundwater flow, there are differences in the concentration of pollutants in different locations in the soil. Its main feature is a decreasing concentration along the direction of groundwater flow. However, due to the complex distribution of groundwater in the soil, the decreasing characteristics of the above-mentioned pollutant concentration exist in a smaller range. In addition, due to differences in the distribution of pollution sources, the distance from the pollution source will also affect the deviation of the pollutant concentration.

[0042] Therefore, uniform sampling is adopted to collect soil samples in the inspection area. The specific uniform sampling method is: uniform sampling is performed with a distance of 1 meter between sampling points. For each soil sample collected, surface enhanced Raman spectroscopy (SERS) is used to detect the concentration of pollutants, and the Raman spectrum data of pollutants in each soil sample during the inspection process is obtained, wherein the Raman spectrum data is the Raman scattering intensity under different Raman shifts.

[0043] At this point, the pollutant concentration data and Raman spectrum data of the soil samples in the area to be tested have been obtained.

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

[0045] Normally, the distribution of pollutants in the soil diffuses around the pollution source, and its diffusion characteristics are affected by groundwater flow. Therefore, if there is soil pollution in the area to be detected, the change in pollution concentration in the local area of ​​the area to be detected will show a decreasing characteristic, and the relative characteristics of pollutant concentrations at different locations in the local area are close, that is, as the pollutant diffuses, the changes in the relative differences in pollutant concentrations at different locations are similar. Based on the above characteristics, the Raman spectral data of different soil samples collected are analyzed, and based on the analysis results, the noise influence characteristics reflecting the Raman spectral data detection process of soil pollutant concentration are obtained, and then the Raman spectral data of soil pollutants are accurately baseline corrected to achieve rapid and accurate detection of soil pollutant concentrations.

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

[0047] According to the above analysis, considering the difference characteristics of concentration diffusion decrease caused by the distribution of pollution sources and groundwater flow in local areas, the Raman data characteristics of the neighboring areas of the soil samples are compared and analyzed. Specifically, the extreme values ​​of the Raman spectrum data of each soil sample are obtained, and the extreme values ​​are arranged in ascending order according to the Raman shift as the extreme value sequence of each soil sample. The extreme value sequence reflects the difference in concentration change characteristics of different pollutants in the area where each soil sample is located; with each soil sample collection location as the center, the soil samples of other collection locations within a circular area with a radius of 5 meters are taken as the neighboring soil samples of each soil sample;

[0048] The differential sequence of the extreme value sequence of each soil sample is obtained, denoted as l. The differential sequence represents the relative characteristics of different pollutant concentrations in the soil sample. According to the differential sequence, the Raman spectrum characteristic differences of each soil sample and the corresponding neighboring soil samples are relatively analyzed to obtain the diffusion characteristic difference value of each soil sample. The diffusion characteristic difference value of each soil sample is the mean value of the difference between the differential sequences corresponding to each soil sample and all its neighboring soil samples.

[0049] Preferably, in this embodiment, the specific calculation relationship of the diffusion characteristic difference value is:

[0050] Among them, k x represents the diffusion characteristic difference value of the x-th soil sample; l x represents the difference sequence corresponding to the x-th soil sample, l x,i represents the differential sequence corresponding to the ith neighboring soil sample of the xth sample; n represents the number of neighboring soil samples of the soil sample; the larger the calculated diffusion characteristic difference value is, the greater the possibility that the Raman spectrum data of soil pollutant concentration detection will lead to scattering intensity deviation due to noise interference based on the comparison of concentration difference characteristics of different pollutants in each soil sample and its neighboring soil samples.

[0051] The diffusion characteristic difference values ​​of all soil samples are used as input, and the clustering results of all the diffusion characteristic difference values ​​are obtained by using the agglomerative hierarchical clustering algorithm, and the set of soil samples corresponding to all the diffusion characteristic difference values ​​in each cluster is used as each sample set. The purpose of the above-mentioned division based on the diffusion characteristic difference values ​​of the area where the soil sample is located and the neighboring soil samples in this embodiment is to accurately compare and analyze the Raman spectral data of soil samples with similar diffusion characteristics, and improve the accuracy of the feature analysis affected by the interference of the Raman spectral data.

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

[0053] Furthermore, for each soil sample in each sample set, the Raman spectrum data of the soil sample is used as input, and the findpeaks function of MATLAB is used to obtain the height, width and position data of the peak in the Raman spectrum data of each soil sample, and the sequence composed of the width and position data of all peaks in the Raman spectrum data curve of each soil sample is used as the width sequence and the position sequence respectively;

[0054] If there are large 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 are offset and changed due to noise interference during the soil pollutant concentration analysis. Therefore, the deviation coefficient of the soil sample is obtained by comparing the differences in different width sequences and different position sequences in each sample set. The deviation coefficient is used to accurately reflect the relative characteristics of the Raman spectral noise interference of the pollutant concentration detection of each soil sample, thereby avoiding the problem of deviation in the Raman data feature analysis caused by the influence of pollution sources and groundwater flow.

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

[0056] 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 each other soil sample in the sample set, and the distance between the position sequence of each soil sample and each other soil sample in the 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 the sample set.

[0057] Preferably, the distance between the sequences in this embodiment adopts Manhattan distance. In actual application, the implementer may adopt other distance measurement methods to analyze the distance between two sequences, and this embodiment does not impose any special limitation on this.

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

[0059] Furthermore, the deviation coefficient of the soil sample is calculated according to 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.

[0060] Preferably, in this embodiment, the specific calculation process is: 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 the sample set to which it belongs, and take the average of the products 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. It can be understood that the larger the deviation coefficient of each soil sample, the more significant the noise interference effect of the Raman spectrum data of the x-th soil sample.

[0061] In this embodiment, the deviation coefficient of each soil sample is obtained by the flowchart. Figure 2 .

[0062] Step 4: Determine the interference 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.

[0063] Generally speaking, the smaller the difference between the diffusion characteristics of a soil sample and the diffusion characteristics of all soil samples in the sample set, the greater the confidence level of the relative analysis of the noise interference effect on the x-th soil sample in the sample set;

[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 diffusion characteristic difference value of all soil samples in the sample set 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 diffusion characteristic difference value of all soil samples in the sample set. The larger the confidence value, the greater the confidence level of the relative analysis of the noise interference effect of the soil sample in the sample set.

[0065] Furthermore, 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 They respectively represent the deviation coefficient and confidence value of the x-th soil sample, r is a value to avoid the denominator being zero, and in this embodiment, the value is 0.01; the larger the calculated interference coefficient is, the greater the noise interference effect of the Raman spectrum data of the x-th soil sample is.

[0066] Step 5: Baseline correction is performed on the Raman spectrum data of the soil samples in the test area according to the interference coefficient of the soil samples to obtain the soil pollutant concentration test results of the test area.

[0067] The interference coefficients of all soil samples in the area to be detected are taken as input, and the Z-score normalization algorithm is used to obtain the normalized results of the interference coefficients of all soil samples in the area to be detected. The specific implementation process of the Z-score normalization algorithm is a well-known technology and will not be described in detail.

[0068] Furthermore, the input is the Raman spectrum data of each soil sample, and the airPLS (adaptive iterative reweighted penalty least square) algorithm is used to obtain the baseline-corrected Raman spectrum data of each soil sample, wherein the smoothing parameter λ in the airPLS algorithm is adjusted according to the interference coefficient of each soil sample. The specific adjustment relationship is λ x =100×(0.5+w x ), where λ x represents the smoothing parameter for baseline correction of the Raman spectrum data of the x-th soil sample, w x It represents the normalized result of the interference coefficient of the x-th soil sample. If, through relative analysis of the Raman spectral data of different soil samples, the Raman spectral data of the x-th soil sample is relatively more susceptible to noise interference, the adjustment amplitude of the corresponding smoothing parameter λ is relatively increased, thereby improving the degree of suppression of the noise influence in the Raman spectral data of the x-th soil sample.

[0069] Furthermore, the baseline-corrected Raman spectroscopy data of each soil sample is analyzed using surface-enhanced Raman spectroscopy technology, including establishing a standard curve to correlate the peak intensity in the Raman spectrum with the pollutant concentration, thereby achieving quantitative analysis of the pollutant concentration in the soil and obtaining the pollutant concentration data of each soil sample; the specific process of obtaining the pollutant concentration data in the soil sample using surface-enhanced Raman spectroscopy technology is a technology well known to those skilled in the art and will not be described in detail.

[0070] Based on the same inventive concept as the above method, an embodiment of the present application also provides a soil pollutant concentration rapid detection system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned soil pollutant concentration rapid detection methods are implemented.

[0071] It is to be understood that the sequence of the embodiments of the present application described above is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present specification. In addition, the processes depicted in the accompanying 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] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0073] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the protection scope of the present 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; Obtaining diffusion characteristic difference values ​​of each soil sample according to the difference between the extreme value change degrees of Raman spectrum data of each soil sample and its multiple neighboring soil samples in the area to be detected; Obtaining a deviation coefficient of each soil sample according to a difference in peak information between Raman spectrum data of each soil sample and other soil samples; Determining the interference 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; 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 acquisition of the diffusion characteristic difference value of each soil sample includes: 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.

4. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The process of obtaining the deviation coefficient is as follows: 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; The deviation coefficient of each soil sample is obtained based on the peak shape difference coefficient and the peak position difference coefficient.

5. A method for rapid detection of soil pollutant concentration according to claim 4, 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.

6. A method for rapid detection of soil pollutant concentration according to claim 4, characterized in that: The coefficient of variation further includes: 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 is analyzed; the average of the products between each soil sample and all other soil samples in the sample set to which it belongs is determined as the deviation coefficient of each soil sample.

7. A method for rapid detection of soil pollutant concentration according to claim 1, characterized in that: The calculation formula of the interference coefficient of each soil sample is: where h x represents the interference coefficient of the x-th soil sample, s x and t x represent the coefficient of deviation and confidence value of the x-th soil sample respectively, and r is the value to avoid the denominator being zero.

8. A method for rapid detection of soil pollutant concentration according to claim 7, 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.

9. 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: x =100×(0.5+w x ), where λ x represents the smoothing parameter for baseline correction of the Raman spectrum data of the x-th soil sample, w x Represents the normalized result of the interference coefficient of the xth soil sample.

10. 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 9 are implemented.

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