Regional sediment organic carbon library evaluation method and device based on infrared spectroscopic analysis

Abstract

The invention relates to a regional sediment organic carbon library evaluation method and device based on infrared spectroscopic analysis, and relates to the technical field of measurement, the method comprises the following steps: using a target sediment sample collected from a target region to carry out infrared spectroscopic measurement and correction treatment to obtain a correction spectrum, determining a characteristic band based on the correction spectrum, and calculating the organic carbon library of the regional sediment. Carrying out area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, carrying out organic carbon relation mapping based on the local peak area to obtain function relation information, and carrying out section estimation processing according to the target sediment sample to obtain the total sediment mass of the sediment sample in the target region; on the basis of the function relation information and the total mass of the sediments, organic carbon library estimation is conducted on the target area, organic carbon library information of the target area is obtained, and the content of stable organic carbon in the sediments in the area can be rapidly and accurately evaluated under the condition that only a small number of samples are used through red light spectroscopic analysis.

Description

Technical Field

[0001] The present application relates to the field of measurement technology, and in particular to a method and device for evaluating regional sediment organic carbon pool based on infrared spectroscopy analysis. Background Art

[0002] Sediment organic carbon is not only an important indicator of water environmental quality, but also an important component of regional ecosystem carbon accounting. As one of the main carbon pools in river and lake systems, the carbon storage and release process of sediments has a significant impact on regional carbon balance. However, the current assessment methods for water system carbon pools are not yet fully mature, especially in the assessment of stable organic carbon in sediments, where there is still limited understanding. Stable organic carbon is an important component of the sediment carbon pool, and its response to environmental changes and long-term stability are crucial to assessing the carbon sequestration potential and carbon cycle dynamics of the sediment carbon pool.

[0003] Existing carbon pool assessment methods, such as elemental analysis and radiocarbon dating, provide a certain basis for understanding sediment carbon pools. However, the formation mechanism and transformation process of stable organic carbon and its interaction with river and lake environmental factors are still unclear, which increases the complexity of sediment carbon pool assessment. Existing carbon pool assessment methods are limited by complex environmental factors and cannot accurately assess the total organic carbon pool and stable organic carbon pool in water system areas. Summary of the invention

[0004] The present application provides a method and device for assessing the regional sediment organic carbon pool based on infrared spectroscopy analysis, which utilizes the high sensitivity and non-destructive detection characteristics of the infrared spectroscopy analysis method to provide a new solution for the rapid assessment of stable organic carbon in sediments. By using infrared spectroscopy technology combined with the functional relationship between organic carbon and peak area, as well as the estimated results of the total mass of sediments, the regional sediment organic carbon pool assessment method provided by the present application can be applied to complex scenarios, and can accurately assess the content of stable organic carbon in sediments with only a small amount of sample, thereby providing a new technical means for the assessment of river and lake sediment carbon pools, and solving the problem that existing carbon pool assessment methods are limited by complex environmental factors and cannot accurately assess the organic carbon pool in water system areas.

[0005] In a first aspect, the present application provides a method for assessing regional sediment organic carbon pools based on infrared spectroscopy analysis, comprising:

[0006] collecting target sediment samples from the target area;

[0007] Performing infrared spectrum measurement and correction processing on the target sediment sample to obtain a corrected spectrum;

[0008] Determine a characteristic band based on the correction spectrum, and perform area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band;

[0009] Based on the local peak area, organic carbon relationship mapping is performed to obtain functional relationship information, and cross-section estimation processing is performed based on the target sediment sample and the target area to obtain the total mass of sediment in the target area;

[0010] Based on the functional relationship information and the total mass of the sediment, the organic carbon pool of the target area is estimated to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

[0011] Optionally, collecting a target sediment sample from the target area includes:

[0012] Determine the information needed for the assessment;

[0013] Based on the evaluation requirement information, a grab bucket collection instruction is generated, wherein the grab bucket collection instruction is used to control the grab bucket to collect surface sediment samples from the target area and use the surface sediment samples as target sediment samples;

[0014] or, based on the assessment requirement information, generating a columnar sampler collection instruction, the columnar sampler collection instruction being used to control a columnar sampler to collect a sediment column from the target area;

[0015] The sediment column is pretreated to obtain a dry sediment sample, and the dry sediment sample is used as a target sediment sample.

[0016] Optionally, performing infrared spectrum measurement and correction processing on the target sediment sample to obtain a corrected spectrum includes:

[0017] Performing infrared spectrum measurement based on the target sediment sample to obtain an infrared spectrum average;

[0018] According to preset correction information, the infrared spectrum mean is subjected to spectrum correction preprocessing to obtain a corrected spectrum;

[0019] The correction information includes atmospheric correction and baseline correction.

[0020] Optionally, determining a characteristic band based on the corrected spectrum comprises:

[0021] extracting main functional group information of organic carbon from the corrected spectrum;

[0022] Band selection is performed based on the main functional group information to obtain characteristic bands.

[0023] Optionally, performing area integration processing on the corrected spectrum based on the characteristic band to obtain a local peak area includes:

[0024] Determine characteristic band upper and lower limit information based on the characteristic band, wherein the characteristic band upper and lower limit information includes a characteristic band upper limit and a characteristic band lower limit;

[0025] Draw a local baseline based on the upper and lower limit information of the characteristic band combined with the correction spectrum;

[0026] The peak area is integrated according to the local baseline to obtain the local peak area.

[0027] Optionally, the functional relationship information includes first functional relationship information and second functional relationship information, and the organic carbon relationship mapping is performed based on the local peak area to obtain the functional relationship information, including:

[0028] According to OC t =1.2814*(A2+A4 / A5+A3 / A1)+0.71131, determine the first functional relationship information;

[0029] According to OC s =0.023646*(A3+A1*A2)+0.62995, determine the second functional relationship information;

[0030] Among them, the first functional relationship information is used to characterize the functional relationship between the total organic carbon and the local peak area in the water body area, and the second functional relationship information is used to characterize the functional relationship between the stable organic carbon and the local peak area in the water body area, and A1, A2, A3, A4 and A5 are all local peak areas corresponding to characteristic bands.

[0031] Optionally, the cross-section estimation process is performed based on the target sediment sample and in combination with the target area to obtain the total mass of sediment in the target area, including:

[0032] By combining a preset cross-sectional method with a remote sensing image method, the cross-sectional volume of the sediment in the target area is detected to obtain the total volume of the sediment in the target area;

[0033] Based on the target sediment sample, layered cutting and layered measurement processing are performed to obtain the comprehensive bulk density of the sediment;

[0034] The total mass of the sediment in the target area is determined based on the total volume of the sediment and the comprehensive bulk density of the sediment.

[0035] Optionally, the cross-sectional volume detection of the sediments in the target area is performed by combining a preset cross-sectional method with a remote sensing image to obtain the total volume of the sediments in the target area, including:

[0036] Obtaining a remote sensing map of the target area and depth information of the sediment to be assessed in the target area;

[0037] According to the preset section spacing, the remote sensing map is used to divide the spacing to obtain at least two mutually parallel cross sections;

[0038] Calculating the area between the cross sections according to the depth information to obtain an average area of ​​two adjacent cross sections;

[0039] Based on the product of the average area and the section spacing, a cross-sectional volume between two adjacent cross-sections in all cross-sections is obtained;

[0040] The total volume of sediments in the target area is obtained by accumulating the cross-sectional volumes.

[0041] Optionally, the step of performing layered cutting and layered measurement processing based on the target sediment sample to obtain a comprehensive bulk density of the sediment includes:

[0042] Performing layered cutting and recording based on the target sediment sample, obtaining layered sediment samples, layered volumes corresponding to each layer of the layered sediment samples, and depth ratios between the layered sediment samples;

[0043] Preprocessing each layer of the stratified sediment sample to obtain the stratified mass corresponding to the stratified sediment sample;

[0044] Determining the stratified bulk density corresponding to the stratified sediment sample based on the stratified volume and the stratified mass;

[0045] The comprehensive bulk density of the sediment is calculated based on the depth ratio and the stratified bulk density.

[0046] In a second aspect, the present application provides a device for evaluating regional sediment organic carbon pools based on infrared spectroscopy analysis, comprising:

[0047] A collection module, used for collecting target sediment samples from a target area;

[0048] A spectrum measurement and correction module, used for performing infrared spectrum measurement and correction processing according to the target sediment sample to obtain a corrected spectrum;

[0049] An area integration processing module is used to determine a characteristic band based on the correction spectrum, and perform area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band;

[0050] A relationship mapping and cross-section estimation processing module, for performing organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and performing cross-section estimation processing based on the target sediment sample and the target area to obtain the total mass of sediment in the target area;

[0051] The estimation module is used to estimate the organic carbon pool of the target area based on the functional relationship information and the total mass of the sediment, so as to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

[0052] In summary, the embodiment of the present application uses the target sediment sample collected from the target area to perform infrared spectroscopy measurement and correction processing to obtain a corrected spectrum, determine the characteristic band based on the corrected spectrum, and perform area integration processing on the corrected spectrum based on the characteristic band to obtain the local peak area, and use the local peak area to characterize the integral area of ​​the target sediment sample in each characteristic band, and then perform organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and perform cross-section estimation processing based on the target sediment sample to obtain the total sediment mass of the sediment sample in the target area, and then estimate the organic carbon pool of the target area based on the functional relationship information and the total sediment mass, and obtain the organic carbon pool information of the target area. It can be seen that the embodiments of the present application make full use of the high sensitivity and non-destructive detection characteristics of the infrared spectroscopy analysis method. By using infrared spectroscopy technology combined with the functional relationship between organic carbon and peak area, and the estimation results of the total mass of sediment, the regional sediment organic carbon pool assessment method provided by the present application can be applied to complex scenarios. It can accurately assess the content of stable organic carbon in sediments with only a small amount of sample, thereby providing a new technical means for the assessment of river and lake sediment carbon pools, and solving the problem that existing carbon pool assessment methods cannot accurately assess the organic carbon pool in water system areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0055] Figure 1 A schematic diagram of a flow chart of a method for assessing regional sediment organic carbon pools based on infrared spectroscopy analysis provided in an embodiment of the present application;

[0056] Figure 2This is a schematic diagram of the steps of a method for assessing regional sediment organic carbon pools based on infrared spectroscopy analysis provided by an optional embodiment of the present application;

[0057] Figure 3 It is a characteristic band partition schematic diagram provided by an optional example of the present application;

[0058] Figure 4 This is an optional example of this application, providing a linear fitting curve diagram of the measured stable organic carbon pool and the estimated value in rivers and lakes in Guangzhou;

[0059] Figure 5 This is a schematic diagram of a linear fitting curve of the measured stable organic carbon pool and the estimated value in Zhongshan rivers and lakes provided as an optional example of this application;

[0060] Figure 6 This is a schematic diagram of a linear fitting curve of the measured stable organic carbon pool and the estimated value in rivers and lakes in Zhongshan City, provided as an optional example of this application;

[0061] Figure 7 It is a schematic diagram of a linear fitting curve of the measured stable organic carbon pool in wetland sediments and the estimated value provided by an optional example of this application;

[0062] Figure 8 This is a schematic diagram of a linear fitting curve of the measured stable organic carbon pool in lake sediments and the estimated value provided by an optional example of this application;

[0063] Fig. 9 It is a schematic diagram of a linear fitting curve of the measured stable organic carbon pool in river sediments and the estimated value provided by an optional example of this application;

[0064] Fig.10 A structural block diagram of a regional sediment organic carbon pool assessment device based on infrared spectroscopy analysis provided in an embodiment of the present application;

[0065] Fig.11 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0067] To facilitate the understanding of the embodiments of the present application, further explanation will be given below in conjunction with the drawings and specific embodiments. The embodiments do not constitute a limitation on the embodiments of the present application.

[0068] Figure 1 A schematic diagram of a method for evaluating regional sediment organic carbon pools based on infrared spectroscopy provided in an embodiment of the present application. Figure 1 As shown, the regional sediment organic carbon pool assessment method based on infrared spectroscopy analysis provided in the embodiment of the present application may specifically include the following steps:

[0069] Step 110 , collecting target sediment samples from the target area.

[0070] Specifically, the target area usually refers to the ecological environment within a certain range, mainly including but not limited to: water system areas, woodlands, wetlands, bare land and other ecological environments. Among them, water system areas can include but not limited to: rivers, lakes, ponds, coastal areas and aquatic plants, etc., and woodlands can include but not limited to: woods, mangroves, etc.

[0071] In this embodiment, the target sediment sample may include but is not limited to: surface sediment sample, fresh sediment sample, dry sediment sample, etc.

[0072] In a specific implementation, for surface sediment samples, this embodiment can use a grab bucket to collect target sediment samples from the target area. Preferably, a 20 cm surface sediment sample is collected using a grab bucket.

[0073] For fresh sediment samples, this embodiment can use a columnar sampler to collect sediment columns (referred to as columnar samples), and the columnar samples are layered according to purpose.

[0074] Preferably, in order to improve the assessment accuracy, after the fresh sediment sample is collected in this embodiment, a series of pretreatments can be performed on the fresh sediment sample, including but not limited to removing large stones and debris, followed by freeze-drying to produce a dry sediment sample as the target sediment sample.

[0075] Furthermore, in the related art, the existing carbon pool assessment methods often have the problem of high sample demand. The regional sediment organic carbon pool assessment method based on infrared spectroscopy analysis provided in the embodiment of the present application only needs to collect a small amount of target sediment samples through a grab bucket or a columnar sampler to accurately assess the organic carbon pool in the target area, effectively reducing the assessment cost and sample demand.

[0076] In an optional embodiment, the embodiment of the present application collects target sediment samples from the target area, which may specifically include: determining evaluation requirement information; generating a grab collection instruction based on the evaluation requirement information, the grab collection instruction being used to control the grab to collect surface sediment samples from the target area, and using the surface sediment samples as target sediment samples; or, based on the evaluation requirement information, generating a columnar sampler collection instruction, the columnar sampler collection instruction being used to control a columnar sampler to collect sediment columns from the target area; preprocessing the sediment columns to obtain dry sediment samples, and using the dry sediment samples as target sediment samples.

[0077] Step 120: perform infrared spectrum measurement and calibration processing on the target sediment sample to obtain a calibrated spectrum.

[0078] In a specific implementation, the present invention can first perform spectral measurement on the target sediment sample to obtain the infrared spectrum corresponding to the sediment sample, and then perform multiple preprocessing on the infrared spectrum, including but not limited to atmospheric correction, baseline correction, etc., to obtain a corrected infrared spectrum as a correction spectrum. The corrected spectrum can then be used for peak area integration and other processing, thereby effectively utilizing the high sensitivity and non-destructive detection characteristics of the infrared spectroscopy analysis method.

[0079] Step 130: determine a characteristic band based on the corrected spectrum, and perform area integration processing on the corrected spectrum based on the characteristic band to obtain a local peak area.

[0080] The local peak area is used to characterize the integrated area of ​​the target sediment sample in the characteristic band.

[0081] Step 140, performing organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and performing cross-section estimation processing based on the target sediment sample and the target area to obtain the total mass of sediment in the target area.

[0082] Step 130 and step 140 are described uniformly:

[0083] Specifically, the local peak area is also called the relative peak area, and the local peak area mainly includes the integral area corresponding to the target sediment in each characteristic band. According to research experience, there is a certain functional relationship between total organic carbon (OCt) and stable organic carbon (OCs) and the integral area of ​​the characteristic band. In this embodiment, the functional relationship information may include the functional relationship between total organic carbon and the integral area of ​​the characteristic band and the functional relationship between stable organic carbon and the integral area of ​​the characteristic band.

[0084] In actual implementation, the infrared spectrum band contains the main functional group information of organic carbon. This embodiment can select characteristic bands according to the correction spectrum, select a certain number of characteristic bands corresponding to the main functional group information of organic carbon from the correction spectrum, and use them to integrate the peak area. The local peak area is obtained by integrating the peak area, and the local peak area is used to characterize the integral area of ​​each characteristic band. Subsequently, the local peak area is used to map the functional relationship with organic carbon, that is, the integral area of ​​each characteristic band is mapped with the organic carbon to obtain the functional relationship information, so that the total amount of organic carbon in the target area can be estimated using the functional relationship information.

[0085] In the specific implementation, the total mass of sediment in the target area can be estimated and determined based on the total volume of sediment and the comprehensive bulk density of sediment in the target area. Specifically, the comprehensive bulk density corresponding to the target sediment sample is determined by performing multiple pre-processing steps such as layered cutting on the target sediment sample; the target area is divided according to a certain interval, such as dividing the remote sensing map corresponding to the target area, to obtain multiple divided areas. Then, the cross-sectional volume of each divided area is calculated to obtain the total volume of sediment in the entire area.

[0086] Furthermore, existing carbon pool assessment methods still have problems such as being time-consuming and cumbersome to operate. In this regard, the embodiments of the present application fully utilize the high sensitivity and non-destructive detection characteristics of infrared spectroscopy analysis methods, provide a basis for the rapid assessment of stable organic carbon in sediments, improve the accuracy of subsequent assessments of organic carbon pools in target areas, reduce the time consumption of organic carbon pool assessments, and simplify the assessment steps.

[0087] Step 150: Based on the functional relationship information and the total mass of the sediment, the organic carbon pool of the target area is estimated to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

[0088] In actual implementation, the estimation result of the organic carbon pool includes total organic carbon pool information (referred to as total organic carbon pool) and stability organic carbon pool information (referred to as stability organic carbon pool). For the total organic carbon pool, the functional relationship between the total organic carbon pool and the integral area of ​​the characteristic band in the functional relationship information can be used, combined with the total mass of the sediment to estimate the organic carbon pool, so as to obtain the total organic carbon pool in the target area; for the stability organic carbon pool, the functional relationship between the stability organic carbon pool and the integral area of ​​the characteristic band in the functional relationship information can be used, combined with the total mass of the sediment to estimate the organic carbon pool, so as to obtain the stability organic carbon pool in the target area. Therefore, the embodiment of the present application realizes a fast, efficient and low-sample-demand sediment stability organic carbon assessment, and only a certain number of samples need to be collected to assess the organic carbon pool in the entire area.

[0089] It can be seen that the embodiment of the present application uses the target sediment sample collected from the target area to perform infrared spectrum measurement and correction processing to obtain a corrected spectrum, to determine the characteristic band based on the corrected spectrum, and to perform area integration processing on the corrected spectrum based on the characteristic band to obtain the local peak area, and use the local peak area to characterize the integral area of ​​the target sediment sample in each characteristic band, and then perform organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and perform cross-section estimation processing based on the target sediment sample to obtain the total sediment mass of the sediment sample in the target area, and then estimate the organic carbon pool of the target area based on the functional relationship information and the total sediment mass, and obtain the organic carbon pool information of the target area. It can be seen that the embodiments of the present application make full use of the high sensitivity and non-destructive detection characteristics of the infrared spectroscopy analysis method. By using infrared spectroscopy technology combined with the functional relationship between organic carbon and peak area, and the estimation results of the total mass of sediment, the regional sediment organic carbon pool assessment method provided by the present application can be applied to complex scenarios. It can accurately assess the content of stable organic carbon in sediments with only a small amount of sample, thereby providing a new technical means for the assessment of river and lake sediment carbon pools, and solving the problem that existing carbon pool assessment methods cannot accurately assess the organic carbon pool in water system areas.

[0090] Reference Figure 2 , shows a schematic flow chart of the steps of a method for assessing regional sediment organic carbon pools based on infrared spectroscopy analysis provided by an optional embodiment of the present application. The method may specifically include the following steps:

[0091] Step 210 , collecting a target sediment sample from the target area.

[0092] The description of step 210 can refer to the above step 110, and this embodiment will not be described in detail here.

[0093] Step 220: perform infrared spectrum measurement and calibration processing on the target sediment sample to obtain a calibrated spectrum.

[0094] In the specific implementation, taking the Guangdong-Hong Kong-Macao Greater Bay Area as an example, in order to quickly characterize the stable organic carbon pool in the sediments of the water system area, this embodiment uses Fourier transform infrared spectroscopy technology to analyze the stable organic carbon density of representative water system sediments in the Guangdong-Hong Kong-Macao Greater Bay Area, thereby providing technical support for the assessment of water system carbon reserves.

[0095] Exemplarily, 106 sampling points are set up in the Guangdong-Hong Kong-Macao Greater Bay Area. The sampling points are distributed in ecological environments such as rivers, lakes, estuaries, and wetlands. Columnar samples are collected from the corresponding sampling points as target sediment samples. Preferably, 5 columnar samples are collected from each sampling point. After freeze-drying, the columnar samples are measured using a Shimadzu total organic carbon analyzer for total organic carbon, and a Fourier transform infrared spectrometer is used to scan the infrared spectrum of the sediment samples. The Fourier transform infrared spectrometer is used to analyze the infrared range (4000-400cm -1 ) is recorded as transmittance with a resolution of 1.4 cm -1 Each sample was repeated 3 times, and the average value was taken to obtain the corresponding infrared spectrum mean. The infrared spectrum mean was then subjected to spectral preprocessing including correction, which included but was not limited to atmospheric correction for carbon dioxide and water, baseline correction, etc., to compensate for slight changes in air humidity, temperature and carbon dioxide (CO2) concentration during measurement.

[0096] In an optional embodiment, the infrared spectrum measurement and correction processing based on the target sediment sample to obtain the corrected spectrum may specifically include: performing infrared spectrum measurement based on the target sediment sample to obtain the infrared spectrum mean; and performing spectrum correction preprocessing on the infrared spectrum mean according to preset correction information to obtain the corrected spectrum. The correction information includes atmospheric correction and baseline correction.

[0097] Step 230: determining a characteristic band based on the corrected spectrum.

[0098] In actual processing, the infrared spectrum band contains the main functional group information of organic carbon, and the corrected spectrum after correction based on the infrared spectrum actually also contains the main functional group information. This embodiment can use the main functional group information to select a certain number of characteristic bands for subsequent peak area integration.

[0099] In an optional embodiment, the embodiment of the present application determines the characteristic band based on the correction spectrum, which may specifically include: extracting the main functional group information of organic carbon from the correction spectrum; performing band selection based on the main functional group information to obtain the characteristic band.

[0100] For example, the infrared range (4000-400cm -1 ) infrared spectrum, whose infrared spectrum band contains the main functional group information of organic carbon (also called organic functional group). Preferably, 5 characteristic bands are selected from the infrared spectrum band as representatives of a series of organic functional groups (limited mineral interference). The characteristic bands are selected at 3587-2989cm -1 、2989-2817cm -1 1778-1483cm -1 、1483-1343cm-1 、1343-930cm -1 Among them, 3587-2989cm -1 The bands are mainly attributed to the stretching vibrations of aliphatic OH and NH; 2989-2817cm -1 Attributable to aliphatic CH stretching vibration; 1778-1483 cm -1 Attributed to aromatic C=C stretching vibration; 1483-1343 cm -1 Attributed to CH deformation and CH bending vibration of CH2; 1343-930cm -1 It is attributed to the stretching vibration of polysaccharide CO.

[0101] Step 240: determining feature band upper and lower limit information based on the feature band, wherein the feature band upper and lower limit information includes a feature band upper limit and a feature band lower limit.

[0102] Step 250: draw a local baseline based on the upper and lower limit information of the characteristic band in combination with the correction spectrum.

[0103] Step 260, performing peak area integration based on the local baseline to obtain a local peak area.

[0104] A unified description of steps 240 to 260 is given as follows:

[0105] In this embodiment, after determining each characteristic band, the local baseline can be drawn according to the upper and lower limits of the characteristic band, and the corrected peak area can be calculated. Then, the five characteristic bands and the local baseline are integrated, and the peak area corresponding to each characteristic band is divided by the sum of the peak areas of the five characteristic bands (relative peak area), and finally the integrated area of ​​the characteristic band characterizing the sediment sample is obtained as the local peak area.

[0106] For example, refer to Figure 3 , Figure 3 In the figure, (a) is a schematic diagram of the characteristic segment partition, and (b) is a peak area integration using a local baseline for calculating the relative peak area. In the figure (a), the part marked with 1 represents: 3587-2989cm -1 Aliphatic OH and NH stretching vibration; part 2 indicates: 2989-2817cm -1 Aliphatic CH stretching vibration; part 3 indicates: 1778-1483cm -1 Aromatic C=C stretching vibration; 4 indicates: 1483-1343cm -1 CH deformation of CH3 and CH bending vibration of CH2; the part marked with 5 indicates: 1343-930cm -1 Stretching vibration of polysaccharide CO; the part marked with 6 represents: 825-716cm-1 aromatic CH bond out-of-plane bending vibration.

[0107] Step 270: Perform organic carbon relationship mapping based on the local peak area to obtain functional relationship information.

[0108] For the description of step 270 , reference may be made to the above-mentioned step 140 , which will not be described in detail in this embodiment.

[0109] In an optional embodiment, in the embodiment of the present application, when the functional relationship information includes the first functional relationship information and the second functional relationship information, the organic carbon relationship mapping based on the local peak area to obtain the functional relationship information may specifically include: according to OC t =1.2814*(A2+A4 / A5+A3 / A1)+0.71131, determine the first functional relationship information; according to OC s =0.023646*(A3+A1*A2)+0.62995, determine the second functional relationship information; wherein, the first functional relationship information is used to characterize the functional relationship between the total organic carbon in the water body area and the local peak area, and the second functional relationship information is used to characterize the functional relationship between the stable organic carbon in the water body area and the local peak area, and A1, A2, A3, A4 and A5 are all local peak areas corresponding to the characteristic bands.

[0110] Step 280, performing cross-sectional volume detection on the sediments in the target area by combining a preset cross-sectional method with a remote sensing image, and obtaining the total volume of the sediments in the target area.

[0111] In a specific implementation, the total amount of sediment in the area is related to the total volume of sediment. This embodiment combines the cross-section method and the remote sensing image combination method to measure the total volume of sediment in the target area. Specifically, the cross-section method is used to divide the target area into multiple cross sections, and the corresponding volume is calculated for each cross section. Then, the volumes of all cross sections of the target area are combined to obtain the total volume of sediment in the entire area.

[0112] Optionally, the above-mentioned method of combining a preset cross-section method and a remote sensing image to perform cross-sectional volume detection on the sediment in the target area to obtain the total volume of the sediment in the target area may specifically include the following sub-steps:

[0113] Sub-step 2801, obtaining a remote sensing map of the target area and depth information of the sediment to be assessed in the target area.

[0114] Sub-step 2802, according to the preset section spacing, use the remote sensing map to divide the spacing to obtain at least two mutually parallel cross sections.

[0115] Sub-step 2803, calculating the area between the cross sections according to the depth information to obtain the average area of ​​two adjacent cross sections.

[0116] Sub-step 2804, based on the product of the average area and the section spacing, obtain the cross-sectional volume between two adjacent cross-sectional areas in all cross-sectional areas.

[0117] Sub-step 2805, accumulating the cross-sectional volumes to obtain the total volume of sediments in the target area.

[0118] Sub-steps 2801 to 2805 are described uniformly:

[0119] In a specific implementation, for measuring the total volume of sediment in the target area, the present embodiment can realize volume measurement by sectioning the target area, and by obtaining the remote sensing map of the target area and sectioning the remote sensing map, thereby realizing the section of the target area. Specifically, the present embodiment can be divided into sections of the remote sensing map according to a certain section spacing. Taking the river channel as an example, the remote sensing map corresponding to the river channel is divided into several mutually parallel cross sections according to a fixed spacing. Since the section spacing is fixed and preset, each cross section obtained after the section segmentation has a fixed section spacing. Then, a columnar sampler (if a columnar sample is required) or a depth gauge can be used to measure the depth of the sediment to be evaluated, and the average area of ​​two adjacent cross sections is calculated, thereby obtaining the area of ​​each cross section, and the product of the cross section area and the section spacing is the volume between the two sections. The total volume of sediment in the entire area can be obtained by adding up the values ​​of each section area, and the total volume of sediment can be represented by V.

[0120] Step 290, performing layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment, and determining the total mass of the sediment in the target area based on the total volume of the sediment and the comprehensive bulk density of the sediment.

[0121] In a specific implementation, this embodiment can combine the comprehensive bulk density of the sediment and the total volume of the sediment to calculate the sediment weight. The total mass of the sediment is calculated as follows:

[0122] Total mass of sediment M = total volume of sediment V × comprehensive bulk density of sediment ρ

[0123] In this embodiment, according to the above formula, the comprehensive bulk density of the sediment can be reversely deduced according to the mass and volume of the collected target sediment sample, and calculated by combining the mass and volume of the target sediment sample. In order to improve the measurement accuracy, in this embodiment, the collected target sediment sample can be cut into layers, the bulk density corresponding to each layer of sediment sample can be measured respectively, and the bulk density of the multiple layers of sediment samples can be averaged to obtain the comprehensive bulk density.

[0124] Optionally, the above-mentioned layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment may specifically include the following sub-steps:

[0125] Sub-step 2901, performing layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment.

[0126] Sub-step 2902, performing layered cutting and recording based on the target sediment sample, to obtain layered sediment samples, layered volumes corresponding to each layer of the layered sediment samples, and depth ratios between the layered sediment samples.

[0127] Sub-step 2903, pre-processing each layer of the stratified sediment sample to obtain the stratified mass corresponding to the stratified sediment sample.

[0128] Sub-step 2904, determining the stratified bulk density corresponding to the stratified sediment sample based on the stratified volume and the stratified mass.

[0129] Sub-step 2905, calculating the comprehensive bulk density of the sediment based on the depth ratio and the layered bulk density.

[0130] Sub-steps 2901 to 2905 are described uniformly:

[0131] In actual implementation, the comprehensive bulk density of sediment can be obtained by calculating the target sediment sample, in order to improve the measurement accuracy of the total mass of sediment, and then improve the estimation accuracy of the organic carbon pool. In this embodiment, the target sediment sample is cut into layers, thereby obtaining multiple layered sediment samples. Among them, for each layer of sediment sample, the depth can be the same or different. In this embodiment, the depth of each layer of sediment sample can be recorded separately, and the corresponding depth ratio can be obtained by combining the depths of multiple layered sediment samples to calculate the comprehensive bulk density of sediment.

[0132] In a specific implementation, for each layered sediment sample, the present embodiment can measure and record the corresponding volume respectively. By drying and weighing the layered sediment sample, the mass corresponding to the layered sediment sample is obtained, and the corresponding bulk density of the layered sediment sample is calculated by using the volume and mass corresponding to the layered sediment sample in combination with the above formula, and the corresponding bulk density of each layered sediment sample is calculated respectively, and the comprehensive bulk density of the sediment is calculated in combination with the depth ratio corresponding to the layered sediment sample.

[0133] Step 300: Based on the functional relationship information and the total mass of the sediment, the organic carbon pool of the target area is estimated to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

[0134] In this embodiment, the total organic carbon pool estimation formula can be used to estimate the total organic carbon pool in the target area in combination with the functional relationship corresponding to the total organic carbon pool and the total mass of the sediment; the stable organic carbon pool estimation formula can be used to estimate the stable organic carbon pool in the target area in combination with the functional relationship corresponding to the stable organic carbon pool and the total mass of the sediment. Therefore, the embodiment of the present application uses infrared spectroscopy technology to accurately evaluate the content of stable organic carbon in sediments with only a small amount of sample, thereby providing a new technical means for the evaluation of carbon pools in river and lake sediments.

[0135] The estimation formula of organic carbon pool is as follows:

[0136]

[0137] As an example, taking Guangzhou, Zhongshan, Foshan and Zhuhai in the Guangdong-Hong Kong-Macao Greater Bay Area as examples, the stable organic carbon in the water system area is estimated.

[0138] Specific example 1: Surface sediment samples were collected from several rivers and lakes in Guangzhou, and the content of total organic carbon (OCt) and stable organic carbon (OCs) was determined. The infrared spectra of the sediment samples were measured and the characteristic band integrals were taken. The results are as follows:

[0139] As shown in Table 1:

[0140]

[0141]

[0142] Table 1 Measured and estimated stable organic carbon stocks in rivers and lakes in Guangzhou

[0143] Reference Figure 4It can be seen that the OCt predicted by infrared spectroscopy data of river and lake sediments in Guangzhou City has a significant correlation with the measured OCt, and the correlation index r reaches 0.952, which is a good prediction of the sediment carbon pool reserves. The predicted OCs have a certain correlation with the measured OCs, and the correlation index r reaches 0.915, which predicts that the stable carbon pool of sediments is low. Among them, Figure 4 The left figure shows the measured OCt and predicted OCt values. Figure 4 The right figure shows the values ​​of measured OCs and predicted OCs.

[0144] In the second specific example, surface sediment samples were collected from several rivers and lakes in Zhongshan City, the contents of OCt and OCs were measured, and the infrared spectra of the sediment samples were measured. The characteristic band integrals were taken, and the results are shown in Table 2 below:

[0145] Sample Point type Measured OCt Estimation of OCt Measured OCs Estimation of OCs Zhongshan Mangrove Mangroves 2.09 1.57 1.281 0.86 Zhongshan Central Lake 1 lake 3.228 2.32 1.88 1.642 Zhongshan Central Lake 2 lake 1.394 1.149 1.051 0.768 Zhongshan Central Lake 3 lake 2.413 2.306 1.415 1.508 Zhongshan West River Middle Reaches River 1 1.093 0.166 0.76 The upper reaches of the Xijiang River tributary in Zhongshan River 2.801 2.87 1.813 1.639 The middle reaches of the Xijiang River tributary in Zhongshan River 2.674 2.821 1.388 1.612 The lower reaches of the Xijiang River tributary in Zhongshan River 2.583 2.872 1.307 1.585

[0146] Table 2 Measured stable organic carbon stocks and estimated values ​​in Zhongshan rivers and lakes

[0147] Reference Figure 5 It can be seen that the OCt predicted by infrared spectroscopy data in Zhongshan River and Lake Sediments has a certain correlation with the measured OCt, with a correlation index r of 0.834. The predicted OCs has a certain correlation with the measured OCs, with a correlation index r of 0.712. The predicted sediment stability carbon pool is low. Figure 5 The left figure shows the measured OCt and predicted OCt values. Figure 5 The right figure shows the values ​​of measured OCs and predicted OCs.

[0148] Specific example 3: Surface sediment samples were collected from several rivers and lakes in Foshan City, the contents of OCt and OCs were measured, and the infrared spectra of the sediment samples were measured. The characteristic band integrals were taken, and the results are shown in Table 3 below:

[0149]

[0150]

[0151] Table 3 Measured stable organic carbon stocks and estimated values ​​in rivers and lakes in Foshan

[0152] Reference Figure 6 It can be seen that the OCt predicted by infrared spectroscopy data of river and lake sediments in Foshan City has a certain correlation with the measured OCt, and the correlation index r reaches 0.894, which is a good prediction of the sediment carbon pool. The predicted OCs has a certain correlation with the measured OCs, and the correlation index r reaches 0.922. Figure 6 The left figure shows the measured OCt and predicted OCt values. Figure 6 The right figure shows the values ​​of measured OCs and predicted OCs.

[0153] In the fourth specific example, surface sediment samples of wetlands were collected in Guangzhou, the contents of total organic carbon (OCt) and stable organic carbon (OCs) were determined, and the infrared spectra of the sediment samples were measured, and the characteristic band integrals were taken. The results are shown in Table 4 below:

[0154] Sample Point type Measured OCt Estimation of OCt Measured OCs Estimation of OCs Haizhu Wetland Park Wetlands 3.593 3.525 2.091 1.445 Haizhu Wetland Park Phase 2 Wetlands 1.508 1.662 1.051 0.986 Haizhu Wetland Park Phase 3 Wetlands 1.655 1.354 0.869 0.869 Nansha Wetland Wetlands 1.502 0.899 0.947 0.698 Seagull Island Wetlands Wetlands 1.737 1.693 0.729 0.983 Seagull Island Mangrove Wetland Wetlands 1.223 0.947 0.407 0.716

[0155] Table 4 Measured and estimated stable organic carbon stocks in wetland sediments

[0156] Reference Figure 7 It can be seen that the OCt predicted by infrared spectroscopy data of wetland sediments has a significant correlation with the measured OCt, and the correlation index r is 0.956, which is a good prediction of the sediment carbon pool. The predicted OCs has a certain correlation with the measured OCs, and the correlation index r is 0.862. Figure 7 The left figure shows the measured OCt and predicted OCt values. Figure 7 The right figure shows the values ​​of measured OCs and predicted OCs.

[0157] Specific example 5: lake surface sediment samples were collected in Guangzhou, Zhongshan and other cities, the content of total organic carbon (OCt) and stable organic carbon (OCs) were determined, and the infrared spectrum of the sediment samples was measured, and the characteristic band integral was taken. The results are shown in Table 5 below:

[0158]

[0159]

[0160] Table 5 Measured and estimated stable organic carbon stocks in lake sediments

[0161] Reference Figure 8 It can be seen that the OCt predicted by infrared spectroscopy data of lake sediments has a significant correlation with the measured data, and the correlation index r reaches 0.926, which is a good prediction of the sediment carbon pool reserves. The predicted OCs has a certain correlation with the measured OCs, and the correlation index r reaches 0.897. Figure 8 The left figure shows the measured OCt and predicted OCt values. Figure 8 The right figure shows the values ​​of measured OCs and predicted OCs.

[0162] Specific example 6: River surface sediment samples were collected in Zhuhai, Zhongshan, Guangzhou and other cities, and the contents of total organic carbon (OCt) and stable organic carbon (OCs) were determined. The infrared spectra of the sediment samples were measured, and the characteristic band integrals were taken. The results are shown in Table 6 below:

[0163]

[0164]

[0165]

[0166] Table 6 Measured and estimated stable organic carbon stocks in river sediments

[0167] Reference Fig. 9 It can be seen that the OCt predicted by infrared spectroscopy data of river sediments has a certain correlation with the measured OCt, and the correlation index r is 0.884, which can accurately predict the sediment carbon pool reserves. The predicted OCs has a certain correlation with the measured OCs, and the correlation index r is 0.873. Fig. 9 The left figure shows the measured OCt and predicted OCt values. Fig. 9 The right figure shows the values ​​of measured OCs and predicted OCs.

[0168] In summary, the embodiment of the present application uses the target sediment sample collected from the target area to perform infrared spectrum measurement and correction processing to obtain a corrected spectrum, so as to determine the characteristic band based on the corrected spectrum, and determine the upper and lower limit information of the characteristic band based on the characteristic band, draw a local baseline based on the upper and lower limit information of the characteristic band in combination with the corrected spectrum, integrate the peak area according to the local baseline to obtain the local peak area, perform organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and perform cross-sectional volume detection on the sediment in the target area through a preset cross-section method and remote sensing image combination method to obtain the total volume of sediment in the target area, and then perform layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment, and determine the total mass of the sediment in the target area based on the total volume of the sediment and the comprehensive bulk density of the sediment, and estimate the organic carbon pool of the target area based on the functional relationship information and the total mass of the sediment to obtain the total organic carbon pool information and stable organic carbon pool information of the target area. It can be seen that the embodiments of the present application make full use of the high sensitivity and non-destructive detection characteristics of the infrared spectroscopy analysis method, provide new possibilities for the rapid assessment of stable organic carbon in sediments, can accurately assess the content of stable organic carbon in sediments, and effectively reduce the time, cost, sample requirements and complexity required for the assessment of sediment carbon pools in the region. It can be effectively used in large-scale and high-frequency monitoring, and solves the problem that existing carbon pool assessment methods cannot accurately assess the organic carbon pool in water systems.

[0169] It should be noted that, for the purpose of simple description, the method embodiments are expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited to the described order of actions, because according to the embodiments of the present application, certain steps may be performed in other orders or simultaneously.

[0170] like Fig.10 As shown, the embodiment of the present application also provides a regional sediment organic carbon pool assessment device 1000 based on infrared spectroscopy analysis, comprising:

[0171] A collection module 1010 is used to collect target sediment samples from a target area;

[0172] A spectrum measurement and correction module 1020 is used to perform infrared spectrum measurement and correction processing based on the target sediment sample to obtain a corrected spectrum;

[0173] An area integration processing module 1030 is used to determine a characteristic band based on the correction spectrum, and perform area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band;

[0174] A relationship mapping and cross-section estimation processing module 1040 is used to perform organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and to perform cross-section estimation processing based on the target sediment sample and the target area to obtain the total mass of sediment in the target area;

[0175] The estimation module 1050 is used to estimate the organic carbon pool of the target area based on the functional relationship information and the total mass of the sediment, and obtain the total organic carbon pool information and stable organic carbon pool information of the target area.

[0176] Optionally, the acquisition module 1010 includes:

[0177] An evaluation requirement information determination submodule is used to determine evaluation requirement information;

[0178] A first collection submodule is used to generate a grab bucket collection instruction based on the evaluation requirement information, wherein the grab bucket collection instruction is used to control the grab bucket to collect surface sediment samples from the target area and use the surface sediment samples as target sediment samples;

[0179] The second collection submodule is used to generate a columnar collection instruction based on the evaluation requirement information, and the columnar collection instruction is used to control the columnar sampler to collect sediment columns from the target area; pre-process the sediment columns to obtain dry sediment samples, and use the dry sediment samples as target sediment samples.

[0180] Optionally, the spectrum measurement and correction module 1020 includes:

[0181] An infrared spectrum measurement submodule, used for performing infrared spectrum measurement based on the target sediment sample to obtain an infrared spectrum mean;

[0182] The spectrum correction submodule is used to perform spectrum correction preprocessing on the infrared spectrum mean according to preset correction information to obtain a corrected spectrum; wherein the correction information includes atmospheric correction and baseline correction.

[0183] Optionally, the area integration processing module 1030 includes:

[0184] A main functional group information extraction submodule, used to extract the main functional group information of organic carbon from the correction spectrum;

[0185] A band selection submodule, used for performing band selection based on the main functional group information to obtain a characteristic band;

[0186] A feature band upper and lower limit determination submodule, used to determine feature band upper and lower limit information based on the feature band, wherein the feature band upper and lower limit information includes a feature band upper limit and a feature band lower limit;

[0187] A local baseline drawing submodule, used for drawing a local baseline based on the upper and lower limit information of the characteristic band in combination with the corrected spectrum;

[0188] The peak area integration submodule is used to integrate the peak area according to the local baseline to obtain the local peak area.

[0189] Optionally, the functional relationship information includes first functional relationship information and second functional relationship information, and the relationship mapping module 1040 is specifically used to:

[0190] According to OCt=1.2814*(A2+A4 / A5+A3 / A1)+0.71131, the first functional relationship information is determined; according to OCs=0.023646*(A3+A1*A2)+0.62995, the second functional relationship information is determined; wherein, the first functional relationship information is used to characterize the functional relationship between the total organic carbon and the local peak area in the water body area, and the second functional relationship information is used to characterize the functional relationship between the stable organic carbon and the local peak area in the water body area, and A1, A2, A3, A4 and A5 are all local peak areas corresponding to the characteristic bands.

[0191] Optionally, the relationship mapping and cross-section estimation processing module 1040 includes:

[0192] The cross-sectional volume detection submodule is used to perform cross-sectional volume detection on the sediments in the target area by combining a preset cross-sectional method with a remote sensing image to obtain the total volume of sediments in the target area;

[0193] A layered cutting submodule is used to perform layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment;

[0194] The total mass of sediments determining submodule is used to determine the total mass of sediments in the target area according to the total volume of the sediments and the comprehensive bulk density of the sediments.

[0195] Optionally, the cross-sectional volume detection submodule includes:

[0196] An acquisition unit, used to acquire a remote sensing map of the target area and depth information of the sediment to be assessed in the target area;

[0197] A spacing division unit, used to perform spacing division using the remote sensing map according to a preset cross-section spacing to obtain at least two mutually parallel cross sections;

[0198] an area calculation unit, configured to calculate the area between the cross sections according to the depth information to obtain an average area of ​​two adjacent cross sections;

[0199] A product unit, used for performing a product based on the average area combined with the section spacing to obtain a cross-sectional volume between two adjacent cross-sectional areas in all cross-sectional areas;

[0200] The accumulation unit is used to accumulate according to the cross-sectional volume to obtain the total volume of sediment in the target area.

[0201] Optionally, the layered cutting submodule includes:

[0202] A layered cutting recording unit, used for performing layered cutting recording based on the target sediment sample, to obtain a layered sediment sample, a layered volume corresponding to each layer of the layered sediment sample, and a depth ratio between the layered sediment samples;

[0203] A preprocessing unit, used for preprocessing each layer of the stratified sediment sample to obtain a stratified mass corresponding to the stratified sediment sample;

[0204] A layered bulk density determining unit, used for determining the layered bulk density corresponding to the layered sediment sample based on the layered volume and the layered mass;

[0205] A sediment comprehensive bulk density calculation unit is used to calculate the sediment comprehensive bulk density based on the depth ratio and the layered bulk density.

[0206] It should be noted that the regional sediment organic carbon pool assessment device based on infrared spectroscopy analysis provided in the embodiment of the present application can execute the regional sediment organic carbon pool assessment method based on infrared spectroscopy analysis provided in any embodiment of the present application, and has the corresponding functions and beneficial effects of the execution method.

[0207] In a specific implementation, the above-mentioned regional sediment organic carbon pool assessment device based on infrared spectroscopy analysis can be integrated into a device, so that the device can use infrared spectroscopy analysis to assess the organic carbon pool in the region from the collected sediment samples, and as an electronic device, accurately assess the content of stable organic carbon in the sediment. The electronic device can be composed of two or more physical entities, or it can be composed of one physical entity, such as a personal computer (PC), a computer, a server, etc., and the embodiment of the present application does not impose specific restrictions on this.

[0208] like Fig.11 As shown, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114; the memory 113 is used to store computer programs; the processor 111 is used to implement the steps of the regional sediment organic carbon pool assessment method based on infrared spectroscopy analysis provided by any of the aforementioned method embodiments when executing the program stored in the memory 113. Exemplarily, the steps of the regional sediment organic carbon pool assessment method based on infrared spectroscopy analysis may include the following steps: collecting a target sediment sample from a target area; performing infrared spectroscopy measurement and correction processing based on the target sediment sample to obtain a corrected spectrum; determining a characteristic band based on the corrected spectrum, and performing area integration processing on the corrected spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band; performing organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and performing cross-section estimation processing based on the target sediment sample and the target area to obtain the total mass of sediments in the target area; estimating the organic carbon pool of the target area based on the functional relationship information and the total mass of sediments to obtain total organic carbon pool information and stable organic carbon pool information of the target area.

[0209] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method provided in any of the aforementioned method embodiments are implemented.

[0210] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0211] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for assessing regional sediment organic carbon pool based on infrared spectroscopy analysis, characterized in that: include: collecting target sediment samples from the target area; Performing infrared spectrum measurement and correction processing on the target sediment sample to obtain a corrected spectrum; Determine a characteristic band based on the correction spectrum, and perform area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band; Based on the local peak area, organic carbon relationship mapping is performed to obtain functional relationship information, and cross-section estimation processing is performed based on the target sediment sample and the target area to obtain the total mass of sediment in the target area; Based on the functional relationship information and the total mass of the sediment, the organic carbon pool of the target area is estimated to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

2. The method according to claim 1, characterized in that The step of collecting a target sediment sample from a target area comprises: Determine the information needed for the assessment; Based on the evaluation requirement information, a grab bucket collection instruction is generated, wherein the grab bucket collection instruction is used to control the grab bucket to collect surface sediment samples from the target area and use the surface sediment samples as target sediment samples; or, based on the assessment requirement information, generating a columnar sampler collection instruction, the columnar sampler collection instruction being used to control a columnar sampler to collect a sediment column from the target area; The sediment column is pretreated to obtain a dry sediment sample, and the dry sediment sample is used as a target sediment sample.

3. The method according to claim 1, characterized in that: The infrared spectrum measurement and correction processing are performed on the target sediment sample to obtain a correction spectrum, including: Performing infrared spectrum measurement based on the target sediment sample to obtain an infrared spectrum average; According to preset correction information, the infrared spectrum mean is subjected to spectrum correction preprocessing to obtain a corrected spectrum; The correction information includes atmospheric correction and baseline correction.

4. The method according to claim 1, characterized in that: The determining of the characteristic band based on the corrected spectrum comprises: extracting main functional group information of organic carbon from the corrected spectrum; Band selection is performed based on the main functional group information to obtain characteristic bands.

5. The method according to claim 1, characterized in that The performing area integration processing on the corrected spectrum based on the characteristic band to obtain the local peak area includes: Determine characteristic band upper and lower limit information based on the characteristic band, wherein the characteristic band upper and lower limit information includes a characteristic band upper limit and a characteristic band lower limit; Draw a local baseline based on the upper and lower limit information of the characteristic band combined with the correction spectrum; The peak area is integrated according to the local baseline to obtain the local peak area.

6. The method according to claim 1, characterized in that The functional relationship information includes first functional relationship information and second functional relationship information, and the organic carbon relationship mapping is performed based on the local peak area to obtain the functional relationship information, including: According to OC t =1.2814*(A2+A4 / A5+A3 / A1)+0.71131, determine the first functional relationship information; According to OC s =0.023646*(A3+A1*A2)+0.62995, determine the second functional relationship information; Among them, the first functional relationship information is used to characterize the functional relationship between the total organic carbon and the local peak area in the water body area, and the second functional relationship information is used to characterize the functional relationship between the stable organic carbon and the local peak area in the water body area, and A1, A2, A3, A4 and A5 are all local peak areas corresponding to characteristic bands.

7. The method according to claim 1, characterized in that The method of performing cross-section estimation processing based on the target sediment sample and combining the target area to obtain the total mass of sediment in the target area includes: By combining a preset cross-sectional method with a remote sensing image method, the cross-sectional volume of the sediment in the target area is detected to obtain the total volume of the sediment in the target area; Based on the target sediment sample, layered cutting and layered measurement processing are performed to obtain the comprehensive bulk density of the sediment; The total mass of the sediment in the target area is determined based on the total volume of the sediment and the comprehensive bulk density of the sediment.

8. The method according to claim 7, characterized in that The method of combining the preset cross-section method and the remote sensing image method to detect the cross-section volume of the sediment in the target area and obtain the total volume of the sediment in the target area includes: Obtaining a remote sensing map of the target area and depth information of the sediment to be assessed in the target area; According to the preset section spacing, the remote sensing map is used to divide the spacing to obtain at least two mutually parallel cross sections; Calculating the area between the cross sections according to the depth information to obtain an average area of ​​two adjacent cross sections; Based on the product of the average area and the section spacing, a cross-sectional volume between two adjacent cross-sections in all cross-sections is obtained; The total volume of sediments in the target area is obtained by accumulating the cross-sectional volumes.

9. The method according to claim 7, characterized in that: The step of performing layered cutting and layered measurement processing based on the target sediment sample to obtain the comprehensive bulk density of the sediment includes: Performing layered cutting and recording based on the target sediment sample, obtaining layered sediment samples, layered volumes corresponding to each layer of the layered sediment samples, and depth ratios between the layered sediment samples; Preprocessing each layer of the stratified sediment sample to obtain the stratified mass corresponding to the stratified sediment sample; Determining the stratified bulk density corresponding to the stratified sediment sample based on the stratified volume and the stratified mass; The comprehensive bulk density of the sediment is calculated based on the depth ratio and the stratified bulk density.

10. A device for evaluating regional sediment organic carbon pool based on infrared spectroscopy analysis, characterized in that: include: A collection module, used for collecting target sediment samples from a target area; A spectrum measurement and correction module, used for performing infrared spectrum measurement and correction processing according to the target sediment sample to obtain a corrected spectrum; An area integration processing module is used to determine a characteristic band based on the correction spectrum, and perform area integration processing on the correction spectrum based on the characteristic band to obtain a local peak area, wherein the local peak area is used to characterize the integral area of ​​the target sediment sample in the characteristic band; A relationship mapping and cross-section estimation processing module, for performing organic carbon relationship mapping based on the local peak area to obtain functional relationship information, and performing cross-section estimation processing based on the target sediment sample and the target area to obtain the total mass of sediment in the target area; The estimation module is used to estimate the organic carbon pool of the target area based on the functional relationship information and the total mass of the sediment, so as to obtain the total organic carbon pool information and the stable organic carbon pool information of the target area.

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