A method and device for rapid determination of soil organic carbon content and a storage medium

Abstract

The application discloses a kind of soil organic carbon content quick measurement method, device and storage medium, the method includes the following steps: using spectrophotometry to process the organic matter solution of soil sample, the concentration of organic matter in organic matter solution is calculated, and the first organic carbon content of soil sample is calculated according to the concentration of organic matter;The first organic carbon content of soil sample is corrected using a correction model, and the true organic carbon content of the corrected soil sample is obtained.The application can simply and conveniently obtain the concentration of organic matter in the organic matter solution of soil sample by using spectrophotometry, and then the first organic carbon content of soil sample can be directly obtained.The first organic carbon content is corrected using a correction model, and the more accurate true organic carbon content of soil sample can be obtained.

Description

Technical Field

[0001] This invention relates to the field of soil organic carbon content determination technology, specifically to a rapid method, apparatus and storage medium for determining soil organic carbon content. Background Technology

[0002] Carbon neutrality is a means of reducing carbon dioxide emissions. It involves offsetting carbon dioxide emissions through afforestation, energy conservation, and emission reduction, achieving "zero emissions." In the context of actively addressing climate change, carbon trading in forests and grasslands will be one of the policy actions for addressing climate change. my country has the world's largest grassland area, and grassland carbon storage accounts for approximately 34.29% of the total carbon storage in my country's terrestrial ecosystems. Grassland carbon sinks play a vital role in maintaining the Earth's ecological balance, regulating climate, and sustaining the sustainable development of human society. However, carbon sequestration in grassland and forest ecosystems still relies on relatively traditional technologies, especially soil organic carbon measurement, which remains at the laboratory research stage and cannot meet the monitoring needs of large-scale, real-time online measurement.

[0003] Currently, domestic and international methods for detecting soil organic carbon often involve digesting samples followed by titration. However, this method is complex, time-consuming, and requires considerable patience from the laboratory personnel for each titration. Furthermore, errors can easily occur in determining the titration endpoint, requiring skilled technicians and making it unsuitable for large-scale sample testing. The Hill Laboratory abroad uses near-infrared spectroscopy (NIRS) to analyze soil, which has preliminarily replaced traditional wet chemical testing methods. However, this method still requires complex sample preparation and titration processes. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus and storage medium for rapid determination of soil organic carbon content, so as to solve the problems of long time consumption and large error in the determination of soil organic carbon content in the prior art.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] This invention discloses a rapid method for measuring soil organic carbon content, comprising:

[0007] The organic matter solution of the soil sample was treated by spectrophotometry, the concentration of organic matter in the organic matter solution was calculated, and the first organic carbon content of the soil sample was calculated based on the concentration of organic matter.

[0008] The first organic carbon content of the soil sample was corrected using a correction model to obtain the true organic carbon content of the soil sample after correction.

[0009] Furthermore, the correction model was used to correct the primary organic carbon content of the soil samples, including:

[0010] The soil samples were treated by titration to obtain the second organic carbon content of the soil samples.

[0011] The second organic carbon content data was used as sample data to train the first neural network model.

[0012] Historical organic carbon content data was used as sample data to train a second neural network model.

[0013] The first organic carbon content is input into the first neural network model, and the output of the first neural network model is input into the second neural network model to obtain the true organic carbon content of the soil sample.

[0014] Furthermore, prior to the treatment of the organic matter solution of the soil sample using spectrophotometry, the method further includes:

[0015] A certain amount of grassland soil from the field was taken as the soil to be tested. Hydrochloric acid was added to the soil to remove inorganic carbon, and impurities in the soil were filtered out to obtain a soil sample.

[0016] Soil samples are added to a specific solvent to obtain an organic matter solution of the soil sample.

[0017] Furthermore, the organic matter solution of the soil sample was processed using spectrophotometry, and the concentration of organic matter in the organic matter solution was calculated, including:

[0018] Compare the organic matter solution of the soil sample with the control group solution of the undissolved soil sample;

[0019] The concentration of organic matter in the organic matter solution of the soil sample is qualitatively and quantitatively calculated based on the light absorbance of the organic matter solution in a specific wavelength range and the Lambert-Beer law.

[0020] Furthermore, the specific wavelength range is 400-800nm; the expression for the Lambert-Beer law is:

[0021] ;

[0022] Where ε represents the molar absorptivity, A is the absorbance of the organic matter, T is the transmittance, b is the thickness of the absorption layer, and c is the concentration of the absorbing substance.

[0023] Furthermore, before correcting the primary organic carbon content of the soil samples using a modified model, the following steps are also included:

[0024] The first organic carbon content of the obtained soil sample is sent to a signal receiving terminal, which stores a correction model; the signal receiving terminal includes a mobile terminal, a computer terminal, a data center, and analytical instruments.

[0025] Secondly, the present invention discloses a rapid testing device for soil organic carbon content, comprising:

[0026] Soil organic carbon content rapid test module: used to process the organic matter solution of soil samples using spectrophotometry, calculate the concentration of organic matter in the organic matter solution, and calculate the first organic carbon content of the soil sample based on the concentration of organic matter.

[0027] Data processing module: used to correct the first organic carbon content of soil samples using a correction model, so as to obtain the true organic carbon content of soil samples after correction.

[0028] Furthermore, it also includes a wireless communication module, which is used to transmit the first organic carbon content of the obtained soil sample to a signal receiving terminal, wherein the signal receiving terminal stores a correction model; the signal receiving terminal includes a mobile phone terminal, a computer terminal, a data center, and analytical instruments.

[0029] Furthermore, the data processing module is used to input the first organic carbon content into the first neural network model, and input the output of the first neural network model into the second neural network model to obtain the true organic carbon content of the soil sample;

[0030] The first neural network model was trained using the second organic carbon content data as sample data, and the second organic carbon content was obtained by titration of soil samples.

[0031] The second neural network model was trained using historical organic carbon content data as sample data.

[0032] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0033] According to the above technical solution, the embodiments of the present invention have at least the following effects:

[0034] 1. This application can easily and conveniently obtain the concentration of organic matter in the organic matter solution of soil sample by using spectrophotometry, and then directly obtain the first organic carbon content of soil sample. By using a correction model to correct the first organic carbon content, a more accurate organic carbon content of real soil sample can be obtained.

[0035] 2. This application uses spectrophotometry to rapidly obtain the concentration of organic matter in the organic matter solution of soil samples, thereby directly obtaining the first organic carbon content of the soil samples. This effectively avoids the cumbersome processes of high-temperature digestion and titration of soil samples in traditional methods, making it suitable for rapid field determination of soil organic carbon content. Through model correction, errors can be optimized to improve data quality and measurement efficiency. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the rapid measurement system in a specific embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the soil organic carbon content rapid testing module in a specific embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the wind and solar photometry method in a specific embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the wireless communication module in a specific embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of the modified model in a specific embodiment of the present invention;

[0041] Figure 6 This is a flowchart of the rapid testing method of the present invention. Detailed Implementation

[0042] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0043] This patented solution, based on the spectrophotometric method (colorimetric method) for determining the organic matter content in soil sample solutions, uses the "Van Bemmelen" factor and wireless communication module to convert the organic matter content in the soil into organic carbon content, and uploads the experimental data to receiving terminals such as laptops and mobile phones for data analysis, comparison and optimization, thus realizing rapid field detection and online data analysis.

[0044] This innovative solution proposes a rapid soil organic carbon content measuring device. After simple pretreatment of the soil, it is dissolved in a specific solvent, and the soil organic carbon content is quickly determined by spectrophotometry. The measurement results are then uploaded to the receiving end via a wireless communication module for data processing and optimization, thus obtaining highly accurate soil organic carbon content data.

[0045] Example 1

[0046] like Figure 6 The present invention discloses a rapid method for measuring soil organic carbon content, comprising the following steps:

[0047] Step 100: Process the organic matter solution of the soil sample using spectrophotometry, calculate the concentration of organic matter in the organic matter solution, and calculate the first organic carbon content of the soil sample based on the concentration of organic matter.

[0048] Step 200: Use a correction model to correct the first organic carbon content of the soil sample to obtain the true organic carbon content of the soil sample after correction.

[0049] In step 100, the organic matter solution of the soil sample is processed using spectrophotometry to calculate the concentration of organic matter in the soil sample's organic matter solution. Specifically, this can be achieved by comparing the organic matter solution of the soil sample with a control group solution containing undissolved soil sample. The concentration of organic matter in the soil sample's organic matter solution is then qualitatively and quantitatively calculated based on the absorbance of the organic matter solution within a specific wavelength range and the Lambert-Beer law.

[0050] Specifically, within the wavelength range of 400-480 nm, the absorption relationship of organic matter in the solution follows the Lambert-Beer absorption law:

[0051]

[0052] Where ε represents the molar absorptivity, A is the absorbance of the organic matter, T is the transmittance, which is the ratio of the emitted light intensity I to the incident light intensity I0, b is the thickness of the absorption layer, and c is the concentration of the absorbing substance, with the unit being mol / L.

[0053] In step 100, the primary organic carbon content of the soil sample can be directly obtained by calculating the organic matter concentration. Specifically, the organic matter content of the soil sample is converted into the primary organic carbon content using the "Van Bemmelen" factor of 1.724.

[0054] Before step 100, a pretreatment step 90 is included, which specifically includes: taking a quantitative amount of grassland soil from the field as the soil to be tested; adding hydrochloric acid to the soil to remove inorganic carbon from the soil to be tested; filtering the soil to be tested for impurities to obtain a soil sample; and adding the soil sample to a specific solvent to obtain an organic matter solution of the soil sample. The specific solvent can be a lead oxide oxidant or potassium dichromate.

[0055] This application employs spectrophotometry to calculate the primary organic carbon content of soil samples. This method is simple to operate, rapid, requires minimal sample processing, does not require heating to digest the soil, and is low in cost, making it suitable for rapid, field-based detection of soil organic carbon content. Because this method has certain systematic errors, this application includes a correction model to adjust the primary organic carbon content, thereby obtaining a more accurate soil organic carbon content.

[0056] The specific steps for obtaining the modified model are as follows: Step 210: Treat the soil sample using titration to obtain the second organic carbon content of the soil sample. Step 220: Use the second organic carbon content data as sample data to train the first neural network model. Step 230: Use historical organic carbon content data as sample data to train the second neural network model.

[0057] The modified model in this application comprises a two-level neural network model. The first neural network model is trained using sample data obtained from soil samples processed by traditional titration methods, while the second neural network model is trained using historical organic carbon content data. This two-level neural network approach effectively reduces errors caused by instrument measurements and allows for reasonable corrections based on historical data from measurement points. It enhances data selectivity, reduces data processing complexity, and effectively improves the practicality and operability of the device, thus possessing significant market value.

[0058] The establishment of the correction model mainly includes two steps: First, using the measurement values ​​of the traditional laboratory titration method as a benchmark, the values ​​measured by the portable device are optimized in the first stage. The data measured by the portable device is used as the input of the first neural network, and the values ​​measured by the laboratory titration method are used as the target output. The number of hidden layers in the neural network is set, and a large amount of data is used for training to update and correct the weights of the hidden layers, so that the sum of squared errors of the final output of the network layer is minimized. The second step compares the data with the data values ​​of historical measurement points. The establishment and optimization of the network layer is similar to the first step. By comparing and analyzing the historical data of the measurement points through the neural network, the approximate function of the changes over the years is obtained. The data values ​​corrected in the first stage are then passed through the trained neural network structure to obtain the data values ​​corresponding to the historical changes, which is the data corrected in the second stage.

[0059] The data obtained in step 100 can be directly transferred to the correction model. More specifically, the first organic carbon content of the obtained soil sample is sent to a signal receiving terminal, which includes a mobile terminal, a computer terminal, a data center, and analytical instruments. The signal receiving terminal can store the correction model to correct the first organic carbon content.

[0060] Furthermore, the first organic carbon content is input into the first neural network model, and the output of the first neural network model is input into the second neural network model to obtain the organic carbon content of the soil sample.

[0061] The method described in this application can easily and conveniently obtain the concentration of organic matter in the organic matter solution of soil samples using spectrophotometry, thereby directly obtaining the primary organic carbon content of the soil sample. By using a correction model to correct the primary organic carbon content, a more accurate organic carbon content of the soil sample can be obtained. This method effectively avoids the cumbersome processes of high-temperature digestion and titration of soil samples in traditional methods, making it suitable for rapid field determination of soil organic carbon content and efficient data analysis in the background. Furthermore, it can optimize measurement errors through analysis, improving data quality and measurement efficiency.

[0062] Example 2

[0063] like Figures 1 to 5 As shown, a rapid testing device for soil organic carbon content is disclosed, which includes a rapid testing module for soil organic carbon content, a wireless communication module, and a data processing module.

[0064] like Figure 1 As shown, the soil organic carbon content rapid measurement module is used to process the organic matter solution of a soil sample using spectrophotometry, calculate the concentration of organic matter in the organic matter solution, and calculate the first organic carbon content of the soil sample based on the concentration of organic matter. The soil organic carbon content rapid measurement module connects to a wireless transmission module via a serial port to transmit the obtained first organic carbon content data to a receiving terminal. The receiving terminal includes a data processing module or communicates with a device having a data processing module. The data processing module is used to correct the first organic carbon content of the soil sample using a correction model to obtain the corrected organic carbon content of the soil sample.

[0065] The soil organic carbon content rapid measurement module of this application adopts a spectrophotometric method. The organic matter in the soil is dissolved in a specific solvent and compared with the experimental group solution without dissolved soil. The concentration of organic matter in the solution is qualitatively and quantitatively calculated based on the light absorbance of the soil solution to a specific wavelength range and the Lambert-Beer law. Finally, the organic matter content is converted into organic carbon content using the Van Bemmelen factor of 1.724.

[0066] The specific process is as follows: A small amount of grassland soil sample from the field was divided into two equal parts. Hydrochloric acid was added to remove inorganic carbon. Then, one part of the filtered soil was added to a specific solvent, while the other part was left untreated. Within a specific wavelength range (400-480 nm), the absorption relationship of organic matter in the solution follows the Lambert-Beer absorption law:

[0067] ;

[0068] Where ε represents the molar absorptivity, A is the absorbance of the organic matter, T is the transmittance, which is the ratio of the emitted light intensity I to the incident light intensity I0, b is the thickness of the absorption layer, and c is the concentration of the absorbing substance, with the unit being mol / L.

[0069] like Figure 3 The diagram illustrates the spectrophotometric determination of organic matter concentration in a soil sample. A beam of light with a fixed intensity is emitted from a selectable light source. This beam passes through a protective plate, a slit, a grating, and color filters to remove stray light and interference from other wavelengths before entering the sample cell. Finally, the detector receives the light and calculates the light intensity. Based on the change in light intensity, the transmittance of the sample solution can be calculated. Since the absorbance and molar absorptivity of organic carbon are constants, the organic matter concentration in the sample cell can be deduced. Wherein, B / D / W / : light source; G: grating; N: receiver; M1: condenser lens; M2 / M3: protective plate; T1 / T2 lens; F1-F5: color filters; S1 / S2: slit; Y: sample cell.

[0070] Spectrophotometry has the advantages of being simple to operate, fast, easy to process samples, does not require heating to digest the soil, and is low in cost, making it suitable for rapid, field detection of soil organic carbon content.

[0071] In a further embodiment, the soil organic content rapid testing module includes a display module that can be controlled by a microcontroller to display data. The soil sample is processed using spectrophotometry to calculate and display the first organic carbon content of the soil sample. The display module is connected to a wireless transmission module via a serial port to transmit the first organic carbon content data. Furthermore, the display module can also receive data transmitted by the wireless transmission module to display the true organic carbon content of the soil sample after correction by a correction model.

[0072] In some further embodiments, in the soil organic carbon content rapid measurement module, the measurement results are displayed on a small LCD screen controlled by a microcontroller. The wireless communication module, written in C language, connects to the microcontroller's serial port controlling the LCD screen via different serial ports, reads the measurement data, and stores it in the wireless communication module. The wireless communication module integrates the data to generate transmittable data for transmission and reception. The generated data is modulated to the signal wireless transmission module as required and transmitted in real time. After transmission, the transmitted signal is received and demodulated by the receiving terminal to recover the transmitted data. The obtained data is entered into a mobile device via a serial port acquisition device. Simultaneously, for simplified office work and rapid processing, the information can be transmitted via a wireless network accessing the operator's network through the mobile terminal. The received signal is transmitted through a PON transmission tower, routed and addressed, then distributed by the OLT (Optical Line Terminal), received and distributed by nearby ONUs (Optical Network Units), and finally delivered to various signal receiving terminals, such as mobile terminals, computer terminals, data centers, analytical instruments, etc. This data will be used for subsequent data analysis and comparison.

[0073] During the process of measuring organic carbon content using the rapid testing module, due to the influence of factors such as the degree of sedimentation of the soil solution after solvent treatment, extreme environments, special climates, and power supply stability, there will be some error between the measured value and the true value. Within a certain range, the error can be tolerated and can also be compensated for through comparison.

[0074] In some embodiments, the data processing module can compensate for errors. Furthermore, soil samples collected from sampling points are processed using conventional titration methods, and the resulting data can be compared with experimental data from the soil organic carbon content rapid measurement module to reduce discrepancies.

[0075] Specifically, at this stage, a correction model can be used to correct the measured values ​​of the device. In this application, the correction model is a neural network model. An artificial neural network model is a mathematical model that simulates the structure of the human brain's neural network. It possesses adaptive, self-learning, and associative memory functions, enabling function simulation and complex logical transformations. It can update the weights of the infinitely differentiable recursive functions of the hidden layers, achieving information feedback. Therefore, a fairly approximate function can be obtained through the backpropagation system using the input vector and the corresponding output vector.

[0076] During the optimization process, a two-step comparison approach can be used.

[0077] First, the measured values ​​obtained using traditional methods such as laboratory titration are optimized. With data from a portable device as input and experimental titration data as output, the input information undergoes certain processing before reaching the input layer. Through step-by-step calculations in the hidden layers, an output result is obtained at the output layer. During the reverse transmission process, the output end compares the calculated network output value with the ideal value, calculates the corresponding error value, and transmits this error value backward to update the weights of each layer. By continuously correcting the actual network output and reducing the error between the actual network output and the corresponding ideal output, the sum of squared errors of the network layer outputs is minimized, thus obtaining the data value corrected in the first stage.

[0078] The second step involves comparing the obtained data with historical data trends at the measurement points to optimize the historical values. Comparative analysis of historical data from the same measurement point reveals that, barring major natural disasters or extreme weather events, the year-to-year changes in historical data are relatively stable. Based on this, a neural network-based comparative analysis of the historical data at the measurement points can be used to derive the approximate function of the annual changes. The data values ​​corrected in the first stage are then passed through a trained neural network structure to obtain data values ​​corresponding to the historical changes—this is the data corrected in the second stage.

[0079] This multi-correction data can effectively reduce errors caused by instrument measurement, and can also make reasonable corrections based on the historical situation of measurement points, thereby improving the selectivity of data, reducing the complexity of data processing, effectively improving the practicality and operability of the device, and having good market value.

[0080] Example 3

[0081] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A rapid method for determining soil organic carbon content, characterized in that, include: The organic matter solution of the soil sample was treated using spectrophotometry to calculate the concentration of organic matter in the solution, including: Compare the organic matter solution of the soil sample with the control group solution of the undissolved soil sample; The concentration of organic matter in the organic matter solution of the soil sample was qualitatively and quantitatively calculated based on the light absorbance of the organic matter solution in a specific wavelength range and the Lambert-Beer law. The specific wavelength range is 400-800nm; the expression for the Lambert-Beer Law is: ； Where ε represents the molar absorptivity, A is the absorbance of the organic matter, T is the transmittance, b is the thickness of the absorption layer, and c is the concentration of the absorbing substance. The first organic carbon content of the soil sample was calculated based on the concentration of organic matter. The primary organic carbon content of soil samples was corrected using a correction model to obtain the corrected true organic carbon content of the soil samples, including: The soil samples were treated by titration to obtain the second organic carbon content of the soil samples. The second organic carbon content data was used as sample data to train the first neural network model. Historical organic carbon content data was used as sample data to train a second neural network model. The first organic carbon content is input into the first neural network model, and the output of the first neural network model is input into the second neural network model to obtain the true organic carbon content of the soil sample.

2. The method for rapid determination of soil organic carbon content according to claim 1, characterized in that, The process further includes, prior to the spectrophotometric treatment of the organic matter solution in the soil sample: A certain amount of grassland soil from the field was taken as the soil to be tested. Hydrochloric acid was added to the soil to remove inorganic carbon, and impurities in the soil were filtered out to obtain a soil sample. Soil samples are added to a specific solvent to obtain an organic matter solution of the soil sample.

3. The method for rapid determination of soil organic carbon content according to claim 1, characterized in that, Before using the modified model to correct for the first organic carbon content of the soil samples, the following steps are also included: The first organic carbon content of the obtained soil sample is sent to a signal receiving terminal, which stores a correction model; the signal receiving terminal includes a mobile terminal, a computer terminal, a data center, and analytical instruments.

4. A rapid testing device for soil organic carbon content, characterized in that, include: Soil organic carbon content rapid test module: used to process the organic matter solution of soil samples using spectrophotometry, calculate the concentration of organic matter in the organic matter solution, and calculate the first organic carbon content of the soil sample based on the concentration of organic matter. The organic matter solution of the soil sample was treated using spectrophotometry, and the concentration of organic matter in the solution was calculated, including: Compare the organic matter solution of the soil sample with the control group solution of the undissolved soil sample; The concentration of organic matter in the organic matter solution of the soil sample was qualitatively and quantitatively calculated based on the light absorbance of the organic matter solution in a specific wavelength range and the Lambert-Beer law. The specific wavelength range is 400-800nm; the expression for the Lambert-Beer Law is: ； Where ε represents the molar absorptivity, A is the absorbance of the organic matter, T is the transmittance, b is the thickness of the absorption layer, and c is the concentration of the absorbing substance. Data processing module: used to correct the first organic carbon content of soil samples using a correction model, and obtain the true organic carbon content of soil samples after correction; The data processing module is used to input the first organic carbon content into the first neural network model, and input the output of the first neural network model into the second neural network model to obtain the true organic carbon content of the soil sample. The first neural network model was trained using the second organic carbon content data as sample data, and the second organic carbon content was obtained by titration of soil samples. The second neural network model was trained using historical organic carbon content data as sample data.

5. The rapid soil organic carbon content testing device according to claim 4, characterized in that, It also includes a wireless communication module, which is used to transmit the first organic carbon content of the obtained soil sample to a signal receiving terminal, wherein the signal receiving terminal stores a correction model; the signal receiving terminal includes a mobile phone terminal, a computer terminal, a data center, and analytical instruments.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 3.

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