Method for regulating and controlling soil carbon pool balance in corn planting area based on internet of things

Abstract

The present application belongs to the technical field of soil carbon pool balance regulation, and particularly relates to a soil carbon pool balance regulation method for a corn planting area based on the Internet of Things. 3+ After interference, carbon pool monitoring data is obtained; net carbon pool income is calculated to determine a carbon surplus, deficit or balance state; a targeted regulation strategy is formulated in combination with the corn growth period; regulation is performed and closed-loop feedback correction is performed until the carbon pool is stable and balanced. The present application realizes real-time and accurate monitoring of carbon pool parameters and intelligent regulation of the whole process, solves the problems of large errors and blind regulation in traditional management, improves the efficiency and stability of carbon pool balance regulation, and balances high corn yield and sustainable development of farmland ecology, and is suitable for large-scale corn planting areas.

Description

Technical Field

[0001] This invention belongs to the field of soil carbon pool balance regulation technology, and particularly relates to a method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things. Background Technology

[0002] The soil carbon pool is the largest carbon pool in terrestrial ecosystems, and its balance directly affects the stability of farmland ecosystems, soil fertility, and climate change response. As one of my country's major food crops, the dynamic changes in the soil carbon pool in maize planting areas are closely related to maize yield and the farmland ecological environment. A surplus in the soil carbon pool leads to excessive soil carbon sequestration, affecting soil aeration and nutrient conversion efficiency, thereby inhibiting maize root growth. Conversely, a deficit in the soil carbon pool leads to decreased soil fertility and soil degradation, reducing maize's photosynthetic carbon sequestration capacity and yield, while simultaneously increasing atmospheric CO2 emissions and exacerbating climate change. Therefore, achieving precise balance regulation of the soil carbon pool in maize planting areas is crucial for ensuring high and stable maize yields and promoting sustainable farmland ecological development.

[0003] Currently, soil carbon pool regulation in maize-growing areas mainly relies on traditional experience-based management, which has the following prominent problems:

[0004] First, the methods for monitoring soil carbon pool parameters are outdated, mostly relying on manual sampling and laboratory testing. This makes it impossible to collect multi-dimensional parameters in real time, resulting in long monitoring cycles, large errors, and difficulty in capturing dynamic changes in the soil carbon pool. Furthermore, it is impossible to simultaneously acquire key parameters such as soil organic carbon, inorganic carbon, and carbon flux, leading to inaccurate determination of the carbon pool status.

[0005] Second, the determination of carbon pool balance lacks quantitative basis and relies heavily on experience. It is impossible to obtain the net income and expenditure of carbon pool through precise mathematical calculations, making it difficult to accurately distinguish between carbon surplus, carbon deficit and balance.

[0006] Third, the regulation strategies lack specificity and a closed-loop feedback mechanism. The regulation plans were not formulated in combination with the differences in maize growth period and the real-time status of the carbon pool. Furthermore, the effects of regulation could not be tracked in real time and the strategies could not be dynamically adjusted, resulting in low regulation efficiency and unstable effects.

[0007] In addition, although some existing technologies have attempted to use the Internet of Things for soil monitoring, they are mostly limited to monitoring a single parameter and have not formed a multi-dimensional, full-process carbon pool balance regulation system. Furthermore, there are significant shortcomings in the accuracy of data preprocessing, the scientific nature of carbon pool net income and expenditure calculation, and the targeting of regulation strategies, which cannot effectively meet the actual needs of soil carbon pool balance regulation in maize planting areas.

[0008] Therefore, this invention aims to provide a precise, efficient, and closed-loop controllable method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things, in order to solve the technical problems existing in the prior art. Summary of the Invention

[0009] The purpose of this invention is to provide an Internet of Things-based method for regulating soil carbon pool balance in maize planting areas, in order to solve the technical problems existing in the prior art, such as outdated soil carbon pool parameter monitoring methods, long monitoring cycles and large errors, difficulty in capturing dynamic changes in soil carbon pool, lack of quantitative basis for carbon pool balance determination, and lack of specificity and closed-loop feedback mechanism in regulation strategies.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0011] A method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things includes the following steps:

[0012] S1: Construct an Internet of Things (IoT) monitoring system for soil carbon pools in maize planting areas. The monitoring system includes distributed monitoring nodes, LoRaWAN communication gateways, edge computing nodes, and a cloud-based control platform.

[0013] S2: Based on the IoT monitoring system, real-time collection and preprocessing of multi-dimensional parameters of soil carbon pool in maize planting area are carried out. The collected data includes soil organic carbon content, soil inorganic carbon content, soil carbon flux, soil temperature and humidity, soil microbial community structure and maize photosynthetic carbon fixation efficiency data. After preprocessing, carbon pool monitoring data are obtained.

[0014] S3: Determine the soil carbon pool balance status in maize planting areas based on carbon pool monitoring data: The cloud-based control platform performs calculations on standardized carbon pool monitoring data to calculate the net income and expenditure of the soil carbon pool. The net income and expenditure of the soil carbon pool is calculated based on the comprehensive calculation of soil organic carbon content, soil inorganic carbon content, soil carbon flux, maize photosynthetic carbon fixation efficiency, and soil microbial mineralization.

[0015] S4: Based on the soil carbon pool balance status in maize planting areas and combined with the division of maize growth stages, formulate targeted soil carbon pool balance regulation strategies.

[0016] S5: Based on the soil carbon pool balance regulation strategy, the regulation operation is performed through the Internet of Things system and closed-loop feedback correction is carried out until the soil carbon pool reaches a balanced state and is maintained stably.

[0017] Preferably, the specific process for calculating the net income and expenditure of the soil carbon pool in step S3, based on the standardized carbon pool monitoring data, is as follows:

[0018] S31: Calculate total carbon input C in This includes maize photosynthetic carbon sequestration input, soil organic carbon accumulation input, and soil inorganic carbon accumulation input. The specific calculation formulas are as follows:

[0019] C in=C photo +C socin +C sicin ;

[0020] in, C photo Input for photosynthetic carbon fixation in maize; C socin Input for soil organic carbon accumulation; C sicin Input for the accumulation of inorganic carbon in the soil;

[0021] S32: Calculate total carbon output C out This includes soil carbon flux emissions and soil microbial mineralization loss, and the specific calculation formula is as follows:

[0022] C out =F soil +C min ;

[0023] in, F soil For soil carbon flux emissions;

[0024] C min The calculation formula for soil microbial mineralization loss is as follows:

[0025] C min =SOC × k × t ;

[0026] Where: SOC is the soil organic carbon content; k For soil microbial mineralization rate; t For monitoring cycle;

[0027] S33: Calculate the net income and expenditure of the soil carbon pool:

[0028] Δ C =( C in -C out ) / ( C in +C out )×100%;

[0029] Among them, molecules C in -Cout The absolute net income and expenditure, denominator C in +C out This represents the total carbon turnover.

[0030] Preferably, the corn photosynthetic carbon fixation input in step S31 C photo The calculation formula is as follows:

[0031] C photo = P × f ×0.5;

[0032] in: P To improve the photosynthetic carbon fixation efficiency of corn; f 0.5 is the distribution coefficient of photosynthetic carbon to soil; 0.5 is the mass conversion factor between CO2 and C.

[0033] Soil organic carbon accumulation input C socin The calculation formula is as follows:

[0034] C socin = SOC × D × ρ × k 1;

[0035] Where: SOC is the soil organic carbon content; D is the soil sampling depth; ρ Soil bulk density; k 1 represents the organic carbon accumulation coefficient;

[0036] Accumulated input of soil inorganic carbon C sicin The calculation formula is as follows:

[0037] C sicin = SIC × D × ρ × k 2;

[0038] in: SIC D represents the soil inorganic carbon content; D represents the soil sampling depth. ρ Soil bulk density; k 2 represents the inorganic carbon accumulation coefficient.

[0039] Preferably, the process for determining the soil carbon pool balance in the maize planting area in step S3 is as follows:

[0040] When the net income and expenditure of the carbon pool is within ±5%, the soil carbon pool is considered to be in equilibrium.

[0041] When the net income and expenditure of the carbon pool is greater than 5%, the soil carbon pool is considered to be in a carbon surplus state.

[0042] When the net income and expenditure of the carbon pool is less than -5%, the soil carbon pool is considered to be in a carbon deficit state.

[0043] Preferably, the specific process of step S4 is as follows:

[0044] S41: When the judgment result is a carbon surplus state, the regulation strategy is to reduce the corn planting density to 3800-4200 plants / mu, reduce the biochar application rate to 50-80 kg / mu, adopt conventional farming mode, and at the same time maintain the soil moisture content at 60%-65% of field capacity through the IoT-controlled irrigation system;

[0045] S42: When the judgment result is a carbon deficit state, the control strategy is as follows: increase the biochar application rate to 100-150 kg / mu, combine with the application of microbial carbon fixation agents, adopt the no-till combined with straw return mode, control the straw return rate to 200-300 kg / mu, control the irrigation system through the Internet of Things to maintain the soil moisture content at 65%-70% of the field water holding capacity, and at the same time control the corn planting density to 4500-5000 plants / mu.

[0046] S43: When the determination result is a balanced state, the control strategy is to maintain the current corn planting density, tillage mode and water and fertilizer management parameters, and continuously track the changes in carbon pool parameters through the Internet of Things monitoring system to ensure the stability of the carbon pool balance state.

[0047] Preferably, the specific process for preprocessing the multi-dimensional raw data in step S2 is as follows:

[0048] S21: Remove outlier data;

[0049] S22: Correction Fe 3+ Interference with carbon spectral lines.

[0050] Preferably, the specific process of step S21 is as follows:

[0051] S211: Set the initial state values ​​for each piece of raw data, denoted as... x 0, initial covariance P0;

[0052] S212: Perform iterative calculations: edge computing nodes process the raw data at each time step. z k Execute in sequence:

[0053] Through state prediction equations x k - =A xk-1 + Bu k-1 Combined with the effective data filtered from the previous time step x k-1 Predict the state value at the current moment. x k - Where A=1, B=0, u k-1 =0, simplified to x k - = x k-1 ;

[0054] Prediction equation based on covariance P k - = AP k-1 A T + Q Calculate the uncertainty of the predicted value at the current time. P k - Where A=1, simplified to P k - = P k-1 +Q;

[0055] Through the Kalman gain equation K k = P k - H T ( HP k - H T + R ) -1 Determine the weight allocation between predicted and actual observed values. K k Where H=1, simplified to K k = P k - ( P k - + R ) -1 ;

[0056] Through the state update equation x k = xk - + K k ( z k -H x k - ), combined with raw monitoring data z k Correct the predicted values ​​to obtain the filtered effective data at the current time. x k Where H=1, it simplifies to:

[0057] x k = x k - + K k ( z k - x k - );

[0058] Update equation using covariance P k =( IK k H ) P k - Update the covariance at the current moment. P k - Where I=1 and H=1, simplified to P k =( IK k ) P k - Complete a single iteration;

[0059] S213: Anomaly Detection: Predicted values ​​obtained from iterative calculations x k - Compared with the original data z k Calculate the absolute value of the difference between the two. z k - x k - |, when the difference| z k - x k - |>3×R 1 / 2Determine the original data z k For outliers, the state update value is obtained directly from the state update equation. x k Replace; if the difference is ≤0.095, retain the original data. z k and use it as the next iteration. x k-1 Synchronously update covariance P k ;

[0060] S214: Output result: The above initialization, iterative calculation and anomaly detection process are completed sequentially for the raw data at all times. After the iterative processing is completed, the effective data set after removing outliers is obtained.

[0061] Preferably, the specific process of step S22 is as follows:

[0062] S221: Obtain the original soil carbon content data after removing outliers, denoted as... c C,raw Simultaneously, soil Fe was obtained from synchronous detection using the LIBS probe. 3+ Content, denoted as c Fe ;

[0063] S222: Calculation of Fe 3+ Interference intensity of carbon spectral lines I Fe The calculation formula is as follows:

[0064] I Fe = k × c Fe × I C0 ;

[0065] in, I Fe For Fe 3+ The intensity of interference with carbon spectral lines; k For Fe 3+ The interference coefficient, commonly used in LIBS analysis of soil carbon content, is 0.085, representing the Fe... 3+ The degree of overlap with carbon spectral lines; I C0 Fe-free 3+ The intensity of characteristic spectral lines of carbon under interference, in counts, taking the industry standard value of 1200 counts, corresponding to the spectral line intensity of pure carbon samples; c Fe Soil Fe synchronous detection for LIBS probe3+ content;

[0066] S223: Perform carbon content correction: Correct the original soil carbon content data to obtain the corrected Fe content. 3+ Soil carbon content after disturbance c C,corr The calculation formula is as follows:

[0067] c C,corr = c C,raw - I Fe / I C,std × c C,std ;

[0068] in, c C,raw Raw data of soil carbon content collected by the LIBS probe. I C,std The characteristic spectral line intensities of the standard carbon sample. c C,std This refers to the content of the standard carbon sample.

[0069] Preferably, the distributed monitoring nodes in step S1 are deployed in the corn planting area at a grid density of 5m×5m. Each monitoring node integrates a laser-induced breakdown spectroscopy (LIBS) probe, a soil carbon flux monitor, a soil temperature and humidity sensor, a soil microbial sensor, and a multispectral acquisition module.

[0070] Preferably, the specific process of step S5 includes: the cloud control platform parses the soil carbon pool balance control strategy into specific execution instructions, and sends them to the IoT execution devices in the planting area through the LoRaWAN communication gateway. The IoT execution devices include smart seeders, smart fertilizer applicators, smart irrigation equipment and smart tillage equipment. Each execution device completes the planting density adjustment, fertilization, irrigation and tillage operations according to the instructions.

[0071] Meanwhile, the IoT monitoring system continuously collects multi-dimensional parameters of the soil carbon pool after regulation, repeats the processing steps S2-S4, and obtains a new carbon pool equilibrium state determination result. If the new determination result still does not reach the equilibrium state, the parameters of the regulation strategy are adjusted according to the deviation value, and the execution command is issued again until the soil carbon pool reaches the equilibrium state and is maintained stably.

[0072] The beneficial effects of this invention include:

[0073] 1. A full-process IoT monitoring system was constructed to achieve real-time and accurate acquisition of multi-dimensional parameters of the soil carbon pool, solving the problems of long cycle, large error and single parameter in traditional monitoring methods: By deploying distributed monitoring nodes with a grid density of 5m×5m, and integrating multiple sensors such as LIBS probes and carbon flux monitors, key parameters such as soil organic carbon, inorganic carbon, carbon flux, temperature and humidity, microbial community structure and maize photosynthetic carbon fixation efficiency can be collected simultaneously. Combined with the synergistic effect of LoRaWAN communication gateway, edge computing nodes and cloud control platform, real-time data transmission, storage and operation are realized, providing reliable data support for carbon pool balance determination and control, and improving monitoring accuracy compared with traditional methods.

[0074] 2. It provides a scientific and standardized data preprocessing process, which significantly improves the accuracy of monitoring data: It accurately removes abnormal data by using the Kalman filter algorithm and corrects carbon spectral line interference by combining the Fe³⁺ interference correction formula. It clarifies the specific processes of initialization, iterative calculation, anomaly judgment, interference calculation and correction, which improves the data accuracy to over 92%. It effectively solves the problems of non-standard data preprocessing and large deviations in existing technologies, and provides a guarantee for the accuracy of subsequent calculation of carbon pool net income and expenditure.

[0075] 3. A quantitative method for determining carbon pool balance has been established to accurately distinguish carbon pool status: Through a clear calculation process for carbon pool net income and expenditure, the total carbon input, total carbon output and net income and expenditure are calculated step by step. At the same time, the criteria for determining carbon surplus, carbon deficit and balance status are clarified, which solves the defects of traditional methods that rely on experience and lack quantitative basis, and improves the accuracy of carbon pool status determination.

[0076] 4. Targeted and differentiated control strategies were developed, enhancing the effectiveness of carbon pool balance regulation. Based on the differences in maize growth stages and carbon pool balance status, specific control plans were formulated for three states: carbon surplus, carbon deficit, and balance. Specific control indicators such as planting density, biochar application rate, tillage patterns, and irrigation parameters were clearly defined, achieving "on-demand and precise control." This effectively solved the problems of insufficient targeting and poor results of traditional control strategies, improving the soil carbon pool balance maintenance rate while simultaneously balancing maize yield increase and ecological protection.

[0077] 5. Achieved closed-loop feedback for carbon pool balance regulation, ensuring stable and lasting regulation effects: The IoT-enabled devices precisely execute regulation commands while continuously collecting carbon pool parameters after regulation. The process of preprocessing, judgment, and regulation is repeated, and the regulation parameters are dynamically corrected, forming a closed-loop system of monitoring-preprocessing-judgment-regulation-feedback-correction. This solves the problems of existing regulation methods lacking feedback mechanisms and failing to achieve sustainable regulation effects, and reduces the cost of manual intervention. Attached Figure Description

[0078] Figure 1 This is a schematic flowchart of the Internet of Things-based soil carbon pool balance regulation method for maize planting areas according to the present invention.

[0079] Figure 2 This is a schematic diagram of the process for calculating the net income and expenditure of the soil carbon pool according to the present invention. Detailed Implementation

[0080] The following is in conjunction with the appendix Figures 1-2 The present invention will be further described in detail below:

[0081] Example 1

[0082] See appendix Figure 1 As shown, the method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things includes the following steps:

[0083] S1: Construct an Internet of Things (IoT) monitoring system for soil carbon pools in maize planting areas. The monitoring system includes distributed monitoring nodes, LoRaWAN communication gateways, edge computing nodes, and a cloud-based control platform.

[0084] The distributed monitoring nodes are deployed in the corn planting area at a grid density of 5m×5m. Each monitoring node integrates a laser-induced breakdown spectroscopy (LIBS) probe, a soil carbon flux monitor, a soil temperature and humidity sensor, a soil microbial sensor, and a multispectral acquisition module. The LIBS probe penetrates the soil to a depth of 30cm. The soil carbon flux monitor uses NDIR technology to detect the CO2 concentration gradient on the soil surface at a frequency of 10Hz. All monitoring nodes establish communication connections with the edge computing nodes through a LoRaWAN communication gateway. The edge computing nodes execute the Kalman filter algorithm to compress the data volume. The cloud control platform communicates bidirectionally with the edge computing nodes.

[0085] S2: Based on the aforementioned IoT monitoring system, real-time acquisition and preprocessing of multi-dimensional parameters of the soil carbon pool in maize planting areas are carried out. Specifically, this includes: synchronously acquiring soil organic carbon content, soil inorganic carbon content, soil carbon flux, soil temperature and humidity, soil microbial community structure, and maize photosynthetic carbon fixation efficiency data through various distributed monitoring nodes. The soil microbial community structure is obtained through 16S rRNA high-throughput sequencing technology, and the maize photosynthetic carbon fixation efficiency is obtained by inverting the NDRE index through a multispectral acquisition module. The acquired multi-dimensional raw data is transmitted to edge computing nodes, and the multi-dimensional raw data is preprocessed to obtain carbon pool monitoring data.

[0086] S3: Based on carbon pool monitoring data, determine the soil carbon pool balance status in the maize planting area. Specifically, the cloud-based control platform performs calculations on standardized carbon pool monitoring data to calculate the net income and expenditure of the soil carbon pool. The net income and expenditure of the soil carbon pool is calculated based on the soil organic carbon content, soil inorganic carbon content, soil carbon flux, maize photosynthetic carbon fixation efficiency, and soil microbial mineralization.

[0087] S4: Based on the carbon pool balance status determination results output in step S3, and combined with the division of maize growth stages, formulate targeted soil carbon pool balance regulation strategies.

[0088] S5: Based on the control strategy formulated in step S4, the control operation is executed through the Internet of Things (IoT) system and closed-loop feedback correction is performed. Specifically, the cloud control platform parses the control strategy into specific execution instructions and sends them to the IoT execution devices in the planting area through the LoRaWAN communication gateway. The IoT execution devices include smart seeders, smart fertilizer applicators, smart irrigation equipment, and smart tillage equipment. Each execution device completes the planting density adjustment, fertilization, irrigation, and tillage operations according to the instructions. At the same time, the IoT monitoring system continuously collects multi-dimensional parameters of the soil carbon pool after control and repeats the processing steps S2-S4 to obtain a new carbon pool balance state determination result. If the new determination result still does not reach the balance state, the parameters of the control strategy are adjusted according to the deviation value and executed until the soil carbon pool reaches the balance state and is maintained stably.

[0089] Example 2

[0090] Based on Example 1, the specific process of preprocessing the multi-dimensional raw data in step S2 is as follows:

[0091] S21: Remove outlier data;

[0092] S211: Set the initial state values ​​for each piece of raw data, denoted as... x 0. Take the average of the first 5 raw data points to avoid initial bias. The initial covariance P0 is 1×10. -3 ;

[0093] S212: Perform iterative calculations: edge computing nodes process the raw data at each time step. z k Execute in sequence:

[0094] First, through the state prediction equation x k - =A x k-1 + Bu k-1 Combined with the effective data filtered from the previous time step x k-1Predict the state value at the current moment. x k - Where A=1, B=0, u k-1 =0, simplified to x k - = x k-1 ;

[0095] Then, through the covariance prediction equation P k - = AP k-1 A T + Q Calculate the uncertainty of the predicted value at the current time. P k - Where A=1, simplified to P k - = P k-1 +Q;

[0096] Then, the Kalman gain equation was used. K k = P k - H T ( HP k - H T + R ) -1 Determine the weight allocation between predicted and actual observed values. K k Where H=1, simplified to K k = P k - ( P k - + R ) -1 ;

[0097] Then, through the state update equation x k = x k - + K k ( z k-H x k - ), combined with raw monitoring data z k Correct the predicted values ​​to obtain the filtered effective data at the current time. x k Where H=1, it simplifies to:

[0098] x k = x k - + K k ( z k - x k - );

[0099] Finally, the covariance update equation is used. P k =( IK k H ) P k - Update the covariance at the current moment. P k - Where I=1 and H=1, simplified to P k =( IK k ) P k - Complete a single iteration;

[0100] S213: Anomaly Detection: Predicted values ​​obtained from iterative calculations x k - Compared with the original data z k Calculate the absolute value of the difference between the two. z k - x k - |, when the difference| z k - x k - |>3×R 1 / 2 That is, greater than 3 times the standard deviation of the observation noise R 1 / 2 Where R = 1 × 10 -3 ,3×R 1 / 2 ≈0.095, indicating the original data is correct.z k For outliers, the state update value is obtained directly from the state update equation. x k Replace; if the difference is ≤0.095, retain the original data. z k and use it as the next iteration. x k-1 Synchronously update covariance P k ;

[0101] S214: Output result: The above initialization, iterative calculation and anomaly detection process are completed sequentially for the raw data at all times. After the iterative processing is completed, the effective data set after removing outliers is obtained.

[0102] S22: Correction Fe 3+ The interference with carbon spectral lines occurs as follows:

[0103] S221: Obtain the raw data of soil carbon content (organic carbon, inorganic carbon) after removing outliers, denoted as... c C,raw Simultaneously, soil Fe was obtained from synchronous detection using the LIBS probe. 3+ Content, denoted as c Fe ;

[0104] S222: Calculation of Fe 3+ Interference intensity of carbon spectral lines I Fe The calculation formula is as follows:

[0105] I Fe = k × c Fe × I C0 ;

[0106] in, I Fe For Fe 3+ The intensity of interference with carbon spectral lines; k For Fe 3+ The interference coefficient, commonly used in LIBS analysis of soil carbon content, is 0.085, representing the Fe... 3+ The degree of overlap with carbon spectral lines; I C0 Fe-free 3+ The intensity of characteristic spectral lines of carbon under interference, in counts, taking the industry standard value of 1200 counts, corresponding to the spectral line intensity of pure carbon samples; c FeSoil Fe synchronous detection for LIBS probe 3+ content;

[0107] S223: Perform carbon content correction: Correct the original soil carbon content data to obtain the corrected Fe content. 3+ Soil carbon content after disturbance c C,corr The calculation formula is as follows:

[0108] c C,corr = c C,raw - I Fe / I C,std × c C,std ;

[0109] in, c C,raw Raw data of soil carbon content collected by the LIBS probe. I C,std The characteristic spectral line intensity of the standard carbon sample, in counts. The standard value of 1200 counts is used to ensure correction accuracy. c C,std The content of standard carbon sample, in g / kg, is taken as the standard value of 10 g / kg, and is used to calibrate the interference correction factor.

[0110] Example 3

[0111] Based on Example 1 or Example 2, see Figure 2 The specific process for calculating the net income and expenditure of the soil carbon pool in step S3, which involves processing the standardized carbon pool monitoring data, is as follows:

[0112] S31: Calculate total carbon input C in Total carbon input is a supplementary source of soil carbon in maize-growing areas, and it mainly consists of three parts: maize photosynthetic carbon sequestration input, cumulative soil organic carbon input, and cumulative soil inorganic carbon input. The specific calculation formula is as follows:

[0113] C in =C photo +C socin +C sicin ;

[0114] in, C photo Input for photosynthetic carbon fixation in maize, unit: gC / (m 2·d), calculated from the photosynthetic carbon fixation efficiency of maize monitored in step S2; C socin For cumulative soil organic carbon input, unit: gC / (m 2 •d): Calculated from the soil organic carbon content monitored in step S2; C sicin For cumulative input of inorganic carbon in soil, unit: gC / (m 2 •d): Calculated from the soil inorganic carbon content monitored in step S2;

[0115] S32: Calculate total carbon output C out Total carbon output is the pathway of soil carbon loss, which mainly includes two parts: soil carbon flux emissions and soil microbial mineralization loss. The formula and parameters are as follows:

[0116] C out =F soil +C min ;

[0117] in, F soil Soil carbon flux emissions, unit: gC / (m 2 ·d) Soil carbon flux directly uses the data monitored in step S2. Soil carbon flux itself represents the amount of carbon emitted from the soil into the atmosphere, and is monitored using NDIR technology. C min Soil microbial mineralization loss, unit: gC / (m²·d): calculated from the soil microbial community structure monitored in step S2 combined with organic carbon content, using the following formula:

[0118] C min =SOC × k × t ;

[0119] Where: SOC is the soil organic carbon content monitored in step S2, in g / kg; k The value is the soil microbial mineralization rate constant, with a common range of 0.005~0.015 / d for maize planting areas; t The monitoring period is in days (d), with 1 day being the unit of measurement. Since step S2 involves real-time data collection, the net income and expenditure are calculated once a day.

[0120] S33: Calculate the net income and expenditure of the soil carbon pool:

[0121] Δ C =( C in -Cout ) / ( C in +C out )×100%;

[0122] molecular C in -C out The absolute net income and expenditure is represented by a positive value indicating a carbon surplus, a negative value indicating a carbon deficit, and zero indicating a break-even point. The denominator is... C in + C out The total carbon turnover is used for normalization to avoid judgment bias caused by differences in planting area and soil type.

[0123] Maize photosynthetic carbon fixation input in step S31 C photo The calculation formula is as follows:

[0124] C photo = P × f ×0.5;

[0125] in: P The photosynthetic carbon fixation efficiency of maize monitored in step S2, in units of: μmol CO2 / (m 2 ·s); f The distribution coefficient of photosynthetic carbon to the soil is the proportion of photosynthetic carbon in maize distributed to the underground part. The general value for maize is 0.32, which means that 32% of the net photosynthetic carbon in maize is transported to the underground part, of which 4.6% is directly input into the soil. 0.5 is the mass conversion factor between CO2 and C. The C element accounts for about 42.9% of CO2. It is simplified to 0.5 for quick calculation in the field.

[0126] Soil organic carbon accumulation input C socin The calculation formula is as follows:

[0127] C socin = SOC × D × ρ × k 1;

[0128] Where: SOC is the soil organic carbon content monitored in step S2, in g / kg; D is the soil sampling depth, i.e., the LIBS probe insertion depth in step S1, 30cm = 0.3m; ρ The soil bulk density in corn-growing areas is 1.1~1.3 g / cm³. 3Take the median value of 1.2 g / cm³ 3 =1200kg / m 3 ; k 1 represents the organic carbon accumulation coefficient, with a common value of 0.001 in maize-growing areas, characterizing the daily accumulation rate of soil organic carbon;

[0129] Accumulated input of soil inorganic carbon C sicin The calculation formula is as follows:

[0130] C sicin = SIC × D × ρ × k 2;

[0131] in: SIC The soil inorganic carbon content monitored in step S2 is expressed in g / kg. D、ρ Similar to the above calculation of soil organic carbon, the sampling depth is 0.3m and the bulk density is 1200kg / m³. 3 ; k 2 represents the inorganic carbon accumulation coefficient, with a common value of 0.0005 in maize-growing areas, because the rate of inorganic carbon accumulation in soil is slower than that of organic carbon.

[0132] The process of determining the soil carbon pool balance in the maize planting area in step S3 is as follows:

[0133] When the net income and expenditure of the carbon pool is within ±5%, the soil carbon pool is considered to be in equilibrium.

[0134] When the net income and expenditure of the carbon pool is greater than 5%, the soil carbon pool is considered to be in a carbon surplus state.

[0135] When the net income and expenditure of the carbon pool is less than -5%, the soil carbon pool is considered to be in a carbon deficit state.

[0136] The specific process of step S4 is as follows:

[0137] S41: When the result indicates a carbon surplus, the control strategies include: reducing the corn planting density to 3800-4200 plants / acre, reducing biochar application to 50-80 kg / acre, adopting conventional tillage methods, increasing soil aeration to promote microbial mineralization, and simultaneously maintaining soil moisture content at 60%-65% of field capacity through IoT-controlled irrigation systems to reduce the carbon deposition efficiency of root exudates and reduce carbon input.

[0138] S42: When the judgment result is a carbon deficit state, the control strategies include: increasing the biochar application rate to 100-150 kg / mu, combined with the application of microbial carbon fixation agents, adopting a no-till combined with straw return mode, controlling the amount of straw returned to the field to 200-300 kg / mu, maintaining the soil moisture content at 65%-70% of field capacity through the IoT-controlled irrigation system, improving the carbon deposition efficiency of root exudates, and at the same time controlling the corn planting density to 4500-5000 plants / mu to enhance the photosynthetic carbon fixation capacity of corn and increase carbon input.

[0139] S43: When the determination result is a balanced state, the control strategy includes: maintaining the current corn planting density, tillage mode and water and fertilizer management parameters, and continuously tracking the changes in carbon pool parameters through the Internet of Things monitoring system to ensure the stability of the carbon pool balance state.

[0140] Example 4

[0141] In this embodiment, taking a corn-growing area (10 mu in area, alluvial soil, corn in the jointing stage, previously using conventional farming methods, planting density of 4500 plants / mu, biochar application rate of 120 kg / mu) as an example, the soil carbon pool balance regulation process of the present invention is illustrated:

[0142] Multi-dimensional raw data of this corn-growing area were collected synchronously through various distributed monitoring nodes. After multiple samplings and averaging, the core collected data are as follows: soil organic carbon content (SOC) = 18 g / kg, soil inorganic carbon content (SIC) = 45 g / kg, and soil carbon flux. F soil =2.8gC / (m 2 (d) Soil temperature 28℃, soil moisture 22%. Soil microbial community structure was obtained using 16S rRNA high-throughput sequencing technology to help determine the microbial mineralization rate constant. k The photosynthetic carbon fixation efficiency of maize is P = 18 μmol CO2 / (m 2 ·s). All collected multi-dimensional raw data are transmitted to edge computing nodes for preprocessing.

[0143] Remove outlier data: First, set the initial state values ​​for each piece of raw data. x 0. Take the average of the first 5 original soil organic carbon content data (17.8 g / kg, 18.2 g / kg, 17.9 g / kg, 18.1 g / kg, 18.0 g / kg) and calculate. x 0 = 18.0 g / kg, initial covariance P0 = 1 × 10 -3 Subsequently, the edge computing nodes process the raw data at each time step. z k (Based on soil organic carbon content) zk (Taking 18.0g / kg as an example) Perform iterative calculations:

[0144] The first step is to use the state prediction equation. x k - =x k-1 (A=1, B=0, u k-1 =0), the initial state value at the previous moment. x 0 = 18.0 g / kg as x k-1 Predict the current state value x k ⁻ =18.0g / kg;

[0145] The second step is to use the covariance prediction equation. P k - = P k-1 +Q (A=1, Q=5×10) -4 ), Substitute P k-1 =P0=1×10 -3 Calculated P k - =1×10 -3 +5×10 -4 =1.5×10 -3 ;

[0146] The third step is to use the Kalman gain equation. K k = P k - / ( P k - +R)(H=1,R=1×10 -3 ), Substitute P k - =1.5×10 -3 R=1×10 -3 Calculated K k =1.5×10 -3 / (1.5×10 -3 +1×10 -3 =0.6;

[0147] The fourth step is to update the state equation. x k = xk - + K k ( z k - x k - (H=1), substitute into x k - =18.0g / kg K k =0.6、 z k =18.0g / kg, calculated as follows x k =18.0+0.6×(18.0-18.0)=18.0g / kg;

[0148] The fifth step is to update the equation using covariance. P k =(1 -K k ) P k - (I=1, H=1), substitute into K k =0.6、 P k - =1.5×10 -3 Calculated P k =(1-0.6)×1.5×10 -3 =0.6×10 -3 This completes a single iteration.

[0149] Then, anomaly detection is performed: the original data is calculated. z k Compared with the predicted value x k - The absolute value of the difference |18.0-18.0|=0, given 3×R 1 / 2 =3×1×10 -3 ) 1 / 2 Since ≈0.095, and 0 ≤ 0.095, the original data is considered valid and is retained. z k =18.0g / kg, and use it as the value for the next iteration. x k-1 Synchronously update covariance P k =0.6×10 -3 .

[0150] Correction Fe3+ Interference with carbon spectral lines: First, obtain the original soil carbon content data after removing outliers. c C,raw (Organic carbon 18 g / kg, inorganic carbon 45 g / kg), and simultaneously obtained soil Fe from the LIBS probe. 3+ content c Fe =35g / kg;

[0151] Fe was then calculated. 3+ Interference intensity of carbon spectral lines I Fe Substitute parameters k =0.085、 c Fe =35g / kg I C0 =1200 counts, calculated as follows I Fe =0.085×35×1200=3570counts;

[0152] Finally, carbon content correction is performed, and the values ​​are substituted into... I C,std =1200 counts c C,std =10g / kg, calculate the corrected soil organic carbon content. c C,corr =18-3570 / 1200×10=18-29.75=-11.75g / kg (significantly exceeding the reasonable range of 0-50g / kg), therefore the interference coefficient needs to be fine-tuned. k Recalculate to 0.08. I Fe =0.08×35×1200=3360 counts, corrected again to get c C,corr =18-3360 / 1200×10=18-28=-10g / kg (still unreasonable, continue fine-tuning) k Calculate up to 0.07. I Fe =0.07×35×1200=2940 counts, corrected to c C,corr =18-2940 / 1200×10=18-24.5=-6.5g / kg (still unreasonable), determine the Fe in this region. 3+ The content is high, so it needs to be adjusted. k Calculate up to 0.05. I Fe =0.05×35×1200=2100counts, corrected to cC,corr =18-2100 / 1200×10=18-17.5=0.5g / kg (within a reasonable range); Similarly, correct the soil inorganic carbon content: c C,raw =45g / kg, I Fe =2100 counts;

[0153] Corrected c C,corr =45-2100 / 1200×10=45-17.5=27.5g / kg.

[0154] After correction, the preprocessed carbon pool monitoring data were obtained: soil organic carbon content (SOC) = 0.5 g / kg, soil inorganic carbon content (SIC) = 27.5 g / kg, and soil carbon flux. F soil =2.8gC / (m²·d), maize photosynthetic carbon fixation efficiency P=18μmolCO2 / (m 2 ·s).

[0155] Determine the soil carbon pool balance:

[0156] Calculate total carbon input C in The core components include maize photosynthetic carbon sequestration input, soil organic carbon accumulation input, and soil inorganic carbon accumulation input. Calculations are performed by substituting specific parameters.

[0157] Maize photosynthetic carbon fixation input:

[0158] C photo = P × f ×0.5=18μmolCO2 / (m 2 ·s)×0.32×0.5=18×0.32×0.5=2.88gC / (m 2 ·d), where f =0.32 is the coefficient for the distribution of photosynthetic carbon from maize to the soil, and 0.5 is the mass conversion coefficient between CO2 and C;

[0159] Accumulated input of soil organic carbon:

[0160] C socin = SOC × D × ρ × k 1 = 0.5 g / kg × 0.3 m × 1200 kg / m 3 ×0.001

[0161] =0.5×0.3×1200×0.001=0.18gC / (m 2 ·d);

[0162] in D =0.3m ρ =1200kg / m 3 , k 1 = 0.001;

[0163] Accumulated input of soil inorganic carbon:

[0164] C sicin = SIC × D × ρ × k 2 = 27.5 g / kg × 0.3 m × 1200 kg / m 3 ×0.0005

[0165] =27.5×0.3×1200×0.0005=4.95gC / (m²·d), where k 2 = 0.0005;

[0166] Therefore, total carbon input C in =2.88 + 0.18 + 4.95 = 8.01 gC / (m 2 ·d).

[0167] Calculate total carbon output C out The core issues include soil carbon flux emissions and soil microbial mineralization loss.

[0168] Soil carbon flux emissions F soil =2.8gC / (m 2 •d) The pre-processed monitoring data is used directly;

[0169] Soil microbial mineralization loss C min = SOC×k×t =0.5g / kg×0.010 / d×1d=0.005gC / (m 2 ·d), where k =0.010 / d The midpoint of the common range of 0.005~0.015 / d in maize planting areas was selected, and t=1d was the monitoring period;

[0170] Therefore, total carbon output C out =2.8 + 0.005 = 2.805 gC / (m 2·d).

[0171] Calculate the net income and expenditure of the soil carbon pool:

[0172] Δ C =( C in -C out ) / ( C in +C out )×100%

[0173] =(8.01-2.805) / (8.01+2.805)×100%

[0174] =5.205 / 10.815×100%≈47.2%.

[0175] Since ΔC≈47.2%>5%, the soil carbon pool in this corn-growing area is determined to be in a state of carbon surplus.

[0176] Given that the corn-growing area is in the jointing stage and the assessment indicates a carbon surplus, the following control strategies were implemented: reducing the corn planting density to 4000 plants / mu (from the original density of 4500 plants / mu), reducing biochar application to 70 kg / mu (from the original application of 120 kg / mu), adopting conventional tillage methods, increasing soil aeration to promote microbial mineralization, and simultaneously maintaining soil moisture content at 62% of field capacity (within the 60%-65% range) through IoT-controlled irrigation systems to reduce the carbon deposition efficiency of root exudates and decrease carbon input.

[0177] Seven days after regulation, parameters were collected and preprocessed simultaneously, yielding the following results: soil organic carbon content (SOC) = 0.45 g / kg, soil inorganic carbon content (SIC) = 25 g / kg, and soil carbon flux. F soil =3.2gC / (m 2 ·d) Maize photosynthetic carbon fixation efficiency P = 16 μmol CO2 / (m 2 ·s);

[0178] Recalculate net income and expenditure: C photo =16×0.32×0.5=2.56gC / (m 2 ·d);

[0179] C socin =0.45×0.3×1200×0.001=0.162gC / (m 2 ·d);

[0180] Csicin =25×0.3×1200×0.0005=4.5gC / (m 2 ·d);

[0181] C in =2.56 + 0.162 + 4.5 = 7.222 gC / (m 2 ·d);

[0182] Cout=3.2+(0.45×0.010×1)=3.2045gC / (m 2 ·d);

[0183] ΔC = (7.222 - 3.2045) / (7.222 + 3.2045) × 100% ≈ 38.6%, still > 5%, indicating a carbon surplus.

[0184] Adjusting control parameters: Based on the deviation value (the difference between 38.6% and 5%), further adjust the control strategy: reduce the biochar application rate to 50 kg / mu and maintain the soil moisture content at 60% of field capacity;

[0185] Seven days after the adjustment, the parameters were collected and calculated again, and ΔC = (6.1-3.8) / (6.1+3.8)×100%≈23.2%, which is still a carbon surplus. The biochar application rate was adjusted to 60 kg / mu, the soil moisture content was maintained at 61%, and the process was repeated.

[0186] Stable equilibrium: Ten days after the third adjustment, parameters were collected and calculated again, and ΔC=(5.2-4.9) / (5.2+4.9)×100%≈2.7% was obtained, which was within ±5%, indicating that the soil carbon pool had reached a state of equilibrium. Subsequently, the changes in carbon pool parameters were continuously tracked through the Internet of Things monitoring system, and data was collected every 3 days. Steps S2-S4 were repeated to ensure that the soil carbon pool equilibrium was maintained stably, thus completing the entire closed-loop regulation process.

Claims

1. A method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things, characterized in that, Includes the following steps: S1: Construct an Internet of Things (IoT) monitoring system for soil carbon pools in maize planting areas. The monitoring system includes distributed monitoring nodes, LoRaWAN communication gateways, edge computing nodes, and a cloud-based control platform. S2: Based on the IoT monitoring system, real-time collection and preprocessing of multi-dimensional parameters of soil carbon pool in maize planting area are carried out. The collected data includes soil organic carbon content, soil inorganic carbon content, soil carbon flux, soil temperature and humidity, soil microbial community structure and maize photosynthetic carbon fixation efficiency data. After preprocessing, carbon pool monitoring data are obtained. S3: Determine the soil carbon pool balance status in maize planting areas based on carbon pool monitoring data: The cloud-based control platform performs calculations on standardized carbon pool monitoring data to calculate the net income and expenditure of the soil carbon pool. The net income and expenditure of the soil carbon pool is calculated based on the comprehensive calculation of soil organic carbon content, soil inorganic carbon content, soil carbon flux, maize photosynthetic carbon fixation efficiency, and soil microbial mineralization. S4: Based on the soil carbon pool balance status in maize planting areas and combined with the division of maize growth stages, formulate targeted soil carbon pool balance regulation strategies. S5: Based on the soil carbon pool balance regulation strategy, the regulation operation is executed through the Internet of Things system and closed-loop feedback correction is performed until the soil carbon pool reaches a balanced state and is maintained stably. The specific process for calculating the net income and expenditure of the soil carbon pool in step S3, which involves processing the standardized carbon pool monitoring data, is as follows: S31: Calculate total carbon input C in This includes maize photosynthetic carbon sequestration input, soil organic carbon accumulation input, and soil inorganic carbon accumulation input. The specific calculation formulas are as follows: C in =C photo +C socin +C sicin ； in, C photo Input for photosynthetic carbon fixation in maize; C socin Input for soil organic carbon accumulation; C sicin Input for the accumulation of inorganic carbon in the soil; S32: Calculate total carbon output C out This includes soil carbon flux emissions and soil microbial mineralization loss, and the specific calculation formula is as follows: C out =F soil +C min ； in, F soil For soil carbon flux emissions; C min The calculation formula for soil microbial mineralization loss is as follows: C min =SOC × k × t ； Where: SOC is the soil organic carbon content; k For soil microbial mineralization rate; t For monitoring cycle; S33: Calculate the net income and expenditure of the soil carbon pool: D C =( C in -C out ) / ( C in +C out )×100%; Among them, molecules C in -C out The absolute net income and expenditure, denominator C in +C out Total carbon turnover; Maize photosynthetic carbon fixation input in step S31 C photo The calculation formula is as follows: C photo = P × f ×0.5； in: P To improve the photosynthetic carbon fixation efficiency of corn; f 0.5 is the distribution coefficient of photosynthetic carbon to soil; 0.5 is the mass conversion factor between CO2 and C. Soil organic carbon accumulation input C socin The calculation formula is as follows: C socin = SOC × D × ρ × k 1； Where: SOC is the soil organic carbon content; D is the soil sampling depth; ρ Soil bulk density; k 1 represents the organic carbon accumulation coefficient; Accumulated input of soil inorganic carbon C sicin The calculation formula is as follows: C sicin = SIC × D × ρ × k 2； in: SIC D represents the soil inorganic carbon content; D represents the soil sampling depth. ρ Soil bulk density; k 2 represents the inorganic carbon accumulation coefficient.

2. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 1, characterized in that, The process of determining the soil carbon pool balance in the maize planting area in step S3 is as follows: When the net income and expenditure of the carbon pool is within ±5%, the soil carbon pool is considered to be in equilibrium. When the net income and expenditure of the carbon pool is greater than 5%, the soil carbon pool is considered to be in a carbon surplus state. When the net income and expenditure of the carbon pool is less than -5%, the soil carbon pool is considered to be in a carbon deficit state.

3. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 1, characterized in that, The specific process of step S4 is as follows: S41: When the judgment result is a carbon surplus state, the regulation strategy is to reduce the corn planting density to 3800-4200 plants / mu, reduce the biochar application rate to 50-80 kg / mu, adopt conventional farming mode, and at the same time maintain the soil moisture content at 60%-65% of field capacity through the IoT-controlled irrigation system; S42: When the judgment result is a carbon deficit state, the control strategy is as follows: increase the biochar application rate to 100-150 kg / mu, combine with the application of microbial carbon fixation agents, adopt the no-till combined with straw return mode, control the straw return rate to 200-300 kg / mu, control the irrigation system through the Internet of Things to maintain the soil moisture content at 65%-70% of the field water holding capacity, and at the same time control the corn planting density to 4500-5000 plants / mu. S43: When the determination result is a balanced state, the control strategy is to maintain the current corn planting density, tillage mode and water and fertilizer management parameters, and continuously track the changes in carbon pool parameters through the Internet of Things monitoring system to ensure the stability of the carbon pool balance state.

4. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 1, characterized in that, The specific process of preprocessing the multi-dimensional raw data in step S2 is as follows: S21: Remove outlier data; S22: Correction Fe 3+ Interference with carbon spectral lines.

5. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 4, characterized in that, The specific process of step S21 is as follows: S211: Initialization: Set the initial state values ​​of various raw data, denoted as... x 0, initial covariance P0; S212: Iterative computation: edge computing nodes process the raw data at each time step. z k Execute in sequence: Through state prediction equations x k - =A x k-1 + Bu k-1 Combined with the effective data filtered from the previous time step x k-1 Predict the state value at the current moment. x k - Where A=1, B=0, u k-1 =0, simplified to x k - = x k-1 ; Prediction equation based on covariance P k - = AP k-1 A T + Q Calculate the uncertainty of the predicted value at the current time. P k - Where A=1, simplified to P k - = P k-1 +Q; Through the Kalman gain equation K k = P k - H T ( HP k - H T + R ) -1 Determine the weight allocation between predicted and actual observed values. K k Where H=1, simplified to K k = P k - ( P k - + R ) -1 ; Through the state update equation x k = x k - + K k ( z k -H x k - ), combined with raw monitoring data z k Correct the predicted values ​​to obtain the filtered effective data at the current time. x k Where H=1, it simplifies to: x k = x k - + K k ( z k - x k - )； Update equation using covariance P k =( IK k H ) P k - Update the covariance at the current moment. P k - Where I=1 and H=1, simplified to P k =( IK k ) P k - Complete a single iteration; S213: Anomaly Detection: Predicted values ​​obtained from iterative calculations x k - Compared with the original data z k Calculate the absolute value of the difference between the two. z k - x k - |, when the difference| z k - x k - |>3×R 1 / 2 Determine the original data z k For outliers, the state update value is obtained directly from the state update equation. x k Replace; if the difference is ≤0.095, retain the original data. z k and use it as the next iteration. x k-1 Synchronously update covariance P k ; S214: Output results: For the raw data at all times, complete the initialization, iterative calculation and anomaly detection process of steps S211~S213 in sequence to obtain the effective data set after removing outliers.

6. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 5, characterized in that, The specific process of step S22 is as follows: S221: Obtain the original soil carbon content data after removing outliers, denoted as... c C,raw Simultaneously, soil Fe was obtained from synchronous detection using the LIBS probe. 3+ Content, denoted as c Fe ; S222: Calculation of Fe 3+ Interference intensity of carbon spectral lines I Fe The calculation formula is as follows: I Fe = k × c Fe × I C0 ； in, I Fe For Fe 3+ The intensity of interference with carbon spectral lines; k For Fe 3+ The interference coefficient, commonly used in LIBS analysis of soil carbon content, is 0.085, representing the Fe... 3+ The degree of overlap with carbon spectral lines; I C0 Fe-free 3+ The intensity of characteristic spectral lines of carbon under interference, in counts, taking the industry standard value of 1200 counts, corresponding to the spectral line intensity of pure carbon samples; c Fe Soil Fe synchronous detection for LIBS probe 3+ content; S223: Perform carbon content correction: Correct the original soil carbon content data to obtain the corrected Fe content. 3+ Soil carbon content after disturbance c C,corr The calculation formula is as follows: c C,corr = c C,raw - I Fe / I C,std × c C,std ； in, c C,raw Raw data of soil carbon content collected by the LIBS probe. I C,std The characteristic spectral line intensities of the standard carbon sample. c C,std This refers to the content of the standard carbon sample.

7. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 1, characterized in that, The distributed monitoring nodes mentioned in step S1 are deployed in the corn planting area at a grid density of 5m×5m. Each monitoring node integrates a laser-induced breakdown spectroscopy (LIBS) probe, a soil carbon flux monitor, a soil temperature and humidity sensor, a soil microbial sensor, and a multispectral acquisition module.

8. The method for regulating soil carbon pool balance in maize planting areas based on the Internet of Things according to claim 1, characterized in that, The specific process of step S5 includes: the cloud control platform parses the soil carbon pool balance control strategy into specific execution instructions, and sends them to the IoT execution devices in the planting area through the LoRaWAN communication gateway. The IoT execution devices include smart seeders, smart fertilizer applicators, smart irrigation equipment and smart tillage equipment. Each execution device completes the planting density adjustment, fertilization, irrigation and tillage operations according to the instructions. Meanwhile, the IoT monitoring system continuously collects multi-dimensional parameters of the soil carbon pool after regulation, repeats the processing steps S2-S4, and obtains a new carbon pool equilibrium state determination result. If the new determination result still does not reach the equilibrium state, the parameters of the regulation strategy are adjusted according to the deviation value, and the execution command is issued again until the soil carbon pool reaches the equilibrium state and is maintained stably.

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